From 619f89c97bb4e0c899448b258c967c23e54d798e Mon Sep 17 00:00:00 2001
From: Pavol Mulinka <mulinka.pavol@gmail.com>
Date: Thu, 1 Aug 2024 11:32:00 +0200
Subject: [PATCH] Deployed cc6cbaf with MkDocs version: 1.3.1

---
 index.html                    |  46 ----------------------------------
 index.md                      |  38 ----------------------------
 inference_model/training.html |  18 ++++++-------
 search/search_index.json      |   2 +-
 sitemap.xml                   |  44 ++++++++++++++++----------------
 sitemap.xml.gz                | Bin 213 -> 212 bytes
 6 files changed, 32 insertions(+), 116 deletions(-)

diff --git a/index.html b/index.html
index 0607740..0922780 100755
--- a/index.html
+++ b/index.html
@@ -454,13 +454,6 @@
     Solution overview
   </a>
   
-</li>
-      
-        <li class="md-nav__item">
-  <a href="#todo" class="md-nav__link">
-    TODO
-  </a>
-  
 </li>
       
         <li class="md-nav__item">
@@ -962,45 +955,6 @@ <h2 id="solution-overview">Solution overview<a class="headerlink" href="#solutio
 <li><a href="https://sustainable-computing.io/">Kepler</a> - gather energy consumption data</li>
 <li><a href="https://redis.io/">Redis</a> - API for transfer of OBU measurements to Kubernetes</li>
 </ul>
-<h2 id="todo">TODO<a class="headerlink" href="#todo" title="Permanent link">&para;</a></h2>
-<pre class="mermaid"><code>graph TB
-    subgraph Cluster1
-        direction TB
-        Worker1[PySyft Worker Node 1]
-    end
-
-    subgraph Cluster2
-        direction TB
-        Worker2[PySyft Worker Node 2]
-    end
-
-    subgraph Cluster3
-        direction TB
-        Worker3[PySyft Worker Node 3]
-    end
-
-    subgraph CentralCluster
-        direction TB
-        CentralServer[PySyft Central Server]
-    end
-
-    Worker1 &lt;--&gt; CentralServer
-    Worker2 &lt;--&gt; CentralServer
-    Worker3 &lt;--&gt; CentralServer</code></pre>
-<ul>
-<li>federated learning/training in Kubernetes:</li>
-<li><strong><a href="https://github.com/OpenMined/PySyft/tree/dev">PySyft [PREFERRED]</a></strong><ul>
-<li>focuses on strong privacy-preserving techniques, offering advanced security features for sensitive data</li>
-<li>easy to <a href="https://github.com/OpenMined/PySyft/blob/dev/notebooks/tutorials/deployments/00-deployment-types.ipynb">deploy workers/clients</a></li>
-</ul>
-</li>
-<li><a href="https://github.com/adap/flower">Flower</a><ul>
-<li>emphasizes flexibility and ease of integration with existing machine learning frameworks, suitable for rapid prototyping and deployment</li>
-<li>more focused on <a href="https://flower.ai/docs/">iOS and Android deployment</a></li>
-</ul>
-</li>
-<li><a href="https://github.com/FederatedAI/KubeFATE">KubeFATE</a> - focused towards secure, scalable enterprise applications with advanced privacy-preserving techniques</li>
-</ul>
 <h2 id="additional-ideas">Additional ideas<a class="headerlink" href="#additional-ideas" title="Permanent link">&para;</a></h2>
 <ul>
 <li>KubeEdge deployment - same as Microk8s except with KubeEdge and Kubeflow/Kserver is swapped for Sedna</li>
diff --git a/index.md b/index.md
index 9d44937..5ad64cc 100755
--- a/index.md
+++ b/index.md
@@ -24,44 +24,6 @@ Description of the components:
 * [Kepler](https://sustainable-computing.io/) - gather energy consumption data
 * [Redis](https://redis.io/) - API for transfer of OBU measurements to Kubernetes
 
-## TODO
-
-```mermaid
-graph TB
-    subgraph Cluster1
-        direction TB
-        Worker1[PySyft Worker Node 1]
-    end
-
-    subgraph Cluster2
-        direction TB
-        Worker2[PySyft Worker Node 2]
-    end
-
-    subgraph Cluster3
-        direction TB
-        Worker3[PySyft Worker Node 3]
-    end
-
-    subgraph CentralCluster
-        direction TB
-        CentralServer[PySyft Central Server]
-    end
-
-    Worker1 <--> CentralServer
-    Worker2 <--> CentralServer
-    Worker3 <--> CentralServer
-```
-
-* federated learning/training in Kubernetes:
-  * **[PySyft [PREFERRED]](https://github.com/OpenMined/PySyft/tree/dev)**
-    * focuses on strong privacy-preserving techniques, offering advanced security features for sensitive data
-    * easy to [deploy workers/clients](https://github.com/OpenMined/PySyft/blob/dev/notebooks/tutorials/deployments/00-deployment-types.ipynb)
-  * [Flower](https://github.com/adap/flower)
-    * emphasizes flexibility and ease of integration with existing machine learning frameworks, suitable for rapid prototyping and deployment
-    * more focused on [iOS and Android deployment](https://flower.ai/docs/)
-  * [KubeFATE](https://github.com/FederatedAI/KubeFATE) - focused towards secure, scalable enterprise applications with advanced privacy-preserving techniques
-
 ## Additional ideas 
 
 * KubeEdge deployment - same as Microk8s except with KubeEdge and Kubeflow/Kserver is swapped for Sedna
diff --git a/inference_model/training.html b/inference_model/training.html
index 8c74137..cc0e88c 100755
--- a/inference_model/training.html
+++ b/inference_model/training.html
@@ -1649,7 +1649,8 @@ <h3 id="inference_model.training.trainer.Trainer" class="doc doc-heading">
 
           <details class="quote">
             <summary>Source code in <code>inference_model/training/trainer.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">14</span>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">13</span>
+<span class="normal">14</span>
 <span class="normal">15</span>
 <span class="normal">16</span>
 <span class="normal">17</span>
@@ -1680,8 +1681,7 @@ <h3 id="inference_model.training.trainer.Trainer" class="doc doc-heading">
 <span class="normal">42</span>
 <span class="normal">43</span>
 <span class="normal">44</span>
-<span class="normal">45</span>
-<span class="normal">46</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+<span class="normal">45</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
     <span class="bp">self</span><span class="p">,</span>
     <span class="n">cat_cols</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
     <span class="n">target_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
@@ -1774,7 +1774,8 @@ <h4 id="inference_model.training.trainer.Trainer.train" class="doc doc-heading">
 
       <details class="quote">
         <summary>Source code in <code>inference_model/training/trainer.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 48</span>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 47</span>
+<span class="normal"> 48</span>
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 <span class="normal"> 50</span>
 <span class="normal"> 51</span>
@@ -1841,8 +1842,7 @@ <h4 id="inference_model.training.trainer.Trainer.train" class="doc doc-heading">
 <span class="normal">112</span>
 <span class="normal">113</span>
 <span class="normal">114</span>
-<span class="normal">115</span>
-<span class="normal">116</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">train</span><span class="p">(</span>
+<span class="normal">115</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">train</span><span class="p">(</span>
     <span class="bp">self</span><span class="p">,</span>
     <span class="n">df_train</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
     <span class="n">params</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -1962,7 +1962,8 @@ <h4 id="inference_model.training.trainer.Trainer.fit" class="doc doc-heading">
 
       <details class="quote">
         <summary>Source code in <code>inference_model/training/trainer.py</code></summary>
-        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">118</span>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">117</span>
+<span class="normal">118</span>
 <span class="normal">119</span>
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@@ -2005,8 +2006,7 @@ <h4 id="inference_model.training.trainer.Trainer.fit" class="doc doc-heading">
 <span class="normal">158</span>
 <span class="normal">159</span>
 <span class="normal">160</span>
-<span class="normal">161</span>
-<span class="normal">162</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
+<span class="normal">161</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
     <span class="bp">self</span><span class="p">,</span>
     <span class="n">df_train</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
     <span class="n">df_valid</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
diff --git a/search/search_index.json b/search/search_index.json
index 27c9398..f079045 100755
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"index.html","text":"\u00b6 Basic Architecture Overview This repository is for the edge pod implementation, monitoring, and analysis in SUCCESS6G project. Solution overview \u00b6 Solution is deployed in Microk8s. Description of the components: Grafana - dashboards Ingress - expose services to the operator Prometheus - gather pod metrics InfluxDB - gather vehicular measurements and predictions MinIO - store models and training/testing data JupyterHub - develop new models MLflow - MLops, experiment and model tracking Kserve - serve inference models to predefined pods Istio - to ensure optimal traffic flow between microservices Knative - to ensure autoscaling of inference service pods Kepler - gather energy consumption data Redis - API for transfer of OBU measurements to Kubernetes TODO \u00b6 graph TB subgraph Cluster1 direction TB Worker1[PySyft Worker Node 1] end subgraph Cluster2 direction TB Worker2[PySyft Worker Node 2] end subgraph Cluster3 direction TB Worker3[PySyft Worker Node 3] end subgraph CentralCluster direction TB CentralServer[PySyft Central Server] end Worker1 <--> CentralServer Worker2 <--> CentralServer Worker3 <--> CentralServer federated learning/training in Kubernetes: PySyft [PREFERRED] focuses on strong privacy-preserving techniques, offering advanced security features for sensitive data easy to deploy workers/clients Flower emphasizes flexibility and ease of integration with existing machine learning frameworks, suitable for rapid prototyping and deployment more focused on iOS and Android deployment KubeFATE - focused towards secure, scalable enterprise applications with advanced privacy-preserving techniques Additional ideas \u00b6 KubeEdge deployment - same as Microk8s except with KubeEdge and Kubeflow/Kserver is swapped for Sedna implement multimodel pods e.g. by ModelMesh , or alpha feature of Kserve use Rancher to manage multi cluster Kubernetes","title":"home"},{"location":"index.html#_1","text":"Basic Architecture Overview This repository is for the edge pod implementation, monitoring, and analysis in SUCCESS6G project.","title":""},{"location":"index.html#solution-overview","text":"Solution is deployed in Microk8s. Description of the components: Grafana - dashboards Ingress - expose services to the operator Prometheus - gather pod metrics InfluxDB - gather vehicular measurements and predictions MinIO - store models and training/testing data JupyterHub - develop new models MLflow - MLops, experiment and model tracking Kserve - serve inference models to predefined pods Istio - to ensure optimal traffic flow between microservices Knative - to ensure autoscaling of inference service pods Kepler - gather energy consumption data Redis - API for transfer of OBU measurements to Kubernetes","title":"Solution overview"},{"location":"index.html#todo","text":"graph TB subgraph Cluster1 direction TB Worker1[PySyft Worker Node 1] end subgraph Cluster2 direction TB Worker2[PySyft Worker Node 2] end subgraph Cluster3 direction TB Worker3[PySyft Worker Node 3] end subgraph CentralCluster direction TB CentralServer[PySyft Central Server] end Worker1 <--> CentralServer Worker2 <--> CentralServer Worker3 <--> CentralServer federated learning/training in Kubernetes: PySyft [PREFERRED] focuses on strong privacy-preserving techniques, offering advanced security features for sensitive data easy to deploy workers/clients Flower emphasizes flexibility and ease of integration with existing machine learning frameworks, suitable for rapid prototyping and deployment more focused on iOS and Android deployment KubeFATE - focused towards secure, scalable enterprise applications with advanced privacy-preserving techniques","title":"TODO"},{"location":"index.html#additional-ideas","text":"KubeEdge deployment - same as Microk8s except with KubeEdge and Kubeflow/Kserver is swapped for Sedna implement multimodel pods e.g. by ModelMesh , or alpha feature of Kserve use Rancher to manage multi cluster Kubernetes","title":"Additional ideas"},{"location":"architecture_overview.html","text":"Architecture \u00b6 Basic Architecture \u00b6 Basic Architecture Single cluster architecture \u00b6 Single cluster architecture Advantages: Simplified Management: Easier to manage and maintain as there's only one cluster. Simplified network configuration and service discovery within the cluster. Resource Efficiency: Better utilization of resources since there is no need to allocate separate resources for multiple clusters. Cost-Effective: Lower operational costs due to reduced overhead in managing multiple clusters. Reduced complexity in monitoring and logging, leading to potential cost savings. Unified Security and Policy Management: Simplified implementation of security policies, access controls, and compliance measures. Consistent Environment: Ensures a consistent environment across all nodes, simplifying development and deployment processes. Disadvantages: Scalability Limitations: May face scalability issues as the number of edge devices or workloads increases. Single point of failure; if the cluster goes down, all connected devices and services are affected. Performance Bottlenecks: Potential for performance bottlenecks if the cluster becomes overloaded. Limited fault isolation; issues in one part of the cluster can impact the entire system. Geographical Constraints: Less optimal for geographically distributed edge nodes as latency might increase. May not efficiently handle diverse network conditions across different locations. Single cluster architecture (advanced) \u00b6 Single cluster architecture(advanced) Architecture improved by involving service mesh for optimal micro service traffic routing. For more info see istio documentation Multi cluster architecture \u00b6 Multi cluster architecture Multi cluster deployment that can utilize dUPF metrics (e.g. number of connected users) to decide if the application should be deployed in a neighboring micro cluster Advantages: Scalability and Fault Isolation: Better scalability by distributing workloads across multiple clusters. Improved fault isolation; problems in one cluster do not affect others. Geographical Distribution: Optimized for geographically distributed deployments, reducing latency by placing clusters closer to the edge devices. Can handle diverse network conditions more effectively. Performance Optimization: Easier to optimize performance by dedicating clusters to specific workloads or regions. Enhanced load balancing and resource distribution. Resilience and High Availability: Higher resilience and availability; failure in one cluster can be mitigated by other clusters. Enhanced disaster recovery capabilities. Disadvantages: Increased Complexity: More complex to manage and maintain multiple clusters. Higher administrative overhead in coordinating and synchronizing clusters. Resource Overhead: Requires more resources for cluster management (e.g., control planes for each cluster). Potentially higher infrastructure costs due to multiple clusters. Security and Policy Management: More complex to implement and manage security policies and compliance across multiple clusters. Potential challenges in maintaining consistent security postures across clusters. Inter-Cluster Communication: Requires robust inter-cluster communication mechanisms, which can add to the complexity. More complicated service discovery and networking between clusters. Conclusion \u00b6 The choice between single-cluster and multi-cluster Kubernetes deployments for IoT on the edge depends on factors such as scale, geographic distribution, resource availability, and management complexity. Single-cluster deployments are simpler and more cost-effective for smaller, localized environments, whereas multi-cluster deployments offer better scalability, fault isolation, and performance optimization for larger, distributed environments.","title":"architecture overview"},{"location":"architecture_overview.html#architecture","text":"","title":"Architecture"},{"location":"architecture_overview.html#basic-architecture","text":"Basic Architecture","title":"Basic Architecture"},{"location":"architecture_overview.html#single-cluster-architecture","text":"Single cluster architecture Advantages: Simplified Management: Easier to manage and maintain as there's only one cluster. Simplified network configuration and service discovery within the cluster. Resource Efficiency: Better utilization of resources since there is no need to allocate separate resources for multiple clusters. Cost-Effective: Lower operational costs due to reduced overhead in managing multiple clusters. 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Max RAM/CPU/GPU from prometheus_api_client import PrometheusConnect from math import ceil def get_pod_max_cpu_memory_in_prometheus( prometheus_url: str = \"http://prometheus-kube-prometheus-prometheus.monitoring:9090\", container_namespace: str = \"jupyterhub\", pod_name: str = \"jupyter-5uperpalo\", ) -> dict: \"\"\" Function to query max CPU and memory consumption of a specific pod in Kubernetes in the past 10 days. container_memory_working_set_bytes metric returns value per spawned pod id (different pods might have been spawned with the same name in the past 10 days) - only the last is checked For the specific query details see the link below: https://stackoverflow.com/questions/58747562/how-to-get-max-cpu-useage-of-a-pod-in-kubernetes-over-a-time-interval-say-30-da https://stackoverflow.com/a/66778814 Args: prometheus_url (str): Prometheus service URL container_namespace (str): Kubernetes pod namespace name pod_name (str): Kubernetes namespace name Returns: metrics (dict): dictionary with values \"\"\" def round_up(n, decimals=0): multiplier = 10**decimals return math.ceil(n * multiplier) / multiplier prom = PrometheusConnect(url=prometheus_url, disable_ssl=True) pod_name = f\"'{pod_name}'\" container_namespace = f\"'{container_namespace}'\" container_cpu_max_query = f\"max_over_time(sum(rate(container_cpu_usage_seconds_total{{namespace={container_namespace}, pod={pod_name}, container!=''}}[1m]))[10d:1m])\" container_memory_max_query = f\"max_over_time(container_memory_working_set_bytes{{namespace={container_namespace}, pod={pod_name}, container!=''}}[10d])\" cpu_max = prom.custom_query(query=container_cpu_max_query) memory_max = prom.custom_query(query=container_memory_max_query) metrics = { \"cpu\": round_up(float(cpu_max[-1][\"value\"][1]), 2), \"memory\": round_up(float(memory_max[-1][\"value\"][1])/1024**3, 2), } return metrics besteffort_pods_list = [ (\"kepler-7dfh2\", \"kepler\"), (\"nginx-ingress-microk8s-controller-h5kfj\", \"ingress\"), ] for pod, namespace in besteffort_pods_list: metrics = get_pod_max_cpu_memory_in_prometheus( container_namespace=namespace, pod_name=pod, ) print(f\"{pod} ({namespace}), {metrics}\") >>> kepler-7dfh2 (kepler), {'cpu': 0.19, 'memory': 0.07} >>> nginx-ingress-microk8s-controller-h5kfj (ingress), {'cpu': 0.12, 'memory': 0.45} Max GPU \u00b6 In case it is necessary a the Prometheus metrics can be extended by including NVIDIA DCGM exporter in exported metrics and then querying the metrics of interest using similar function to get_pod_max_cpu_memory_in_prometheus . Energy measurements \u00b6 Using Kubernetes Efficient Power Level Exporter (Kepler) . Core/Uncore measurements might not be available(only PKG) depending on Intel architecture. See RAPL section in Kepler metrics explanation. Kepler metrics from datetime import datetime from prometheus_api_client import PrometheusConnect def get_kepler_pod_stats( to_timestamp: float, from_timestamp: float, prometheus_url: str = \"http://prometheus-kube-prometheus-prometheus.monitoring:9090\", container_namespace: str = \"jupyterhub\", pod_name: str = \"jupyter-5uperpalo\", ) -> dict: \"\"\"Function to query Kepler power consumption data of specific pod in Kubernetes. # https://sustainable-computing.io/design/kepler-energy-sources/ # https://github.com/sustainable-computing-io/kepler/blob/1c397ff00b72b5cb1585d0de2cd495c73d88f07a/grafana-dashboards/Kepler-Exporter.json#L299 # https://prometheus.io/docs/prometheus/latest/querying/basics/#time-durations # [metric for metric in prom.all_metrics() if \"kepler\" in metric] Args: to_timestamp (list): 'to' timestamp from_timestamp (list): 'from' timestamp prometheus_url (str): Prometheus service url container_namespace (str): Kubernetes pod namespace name pod_name (str): Kubernetes namespace name Returns: metrics (dict): Kepler metrics of the power consumption of pod in Kubernetes \"\"\" prom = PrometheusConnect(url=prometheus_url, disable_ssl=True) pod_name = f\"'{pod_name}'\" container_namespace = f\"'{container_namespace}'\" time_range_sec = str(int(to_timestamp - from_timestamp)) container_sum_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_core_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_core_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_uncore_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_uncore_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_pkg_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_package_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_dram_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_dram_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_other_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_other_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_gpu_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_gpu_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" sum_data = prom.custom_query(query=container_sum_query) core_data = prom.custom_query(query=container_core_query) uncore_data = prom.custom_query(query=container_uncore_query) pkg_data = prom.custom_query(query=container_pkg_query) dram_data = prom.custom_query(query=container_dram_query) other_data = prom.custom_query(query=container_other_query) gpu_data = prom.custom_query(query=container_gpu_query) metrics = { \"from\": datetime.fromtimestamp(from_timestamp).strftime(\"%m/%d/%Y, %H:%M:%S\"), \"to\": datetime.fromtimestamp(to_timestamp).strftime(\"%m/%d/%Y, %H:%M:%S\"), \"sum\": float(sum_data[0][\"value\"][1]), \"core\": float(core_data[0][\"value\"][1]), \"uncore\": float(uncore_data[0][\"value\"][1]), \"pkg\": float(pkg_data[0][\"value\"][1]), \"dram\": float(dram_data[0][\"value\"][1]), \"other\": float(other_data[0][\"value\"][1]), \"gpu\": float(gpu_data[0][\"value\"][1]), } return metrics >>> # prometheus has UTC 00:00 as opposed to Barcelona UTC +02:00 >>> to_timestamp = datetime(2024, 6, 11, 8, 31).timestamp() >>> from_timestamp = datetime(2024, 6, 11, 8, 25).timestamp() >>> >>> get_kepler_pod_stats(to_timestamp=to_timestamp, from_timestamp=from_timestamp) >>> >>> {'from': '06/11/2024, 08:25:00', >>> 'to': '06/11/2024, 08:31:00', >>> 'sum': 45.333333333333336, >>> 'core': 0.0, >>> 'uncore': 0.0, >>> 'pkg': 44.48569999999987, >>> 'dram': 0.8289999999998447, >>> 'other': 0.0, >>> 'gpu': 0.0} Carbon emission monitoring \u00b6 Grafana dashboard provided by Kepler uses predefined natural gas/coal/petroleum conversions from US . See simple implemented formula here with coefficient defined here . The Energy measurements can be easily transformed to Carbon emissions using CodeCarbon methodology . Code \u00b6 RAM/CPU/GPU \u00b6 All-in-one solution by Scalene: https://github.com/plasma-umass/scalene Carbon emission monitoring: \u00b6 https://mlco2.github.io/codecarbon/usage.html# https://github.com/sb-ai-lab/Eco2AI https://github.com/lfwa/carbontracker Code Execution timing \u00b6 Timer decorator Import and prepend the time decorator to log time(into specified log file) that it takes to execute analyzed function/class, e.g.: import logging from functools import wraps from time import time def timer(func): \"\"\"Wrapper to time and log the function execution. Parameters: func: function \"\"\" @wraps(func) def timer_func(*args, **kwargs): start_time = time() value = func(*args, **kwargs) end_time = time() logging.info( f\"Finished {func.__name__} in {(end_time - start_time):.4f} seconds.\" # noqa ) return value return timer_func @timer def training(config: TrainingConfig, custom_params: CustomParameters): [Legacy] RAM \u00b6 Max RAM To get max RAM used during executition of the function import the decorator and put it above it function to log max RAM used into specified logging file, e.g.: NOTE : this applies only to functions and NOT classes or class methods. import logging from functools import wraps from memory_profiler import memory_usage def ram_usage(func): \"\"\"Wrapper to monitor and log RAM usage during the function execution. NOTE: can be applied to function but not to method of a class. Parameters: func: function \"\"\" @wraps(func) def ram_usage_func(*args, **kwargs): ram, value = memory_usage( (func, args, *kwargs), interval=1.0, retval=True, max_usage=True ) logging.info( f\"Finished {func.__name__,}. Max RAM used {(ram / 1000):.4f} GB.\" ) # noqa return value return ram_usage_func from churn_pred.code_profiling import ram_usage @ram_usage def training(config: TrainingConfig, custom_params: CustomParameters): Per line RAM usage analysis To analyze RAM usage per line of the code import profile decorator, put it above the function you want to analyze: from memory_profiler import profile @profile def training(config: TrainingConfig, custom_params: CustomParameters): , run the code using memory_profile command line util, and analyze the RAM usage, e.g.: $ python -m memory_profiler local_run.py Filename: /XXX/main.py Line # Mem usage Increment Occurrences Line Contents ============================================================= 53 230.8 MiB 230.8 MiB 1 @ram_usage 54 @profile 55 def training(config: TrainingConfig, custom_params: CustomParameters): 56 230.9 MiB 0.1 MiB 1 data_config = DataConfig.load(config.data_config) 57 230.9 MiB 0.0 MiB 1 hyperparameters = Hyperparameters.parse_obj(config.hyperparameters) 58 59 687.5 MiB 456.6 MiB 1 queries, df_data = get_data(config, custom_params) 60 687.5 MiB 0.0 MiB 1 (","title":"code profiling"},{"location":"code_profiling.html#code-profiling-5-min-read","text":"","title":"Code profiling [5 min read]"},{"location":"code_profiling.html#jupyterhubany-kubernetes-pod","text":"","title":"JupyterHub(any Kubernetes) pod"},{"location":"code_profiling.html#code","text":"","title":"Code"},{"location":"contributing.html","text":"Code \u00b6 Before making a pushing a code and making a pull request please run codestyle checks and tests ./code_style.sh pytest --doctest-modules churn_pred --cov-report xml --cov-report term --disable-pytest-warnings --cov = eda tests/ Documentation \u00b6 Documentation is created using github pages and mkdocs, see # to build locally cd docs pip install -r requirements.txt mkdocs build # to push to github pages mkdocs gh-deploy # if you want to run webserver locally mkdocs serve","title":"contributing"},{"location":"contributing.html#code","text":"Before making a pushing a code and making a pull request please run codestyle checks and tests ./code_style.sh pytest --doctest-modules churn_pred --cov-report xml --cov-report term --disable-pytest-warnings --cov = eda tests/","title":"Code"},{"location":"contributing.html#documentation","text":"Documentation is created using github pages and mkdocs, see # to build locally cd docs pip install -r requirements.txt mkdocs build # to push to github pages mkdocs gh-deploy # if you want to run webserver locally mkdocs serve","title":"Documentation"},{"location":"data_management.html","text":"Data management \u00b6 The choice of data storage and management options was influenced by the following factors: * time series data (measurements and predictions) -> time series database * easy to administer, popular solution with helm chart available by developers and Python API support -> InfluxDB * responsibility ownership of communication between vehicle and endpoint (v2x communication) on v2x measurement provider, i.e. Idneo -> Idneo choice - Redis * monitoring of energy consumption(carbonn footprint) + CPU/GPU/RAM utilization in Kubernetes -> Prometheus + Kepler + Grafana Local/edge data management \u00b6 Both the vehicle On Board Unit (OBU) and endpoint API are administered by Idneo . V2X vehicle measurements refer to the data that the vehicle uses to communicate its presence on the road. This data is essential in Intelligent Transportation Systems (ITS) to complement and provide the Cooperative Awareness (CA) service, which generates Cooperative Awareness Messages (CAM) according to the European standard ETSI, equivalent to the Basic Safety Message (BSM) of the American standard (SAE). The data shared through V2X in ITS services represents only a small portion of the data available in the vehicle's internal network Notice that generating simple V2X messages with their mandatory data, only requires the public vehicle data. However, a V2X stack has an interface where car manufacturers can insert some private data to enrich the V2X messages (these are optional fields) The data is sampled through the OBDII diagnostic port by constant polling. This data is uploaded to a transactional database Each variable has its operating range and units. For more details, see On-board diagnostics parameter definitions . For a detailed communication description, i.e. how the communication is enforced to stay in an edge node, please see networking section single cluster Kubernetes the Redis database is deployed on every edge node by use of Kubernetes pod assigning options and defining DaemonSets that will run a pod on every selected node multi-cluster Kubernetes multi-cluster environment suggests the use of a small cluster per edge location, in this case, a separate Redis database deployment in each cluster is sufficient Predictions \u00b6 If there is no responsibility ownership division an Inference Service provided by Kserve would be an endpoint for OBU. Compatibility with this scenario is assured by model predict methods . As the Redis database is an endpoint for OBU we use the Redis client channel subscription method to get the latest vehicle measurements and forward the predictions to the central Influx database. Additional notes to \"Local/edge data management\" and \"Predictions\" \u00b6 channel subscription scenario is not \"usual\" as inference service is used indirectly keeping the v2x data on the edge reduces communication overhead and if necessary for analysis it can be (i) queried from the Redis database or (ii) aggregated to InfluxDB by use of Telegram keeping the v2x data on the edge also reduces the possible security/privacy concerns caused by transferring all the data to the central location we do not use Prometheus for predictions storing as Prometheus is designed for scraping metrics and not storing data; storing functionality can improved by Thanos , but the solution is primarily designed for metrics not measurements and predictions we do not use Grafana for visualization of predictions as InfluxDB by default includes User Interface(UI) for visualization and forwarding data to Grafana would cause additional unnecessary data communication System Metrics \u00b6 We use combination of Prometheus + Kepler + Grafana to monitor resource utilization inside the cluster. This data can be later used for downscaling the solution and reducing the carbon footprint in production.","title":"data management"},{"location":"data_management.html#data-management","text":"The choice of data storage and management options was influenced by the following factors: * time series data (measurements and predictions) -> time series database * easy to administer, popular solution with helm chart available by developers and Python API support -> InfluxDB * responsibility ownership of communication between vehicle and endpoint (v2x communication) on v2x measurement provider, i.e. Idneo -> Idneo choice - Redis * monitoring of energy consumption(carbonn footprint) + CPU/GPU/RAM utilization in Kubernetes -> Prometheus + Kepler + Grafana","title":"Data management"},{"location":"data_management.html#localedge-data-management","text":"Both the vehicle On Board Unit (OBU) and endpoint API are administered by Idneo . V2X vehicle measurements refer to the data that the vehicle uses to communicate its presence on the road. This data is essential in Intelligent Transportation Systems (ITS) to complement and provide the Cooperative Awareness (CA) service, which generates Cooperative Awareness Messages (CAM) according to the European standard ETSI, equivalent to the Basic Safety Message (BSM) of the American standard (SAE). The data shared through V2X in ITS services represents only a small portion of the data available in the vehicle's internal network Notice that generating simple V2X messages with their mandatory data, only requires the public vehicle data. However, a V2X stack has an interface where car manufacturers can insert some private data to enrich the V2X messages (these are optional fields) The data is sampled through the OBDII diagnostic port by constant polling. This data is uploaded to a transactional database Each variable has its operating range and units. For more details, see On-board diagnostics parameter definitions . For a detailed communication description, i.e. how the communication is enforced to stay in an edge node, please see networking section single cluster Kubernetes the Redis database is deployed on every edge node by use of Kubernetes pod assigning options and defining DaemonSets that will run a pod on every selected node multi-cluster Kubernetes multi-cluster environment suggests the use of a small cluster per edge location, in this case, a separate Redis database deployment in each cluster is sufficient","title":"Local/edge data management"},{"location":"data_management.html#predictions","text":"If there is no responsibility ownership division an Inference Service provided by Kserve would be an endpoint for OBU. Compatibility with this scenario is assured by model predict methods . As the Redis database is an endpoint for OBU we use the Redis client channel subscription method to get the latest vehicle measurements and forward the predictions to the central Influx database.","title":"Predictions"},{"location":"data_management.html#additional-notes-to-localedge-data-management-and-predictions","text":"channel subscription scenario is not \"usual\" as inference service is used indirectly keeping the v2x data on the edge reduces communication overhead and if necessary for analysis it can be (i) queried from the Redis database or (ii) aggregated to InfluxDB by use of Telegram keeping the v2x data on the edge also reduces the possible security/privacy concerns caused by transferring all the data to the central location we do not use Prometheus for predictions storing as Prometheus is designed for scraping metrics and not storing data; storing functionality can improved by Thanos , but the solution is primarily designed for metrics not measurements and predictions we do not use Grafana for visualization of predictions as InfluxDB by default includes User Interface(UI) for visualization and forwarding data to Grafana would cause additional unnecessary data communication","title":"Additional notes to \"Local/edge data management\" and \"Predictions\""},{"location":"data_management.html#system-metrics","text":"We use combination of Prometheus + Kepler + Grafana to monitor resource utilization inside the cluster. This data can be later used for downscaling the solution and reducing the carbon footprint in production.","title":"System Metrics"},{"location":"development.html","text":"\u00b6 SUstainable and high PErformance COMputing platform Research & Development testbed. R&D Architecture steps: setup full testbed use RaspberryPis to emulate edge nodes send gathered data using Python script and target specific edge nodes (emulation of vehicle moving between base stations) test centralized vs edge computation vs federated learning query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, Testbed environment make pictures of used HW (RaspberyPis, workstation, SUPERCOM servers rack?)","title":"development"},{"location":"development.html#_1","text":"SUstainable and high PErformance COMputing platform Research & Development testbed. R&D Architecture steps: setup full testbed use RaspberryPis to emulate edge nodes send gathered data using Python script and target specific edge nodes (emulation of vehicle moving between base stations) test centralized vs edge computation vs federated learning query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, Testbed environment make pictures of used HW (RaspberyPis, workstation, SUPERCOM servers rack?)","title":""},{"location":"ml_model_development.html","text":"Machine learning model development \u00b6 Centralized training \u00b6 The project is focused on developing a production-ready solution. At the time of writing the documentation, we do not have vehicular measurements that would include labels with vehicle faults. We focus on exploring the best possible overall/engineering solutions as opposed to finding the best-performing model. Centralized training is therefore first choice for implementation. advantages: simplified data management - having all data in one place can simplify data management, preprocessing, and cleaning disadvantages: privacy concerns - ggregating data from multiple sources can lead to significant privacy issues, especially with sensitive information Online/stream vs batch \u00b6 V2X measurements form a stream of data from a vehicle to AI/ML on the edge. Unfortunately, this causes issues with the possibility of a high number(100-10,000?) streams per edge location. Until such a system is implemented we do not know the probable number of streams per edge location and we are unable to define requirements of the system that would be able to process X streams with Y data points per second. Such a system has to be able to process streams either by multi-threading or parallel processing. Also, methods like ADWIN as the window size is reset to the initial value each time vehicle changes edge device as the state of the ML model can't be migrated between locations per vehicle as of the number of vehicles and their traveling speed VS migration time of the model to a new location. For now, we pick the \"easier\" option of batch processing where (i) every sample is treated individually, or (ii) samples from the same vehicle are processed in fixed batch sizes Deep vs \"Traditional\" \u00b6 As we are dealing with strictly tabular data we focus on the tree-based model solution implementation. For detailed answer, we refer a reader to e.g. paper \"Why do tree-based models still outperform deep learning on tabular data?\" [TODO] Federated learning/training \u00b6 We implement a federated learning model to address concerns of privacy and data management in case of multi-cluster solution. advantages: enhanced privacy - raw data never leaves the local devices, reducing privacy risks reduced data transfer - only model updates (rather than raw data) are transmitted, which can be more bandwidth-efficient compliance - easier to comply with data sovereignty and privacy regulations since data remains local fault tolerance - the system can be more resilient to individual server failures since the data and computation are distributed disadvantages: complexity - implementing federated learning is more complex due to the need to manage distributed training, aggregation, and synchronization heterogeneity - variability in data quality and distribution across devices can impact model performance limited computational resources - Local devices may have limited computational power compared to centralized data centers communication overhead - while raw data isn't transferred, frequent model updates can still generate significant communication overhead, especially in large-scale systems graph TB subgraph Cluster1 direction TB Worker1[PySyft Worker Node 1] end subgraph Cluster2 direction TB Worker2[PySyft Worker Node 2] end subgraph Cluster3 direction TB Worker3[PySyft Worker Node 3] end subgraph CentralCluster direction TB CentralServer[PySyft Central Server] end Worker1 <--> CentralServer Worker2 <--> CentralServer Worker3 <--> CentralServer PySyft [PREFERRED] focuses on strong privacy-preserving techniques, offering advanced security features for sensitive data easy to deploy workers/clients Flower emphasizes flexibility and ease of integration with existing machine learning frameworks, suitable for rapid prototyping and deployment more focused on iOS and Android deployment KubeFATE - focused towards secure, scalable enterprise applications with advanced privacy-preserving techniques","title":"ML model dev"},{"location":"ml_model_development.html#machine-learning-model-development","text":"","title":"Machine learning model development"},{"location":"ml_model_development.html#centralized-training","text":"The project is focused on developing a production-ready solution. At the time of writing the documentation, we do not have vehicular measurements that would include labels with vehicle faults. We focus on exploring the best possible overall/engineering solutions as opposed to finding the best-performing model. Centralized training is therefore first choice for implementation. advantages: simplified data management - having all data in one place can simplify data management, preprocessing, and cleaning disadvantages: privacy concerns - ggregating data from multiple sources can lead to significant privacy issues, especially with sensitive information","title":"Centralized training"},{"location":"ml_model_development.html#todo-federated-learningtraining","text":"We implement a federated learning model to address concerns of privacy and data management in case of multi-cluster solution. advantages: enhanced privacy - raw data never leaves the local devices, reducing privacy risks reduced data transfer - only model updates (rather than raw data) are transmitted, which can be more bandwidth-efficient compliance - easier to comply with data sovereignty and privacy regulations since data remains local fault tolerance - the system can be more resilient to individual server failures since the data and computation are distributed disadvantages: complexity - implementing federated learning is more complex due to the need to manage distributed training, aggregation, and synchronization heterogeneity - variability in data quality and distribution across devices can impact model performance limited computational resources - Local devices may have limited computational power compared to centralized data centers communication overhead - while raw data isn't transferred, frequent model updates can still generate significant communication overhead, especially in large-scale systems graph TB subgraph Cluster1 direction TB Worker1[PySyft Worker Node 1] end subgraph Cluster2 direction TB Worker2[PySyft Worker Node 2] end subgraph Cluster3 direction TB Worker3[PySyft Worker Node 3] end subgraph CentralCluster direction TB CentralServer[PySyft Central Server] end Worker1 <--> CentralServer Worker2 <--> CentralServer Worker3 <--> CentralServer PySyft [PREFERRED] focuses on strong privacy-preserving techniques, offering advanced security features for sensitive data easy to deploy workers/clients Flower emphasizes flexibility and ease of integration with existing machine learning frameworks, suitable for rapid prototyping and deployment more focused on iOS and Android deployment KubeFATE - focused towards secure, scalable enterprise applications with advanced privacy-preserving techniques","title":"[TODO] Federated learning/training"},{"location":"networking.html","text":"Networking \u00b6 Description of traffic flow for simple(no Istio) Single cluster traffic flow. Vehicle data processing \u00b6 Vehicle data processing Traffic flow: Vehicle sends measurements to Vehicle API service (provided by Idneo \u2013 data preprocessing +Redis DB) transmission is received by antenna Transmission enters data(IPv4/IPv6) network Data is forwarded to the closest Kubernetes Edge node using (i) Anycast IP address, shared by all Edge Nodes, and (ii) nodePort defined in NodePort service Edge Node receives the data, checks the IP table in kube-proxy, and forwards it to local Pod A as the service is configured with externalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the packet is dropped Vehicle API service forwards the data to the ML prediction service(Kserve inference) Data is forwarded to Pod B as Kserve inference service is configured with internalTrafficPolicy: Local If there is no local Pod for Kserve inference service, the packet is dropped or ML prediction service pod periodically queries local Redis DB in Vehicle API service \u2013 as Vehicle API service is also configured with internalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the queries/requests are dropped Kserve inference service pushes the data into InfluxDB Grafana periodically queries data from InfluxDB Energy consumption monitoring \u00b6 Energy consumption monitoring Traffic flow: Kepler exporter uses eBPF to probe CPU, GPU, RAM performance counters and exposes them over time via HTTP Prometheus periodically queries Kepler exporter for a new data Grafana periodically queries Prometheus for anew data Vehicle data and energy consumption monitoring \u00b6 Vehicle data and energy consumption monitoring Traffic flow: User enters Grafana FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it Grafana GUI comes up in the Users web browser ML model development/update \u00b6 ML model development/update Traffic flow: User enters MLflow FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it to Mlflow GUI where user can check current models User updates the model locally or in cloud and uploads it to MLflow API Mlflow API forwards the model to the backend database - Minio ML model deployment \u00b6 ML model deployment Traffic flow: Admin user instructs Kserve to deploy a model Kserve queries the model from Minio storage Kserve updates/\u201dserves\u201d the model in all Kserver inference Pods","title":"networking"},{"location":"networking.html#networking","text":"Description of traffic flow for simple(no Istio) Single cluster traffic flow.","title":"Networking"},{"location":"networking.html#vehicle-data-processing","text":"Vehicle data processing Traffic flow: Vehicle sends measurements to Vehicle API service (provided by Idneo \u2013 data preprocessing +Redis DB) transmission is received by antenna Transmission enters data(IPv4/IPv6) network Data is forwarded to the closest Kubernetes Edge node using (i) Anycast IP address, shared by all Edge Nodes, and (ii) nodePort defined in NodePort service Edge Node receives the data, checks the IP table in kube-proxy, and forwards it to local Pod A as the service is configured with externalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the packet is dropped Vehicle API service forwards the data to the ML prediction service(Kserve inference) Data is forwarded to Pod B as Kserve inference service is configured with internalTrafficPolicy: Local If there is no local Pod for Kserve inference service, the packet is dropped or ML prediction service pod periodically queries local Redis DB in Vehicle API service \u2013 as Vehicle API service is also configured with internalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the queries/requests are dropped Kserve inference service pushes the data into InfluxDB Grafana periodically queries data from InfluxDB","title":"Vehicle data processing"},{"location":"networking.html#energy-consumption-monitoring","text":"Energy consumption monitoring Traffic flow: Kepler exporter uses eBPF to probe CPU, GPU, RAM performance counters and exposes them over time via HTTP Prometheus periodically queries Kepler exporter for a new data Grafana periodically queries Prometheus for anew data","title":"Energy consumption monitoring"},{"location":"networking.html#vehicle-data-and-energy-consumption-monitoring","text":"Vehicle data and energy consumption monitoring Traffic flow: User enters Grafana FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it Grafana GUI comes up in the Users web browser","title":"Vehicle data and energy consumption monitoring"},{"location":"networking.html#ml-model-developmentupdate","text":"ML model development/update Traffic flow: User enters MLflow FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it to Mlflow GUI where user can check current models User updates the model locally or in cloud and uploads it to MLflow API Mlflow API forwards the model to the backend database - Minio","title":"ML model development/update"},{"location":"networking.html#ml-model-deployment","text":"ML model deployment Traffic flow: Admin user instructs Kserve to deploy a model Kserve queries the model from Minio storage Kserve updates/\u201dserves\u201d the model in all Kserver inference Pods","title":"ML model deployment"},{"location":"testing.html","text":"Real-world testing and data gathering - Parcmotor Castellol\u00ed administered by Cellnex \u00b6 Castellol\u00ed testing architecture Castellol\u00ed testing architecture legend: white - mandatory grey pattern - optional steps: prepare the Kserve model and helm charts make testing requests before the day of testing run the real-world test with vehicles in Castelloli query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, NBC environment make vehicle pictures","title":"testing"},{"location":"testing.html#real-world-testing-and-data-gathering-parcmotor-castelloli-administered-by-cellnex","text":"Castellol\u00ed testing architecture Castellol\u00ed testing architecture legend: white - mandatory grey pattern - optional steps: prepare the Kserve model and helm charts make testing requests before the day of testing run the real-world test with vehicles in Castelloli query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, NBC environment make vehicle pictures","title":"Real-world testing and data gathering - Parcmotor Castellol\u00ed administered by Cellnex"},{"location":"use_cases.html","text":"Vehicular condition monitoring with robust V2X connectivity - EXTEND \u00b6 Use Case Extend Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. However, wireless transmission of in-vehicle monitoring data comes inadvertently with challenges which involve distortions and missing data owing to the inherently shared wireless medium. Incomplete vehicular data attributed to either increased interference levels, channel attenuation phenomena or sensor malfunctioning may lead to incorrect decision-making and failure to predict maintenance actions. Addressing the degradation of transmitted V2X data is thus essential for high-fidelity estimation of the system network state and informed actions. In this context, machine learning (ML)-empowered signal reconstruction and imputation techniques are expected to obtain accurate estimates of incomplete sensor trajectories by leveraging the intrinsic spatiotemporal synergies of captured information. By deeply investigating the interplay between vehicle dynamics and fault provisioning, faulty vehicular equipment will be timely detected/predicted, improving road safety by preventing critical failures. Vehicular condition monitoring with robust V2X connectivity - DEVISE \u00b6 Use Case Devise Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. Wireless transmission of in-vehicle monitoring data is susceptible to various security threats which may destabilize system operation, degrade network performance, and potentially lead to incorrect decision-making regarding the vehicle condition status. At the same time, the pervasive integration of data-driven techniques expands the attack surface, giving rise to finely targeted, stealthier, and scalable attacks targeting both phases (training, inference) of ML techniques. The mitigation of such threats becomes imperative for the correct estimation of vehicle condition. Condition monitoring data used for subsequent predictive diagnostics should be properly secured such that they do not contain falsified information, while abnormal traffic should be detected and isolated in its entirety. In this context, appropriate security countermeasures are expected to accurately detect malicious content in the transmitted information and ensure the semantic correctness of the aggregated data for trustworthy decision-making. Vehicular condition monitoring with robust V2X connectivity - VERIFY \u00b6 Use Case Extend 01 Use Case Extend 02 Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. The limited computational capabilities of edge nodes pose significant challenges for the realization of computationally efficient V2X communication aimed for condition monitoring. Aiming to achieve dramatically reduced data processing times, over-the-air computing methods and decentralized deployments of AI models aim at rapid knowledge extraction from the vehicular measurement streams and reduction of computational overhead. In this context, over-the-air computing methods will be explored to address the computational limitations and stringent latency requirements in V2X systems by means of over-the-air aggregation. In addition, decentralized methods for predictive diagnosis of vehicles condition will be investigated to leverage the benefits of edge-computing capabilities for efficient inference, aiming at minimizing the vehicle downtime incidents. The integration of both approaches holds the promise of distributed learning parallelization over the wireless compute medium and the realization of data intensive V2X services with stringent performance requirements.","title":"use cases"},{"location":"use_cases.html#vehicular-condition-monitoring-with-robust-v2x-connectivity-extend","text":"Use Case Extend Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. However, wireless transmission of in-vehicle monitoring data comes inadvertently with challenges which involve distortions and missing data owing to the inherently shared wireless medium. Incomplete vehicular data attributed to either increased interference levels, channel attenuation phenomena or sensor malfunctioning may lead to incorrect decision-making and failure to predict maintenance actions. Addressing the degradation of transmitted V2X data is thus essential for high-fidelity estimation of the system network state and informed actions. In this context, machine learning (ML)-empowered signal reconstruction and imputation techniques are expected to obtain accurate estimates of incomplete sensor trajectories by leveraging the intrinsic spatiotemporal synergies of captured information. By deeply investigating the interplay between vehicle dynamics and fault provisioning, faulty vehicular equipment will be timely detected/predicted, improving road safety by preventing critical failures.","title":"Vehicular condition monitoring with robust V2X connectivity - EXTEND"},{"location":"use_cases.html#vehicular-condition-monitoring-with-robust-v2x-connectivity-devise","text":"Use Case Devise Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. Wireless transmission of in-vehicle monitoring data is susceptible to various security threats which may destabilize system operation, degrade network performance, and potentially lead to incorrect decision-making regarding the vehicle condition status. At the same time, the pervasive integration of data-driven techniques expands the attack surface, giving rise to finely targeted, stealthier, and scalable attacks targeting both phases (training, inference) of ML techniques. The mitigation of such threats becomes imperative for the correct estimation of vehicle condition. Condition monitoring data used for subsequent predictive diagnostics should be properly secured such that they do not contain falsified information, while abnormal traffic should be detected and isolated in its entirety. In this context, appropriate security countermeasures are expected to accurately detect malicious content in the transmitted information and ensure the semantic correctness of the aggregated data for trustworthy decision-making.","title":"Vehicular condition monitoring with robust V2X connectivity - DEVISE"},{"location":"use_cases.html#vehicular-condition-monitoring-with-robust-v2x-connectivity-verify","text":"Use Case Extend 01 Use Case Extend 02 Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. The limited computational capabilities of edge nodes pose significant challenges for the realization of computationally efficient V2X communication aimed for condition monitoring. Aiming to achieve dramatically reduced data processing times, over-the-air computing methods and decentralized deployments of AI models aim at rapid knowledge extraction from the vehicular measurement streams and reduction of computational overhead. In this context, over-the-air computing methods will be explored to address the computational limitations and stringent latency requirements in V2X systems by means of over-the-air aggregation. In addition, decentralized methods for predictive diagnosis of vehicles condition will be investigated to leverage the benefits of edge-computing capabilities for efficient inference, aiming at minimizing the vehicle downtime incidents. The integration of both approaches holds the promise of distributed learning parallelization over the wireless compute medium and the realization of data intensive V2X services with stringent performance requirements.","title":"Vehicular condition monitoring with robust V2X connectivity - VERIFY"},{"location":"idneo/datasets.html","text":"Datasets \u00b6 DS1 : Ignition on but stopped DS2 : national road. ~ 90km/h DS3 : highway. ~ 120km/h Data structure \u00b6 STAMP : Time delay in seconds after starting the log. STAMP Sensor 1.02 value 2.48 value 3.95 value","title":"datasets"},{"location":"idneo/datasets.html#datasets","text":"DS1 : Ignition on but stopped DS2 : national road. ~ 90km/h DS3 : highway. ~ 120km/h","title":"Datasets"},{"location":"idneo/datasets.html#data-structure","text":"STAMP : Time delay in seconds after starting the log. STAMP Sensor 1.02 value 2.48 value 3.95 value","title":"Data structure"},{"location":"idneo/vehicle_injector_tool.html","text":"Vehicle Injector Tool \u00b6 This tool facilitates the incorporation of real data from a vehicle's sensors, stored in csv Datasets. Its purpose is to facilitate the development of algorithms responsible for processing the information that is collected and stored in this REDIS database within the Success6G project. graph LR Client(VEHICLE <br> INJECTOR) -- TCP/IP <br> (63790 / 127.0.0.1)--> Database(REDIS <br> DATABASE); click Client \"https://github.com/5uperpalo/success6g-edge/blob/main/tools/vehicle/vehicle_injector.py\" click Database \"https://github.com/5uperpalo/success6g-edge/blob/main/configs/edge_redis.yaml\" Dataset[Dataset File] --> Client classDef orange fill:#f96,stroke:#333,stroke-width:2px class Dataset orange Usage \u00b6 Install the Redis database in your local development setup. docker_compose -d configs/edge_redis.yaml Verify docker container is running docker ps -a Run \u201cVehicle Injector\u201c script with dataset and station-id script: python3 vehicle_injector.py -f","title":"vehicle injector tool"},{"location":"idneo/vehicle_injector_tool.html#vehicle-injector-tool","text":"This tool facilitates the incorporation of real data from a vehicle's sensors, stored in csv Datasets. Its purpose is to facilitate the development of algorithms responsible for processing the information that is collected and stored in this REDIS database within the Success6G project. graph LR Client(VEHICLE <br> INJECTOR) -- TCP/IP <br> (63790 / 127.0.0.1)--> Database(REDIS <br> DATABASE); click Client \"https://github.com/5uperpalo/success6g-edge/blob/main/tools/vehicle/vehicle_injector.py\" click Database \"https://github.com/5uperpalo/success6g-edge/blob/main/configs/edge_redis.yaml\" Dataset[Dataset File] --> Client classDef orange fill:#f96,stroke:#333,stroke-width:2px class Dataset orange","title":"Vehicle Injector Tool"},{"location":"idneo/vehicle_injector_tool.html#usage","text":"Install the Redis database in your local development setup. docker_compose -d configs/edge_redis.yaml Verify docker container is running docker ps -a Run \u201cVehicle Injector\u201c script with dataset and station-id script: python3 vehicle_injector.py -f","title":"Usage"},{"location":"inference_model/eda.html","text":"Preprocess data \u00b6 analysis \u00b6 init_check \u00b6 init_check ( df , identifier = None , cat_cols = None , cont_cols = None , verbose = False , ) Procedure to check: * duplicated rows in teh dataset * general stats of numerical features * general stats of categorical features Parameters: df ( DataFrame ) \u2013 pandas dataframe identifier ( str ) \u2013 column which identifies unique user IDs cat_cols ( list ) \u2013 categorical features in the dataset cont_cols ( list ) \u2013 numerical features in the dataset Returns: duplicated_ids ( int ) \u2013 cont_cols_desc ( pd . DataFrame ) \u2013 cat_cols_desc ( pd . DataFrame ) \u2013 Source code in inference_model/eda/features/analysis.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 def init_check ( df : pd . DataFrame , identifier : Optional [ str ] = None , cat_cols : Optional [ list ] = None , cont_cols : Optional [ list ] = None , verbose : bool = False , ) -> Tuple [ int , pd . DataFrame , pd . DataFrame ]: \"\"\"Procedure to check: * duplicated rows in teh dataset * general stats of numerical features * general stats of categorical features Args: df (DataFrame): pandas dataframe identifier (str): column which identifies unique user IDs cat_cols (list): categorical features in the dataset cont_cols (list): numerical features in the dataset Returns: duplicated_ids (int): cont_cols_desc (pd.DataFrame): cat_cols_desc (pd.DataFrame): \"\"\" data = df . copy () if identifier : duplicated_ids = data [ identifier ] . duplicated () . sum () if verbose : print ( \"[CHECK] Number of duplicated ids: {} \" . format ( duplicated_ids )) else : duplicated_ids = None if cont_cols : cont_cols_f = intsec ( data . columns . values , cont_cols ) cont_cols_desc = data [ cont_cols_f ] . describe () . transpose () if verbose : print ( \"[CHECK] Numerical columns\" ) with pd . option_context ( \"display.precision\" , 2 ): print ( cont_cols_desc ) else : cont_cols_desc = None if cat_cols : cat_cols_f = intsec ( data . columns . values , cat_cols ) cat_cols_desc = data [ cat_cols_f ] . describe () . transpose () if verbose : print ( \"[CHECK] Categorical columns\" ) with pd . option_context ( \"display.precision\" , 2 ): print ( cat_cols_desc ) else : cat_cols_desc = None return duplicated_ids , cont_cols_desc , cat_cols_desc missing \u00b6 missing ( df , scale = 'linear' , plot = False ) Procedure to check fraction of missing values in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: missing_val_frac ( DataFrame ) \u2013 sorted dataframe with fraction of missing values per feature fig ( Figure ) \u2013 plot with sorted fractions of missing values in each column Source code in inference_model/eda/features/analysis.py 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 def missing ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to check fraction of missing values in the dataset. Args: df (DataFrame): pandas dataframe scale (str): y scale of the plot plot (bool): whether to output the plot Returns: missing_val_frac (DataFrame): sorted dataframe with fraction of missing values per feature fig (Figure): plot with sorted fractions of missing values in each column \"\"\" data = df . copy () missing_val_frac = data . isna () . sum () / len ( data ) missing_val_frac . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( missing_val_frac , ax , title = \"Fraction of missing values in the dataset\" , ) else : fig = None ax . set_yscale ( scale ) return missing_val_frac , fig zero \u00b6 zero ( df , scale = 'linear' , plot = False ) Procedure to check fraction of zero values in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: zero_val_frac ( DataFrame ) \u2013 sorted dataframe with fraction of '0' values per feature fig ( Figure ) \u2013 plot with sorted fractions of '0' values in each column Source code in inference_model/eda/features/analysis.py 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 def zero ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to check fraction of zero values in the dataset. Args: df (DataFrame): pandas dataframe scale (str): y scale of the plot plot (bool): whether to output the plot Returns: zero_val_frac (DataFrame): sorted dataframe with fraction of '0' values per feature fig (Figure): plot with sorted fractions of '0' values in each column \"\"\" data = df . copy () zero_val_frac = data . isin ([ 0 ]) . sum () / len ( data ) zero_val_frac . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( zero_val_frac , ax , title = \"Fraction of zero values in the dataset\" ) else : fig = None ax . set_yscale ( scale ) return zero_val_frac , fig nunique \u00b6 nunique ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their number of unique values. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted number of unique values in features Source code in inference_model/eda/features/analysis.py 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 def nunique ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their number of unique values. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted number of unique values in features \"\"\" data = df . copy () data_nunique = data . nunique () data_nunique . sort_values ( ascending = True , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( data_nunique . dropna (), ax , title = \"Features number of unique values (NA are dropped)\" , ) else : fig = None ax . set_yscale ( scale ) return data_nunique , fig std \u00b6 std ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their variance. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted standard deviation of continuous features Source code in inference_model/eda/features/analysis.py 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def std ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their variance. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted standard deviation of continuous features \"\"\" data = df . copy () data_std = data . std () data_std . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( data_std . dropna (), ax , title = \"Features variance (NA are dropped)\" ) else : fig = None ax . set_yscale ( scale ) return data_std , fig entropy \u00b6 entropy ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their entropy. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted entropy of the features Source code in inference_model/eda/features/analysis.py 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 def entropy ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their entropy. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted entropy of the features \"\"\" data = df . copy () data = data . astype ( str ) col_entropies = data . apply ( entropy_calc , axis = 0 ) col_entropies . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( col_entropies . dropna (), ax , title = \"Features entropies (NA are dropped)\" ) else : fig = None ax . set_yscale ( scale ) return col_entropies , fig plotting \u00b6 cross_correlation \u00b6 cross_correlation ( df , n = 10 , verbose = False ) Procedure to calculate and plot cross-correlation of features in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe verbose ( bool ) \u2013 show n most correlated features n ( int ) \u2013 number correlated features in verbose output Returns: fig ( Figure ) \u2013 heatmap of continuous features cross correlations Source code in inference_model/eda/features/plotting.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def cross_correlation ( df : pd . DataFrame , n : int = 10 , verbose : bool = False , ) -> Figure : \"\"\"Procedure to calculate and plot cross-correlation of features in the dataset. Args: df (DataFrame): pandas dataframe verbose (bool): show n most correlated features n (int): number correlated features in verbose output Returns: fig (Figure): heatmap of continuous features cross correlations \"\"\" data = df . copy () # corrwith uses numpy which does not like pandas Float dtypes corr_matrix = data . astype ( float ) . corr () upper_stacked_sorted = ( corr_matrix . abs () . where ( np . triu ( np . ones ( corr_matrix . shape ), k = 1 ) . astype ( \"bool\" )) . stack () . sort_values ( ascending = False ) ) fig , ax = plt . subplots ( figsize = ( 10 , 10 )) ax . set_title ( \"Cross-correlation matrix\" , size = 20 ) sns . set ( font_scale = 1.0 ) sns . heatmap ( corr_matrix , ax = ax , cbar = True , square = True , # linewidths=0.5, fmt = \".2f\" , ) if verbose : print ( \"Top {} absolute correlations\" . format ( n )) print ( upper_stacked_sorted [: n ]) return fig distributions \u00b6 distributions ( df , low_per_cut = 0 , high_per_cut = 1 , type = \"box\" ) Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Parameters: df ( DataFrame ) \u2013 pandas dataframe low_per_cut ( float ) \u2013 lower percentile where to cut the plot for better readability high_per_cut ( float ) \u2013 higher percentile where to cut the plot for better readability type ( str ) \u2013 type of distribution plot Returns: fig ( Figure ) \u2013 ditribution plot per each feature Source code in inference_model/eda/features/plotting.py 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 def distributions ( df : pd . DataFrame , low_per_cut : float = 0 , high_per_cut : float = 1 , type : Literal [ \"box\" , \"violin\" ] = \"box\" , ) -> Figure : \"\"\"Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Args: df (DataFrame): pandas dataframe low_per_cut (float): lower percentile where to cut the plot for better readability high_per_cut (float): higher percentile where to cut the plot for better readability type (str): type of distribution plot Returns: fig (Figure): ditribution plot per each feature \"\"\" data = df . copy () cols = data . columns . values data = data . fillna ( 0 ) . astype ( float ) fig , ax = plt . subplots ( len ( cols ), 1 , figsize = ( 16 , len ( cols ) * 2 )) fig . suptitle ( \"Distribution of feature values\" , size = 20 ) for col , i in zip ( cols , range ( len ( cols ))): if type == \"violin\" : sns . violinplot ( ax = ax [ i ], x = col , orient = \"h\" , # cut=0, # showextrem=False, data = data , ) if type == \"box\" : sns . boxplot ( ax = ax [ i ], x = col , orient = \"h\" , data = data , ) ax [ i ] . set_xlim ( data [ col ] . quantile ( low_per_cut ), data [ col ] . quantile ( high_per_cut ) ) ax [ i ] . get_yaxis () . set_visible ( False ) plt . subplots_adjust ( hspace = 0.9 ) return fig analysis \u00b6 correlation \u00b6 correlation ( df , target , scale = 'linear' , plot = False ) Procedure to plot most correlated numerical features with target column. Parameters: df ( pd . DataFrame ) \u2013 pandas dataframe target ( pd . Series ) \u2013 target values scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: sorted_corr_cols ( pd . DataFrame ) \u2013 sorted dataframe with feature and target value correlation fig ( Figure ) \u2013 sorted bar plot with feature and target value correlation Source code in inference_model/eda/target/analysis.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 def correlation ( df : pd . DataFrame , target : pd . Series , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot most correlated numerical features with target column. Args: df (pd.DataFrame): pandas dataframe target (pd.Series): target values scale (str): y scale of the plot plot (bool): whether to output the plot Returns: sorted_corr_cols (pd.DataFrame): sorted dataframe with feature and target value correlation fig (Figure): sorted bar plot with feature and target value correlation \"\"\" data = df . copy () target_col = target . name correlation_df = pd . concat ([ data , target ], axis = 1 ) . corr () sorted_corr_cols = correlation_df [ target_col ] . drop ( index = [ target_col ]) sorted_corr_cols . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( sorted_corr_cols , ax , title = f \"Sorted features by their correlation with { target_col } \" , ) else : fig = None ax . set_yscale ( scale ) return sorted_corr_cols , fig plotting \u00b6 prob_distrib_per_class \u00b6 prob_distrib_per_class ( predicted_probs , actual , task ) Procedure to plot probability density distributions per class from LightGBM predictions. Parameters: predicted_probs ( ndarray ) \u2013 predicted probs actual ( ndarray ) \u2013 ground truth classes task ( str ) \u2013 type of task Returns: fig ( Figure ) \u2013 probability density ditributions plot per each class Source code in inference_model/eda/target/plotting.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def prob_distrib_per_class ( predicted_probs : np . ndarray , actual : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ], ) -> Figure : \"\"\"Procedure to plot probability density distributions per class from LightGBM predictions. Args: predicted_probs (ndarray): predicted probs actual (ndarray): ground truth classes task (str): type of task Returns: fig (Figure): probability density ditributions plot per each class \"\"\" if task == \"binary\" : temp = pd . DataFrame ({ \"predicted_proba\" : predicted_probs , \"actual\" : actual }) fig , ax = plt . subplots ( figsize = ( 3 , 2 )) fig . suptitle ( \"Predicted probability density per class\" ) for label , alpha , color in zip ([ 0 , 1 ], [ 1 , 0.7 ], [ \"black\" , \"red\" ]): ax . hist ( temp [ temp [ \"actual\" ] == label ][ \"predicted_proba\" ], density = True , bins = np . linspace ( 0 , 1 , 10 ), alpha = alpha , color = color , ec = \"k\" , ) ax . set_xlim ([ 0 , 1 ]) ax . set_xlabel ( \"probability\" ) ax . set_ylabel ( \"probability_density\" ) elif task == \"multiclass\" : temp = pd . DataFrame ( { \"predicted_proba\" : np . take_along_axis ( predicted_probs , np . vstack ( actual ), axis = 1 # type: ignore ) . flatten (), \"actual\" : actual , } ) # Dictionary of color for each label color_d = dict ( zip_longest ( temp [ \"actual\" ] . unique (), plt . rcParams [ \"axes.prop_cycle\" ] . by_key ()[ \"color\" ], ) ) n_classes = temp [ \"actual\" ] . nunique () fig , ax = plt . subplots ( ncols = n_classes , figsize = ( n_classes * 3 , 2 ), sharex = True , sharey = True ) fig . suptitle ( \"Predicted probability density per class\" ) plt . subplots_adjust ( wspace = 0.3 , top = 0.75 ) for i , ( label , gp ) in enumerate ( temp . groupby ( \"actual\" )): ax [ i ] . hist ( gp [ \"predicted_proba\" ], density = True , bins = np . linspace ( 0 , 1 , 10 ), color = color_d [ label ], ec = \"k\" , ) ax [ i ] . set_title ( label ) ax [ i ] . set_xlim ([ 0 , 1 ]) ax [ i ] . set_xlabel ( \"probability\" ) ax [ i ] . set_ylabel ( \"probability_density\" ) return fig distributions_in_binary_cls \u00b6 distributions_in_binary_cls ( df , target , low_per_cut = 0 , high_per_cut = 1 ) Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Parameters: df ( DataFrame ) \u2013 pandas dataframe cont_cols ( list ) \u2013 numerical features in the dataset target ( pd . Series ) \u2013 target values, i.e. binary classes Returns: fig ( Figure ) \u2013 ditribution plot per each feature Source code in inference_model/eda/target/plotting.py 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 def distributions_in_binary_cls ( df : pd . DataFrame , target : pd . Series , low_per_cut : float = 0 , high_per_cut : float = 1 , ) -> Figure : \"\"\"Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Args: df (DataFrame): pandas dataframe cont_cols (list): numerical features in the dataset target (pd.Series): target values, i.e. binary classes Returns: fig (Figure): ditribution plot per each feature \"\"\" data = pd . concat ([ df , target ], axis = 1 ) cols = data . columns . values data [ \"dummy\" ] = 0 data = data . fillna ( 0 ) . astype ( float ) fig , ax = plt . subplots ( len ( cols ), 1 , figsize = ( 16 , len ( cols ) * 2 )) fig . suptitle ( f \"Distribution of feature values splitted by { target . name } \" , size = 20 ) for col , i in zip ( cols , range ( len ( cols ))): if col != target . name : sns . violinplot ( ax = ax [ i ], hue = target . name , x = col , y = \"dummy\" , orient = \"h\" , cut = 0 , showextrem = False , split = True , data = data , ) ax [ i ] . get_legend () . remove () ax [ i ] . set_xlim ( data [ col ] . quantile ( low_per_cut ), data [ col ] . quantile ( high_per_cut ) ) ax [ i ] . get_yaxis () . set_visible ( False ) plt . subplots_adjust ( hspace = 0.9 , top = 0.97 ) return fig plotting \u00b6 bar_plot \u00b6 bar_plot ( df , ax , title ) Helper method for unified bar plot Parameters: df ( DataFrame ) \u2013 dataframe to plot ax ( Axes ) \u2013 axes defining where to plot it Returns: adjusted_plot ( Axes ) \u2013 adjusted axes Source code in inference_model/eda/plotting.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 def bar_plot ( df : pd . DataFrame , ax : Axes , title : str ) -> Axes : \"\"\"Helper method for unified bar plot Args: df (DataFrame): dataframe to plot ax (Axes): axes defining where to plot it Returns: adjusted_plot (Axes): adjusted axes \"\"\" data = df . copy () ax . set_title ( title , size = 20 ) ax . tick_params ( labelsize = 8 ) ax . set_xlabel ( \"sorted features\" , size = 12 ) return data . plot . bar ( ax = ax ) general_utils \u00b6 intsec \u00b6 intsec ( list1 , list2 ) Simple intesection of two lists. Parameters: list1 ( list ) \u2013 list1 list2 ( list ) \u2013 list2 Returns: list ( list ) \u2013 intersection of lists Source code in inference_model/eda/general_utils.py 6 7 8 9 10 11 12 13 14 def intsec ( list1 : list , list2 : list ) -> list : \"\"\"Simple intesection of two lists. Args: list1 (list): list1 list2 (list): list2 Returns: list (list): intersection of lists \"\"\" return list ( set . intersection ( set ( list1 ), set ( list2 ))) entropy_calc \u00b6 entropy_calc ( labels , base = np . e ) Computes entropy of both continuous and categorical features. Shamelessly stolen from : https://stackoverflow.com/a/45091961 Parameters: labels ( list , ndarray , Series ) \u2013 list of values Returns: ent ( float ) \u2013 entropy of the list of values Source code in inference_model/eda/general_utils.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def entropy_calc ( labels : list , base : float = np . e ) -> float : \"\"\"Computes entropy of both continuous and categorical features. Shamelessly stolen from : https://stackoverflow.com/a/45091961 Args: labels (list, ndarray, Series): list of values Returns: ent (float): entropy of the list of values \"\"\" n_labels = len ( labels ) value , counts = np . unique ( labels , return_counts = True ) probs = counts / n_labels n_classes = np . count_nonzero ( probs ) ent = float ( 0 ) if n_classes > 1 : for i in probs : ent -= i * math . log ( i , base ) return ent","title":"eda"},{"location":"inference_model/eda.html#preprocess-data","text":"","title":"Preprocess data"},{"location":"inference_model/eda.html#inference_model.eda.features.analysis","text":"","title":"analysis"},{"location":"inference_model/eda.html#inference_model.eda.features.plotting","text":"","title":"plotting"},{"location":"inference_model/eda.html#inference_model.eda.target.analysis","text":"","title":"analysis"},{"location":"inference_model/eda.html#inference_model.eda.target.plotting","text":"","title":"plotting"},{"location":"inference_model/eda.html#inference_model.eda.plotting","text":"","title":"plotting"},{"location":"inference_model/eda.html#inference_model.eda.general_utils","text":"","title":"general_utils"},{"location":"inference_model/preprocessing.html","text":"Preprocess data \u00b6 label_encoder \u00b6 LabelEncoder \u00b6 LabelEncoder ( columns_to_encode = None ) Bases: object Label Encode categorical values for multiple columns at once NOTE : Shamlessly copied from https://github.com/jrzaurin/pytorch-widedeep NOTE : LabelEncoder reserves 0 for unseen new categories. This is convenient when defining the embedding layers, since we can just set padding idx to 0. Parameters: columns_to_encode ( list, Optional, default = None ) \u2013 List of strings containing the names of the columns to encode. If None all columns of type object in the dataframe will be label encoded. Attributes: encoding_dict ( Dict ) \u2013 Dictionary containing the encoding mappings in the format, e.g. : {'colname1': {'cat1': 1, 'cat2': 2, ...}, 'colname2': {'cat1': 1, 'cat2': 2, ...}, ...} # noqa inverse_encoding_dict(Dict) ( Dict ) \u2013 Dictionary containing the inverse encoding mappings in the format, e.g. : {'colname1': {1: 'cat1', 2: 'cat2', ...}, 'colname2': {1: 'cat1', 2: 'cat2', ...}, ...} # noqa Source code in inference_model/preprocessing/label_encoder.py 29 30 31 32 33 def __init__ ( self , columns_to_encode : Optional [ List [ str ]] = None , ): self . columns_to_encode = columns_to_encode fit \u00b6 fit ( df ) Creates encoding attributes Returns: LabelEncoder ( LabelEncoder ) \u2013 LabelEncoder fitted object Source code in inference_model/preprocessing/label_encoder.py 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 def fit ( self , df : pd . DataFrame ) -> \"LabelEncoder\" : \"\"\"Creates encoding attributes Returns: LabelEncoder: `LabelEncoder` fitted object \"\"\" df_inp = df . copy () if self . columns_to_encode is None : self . columns_to_encode = list ( df_inp . select_dtypes ( include = [ \"object\" ]) . columns ) else : # sanity check to make sure all categorical columns are in an adequate # format for col in self . columns_to_encode : df_inp [ col ] = df_inp [ col ] . astype ( \"O\" ) unique_column_vals = dict () for c in self . columns_to_encode : unique_column_vals [ c ] = df_inp [ c ] . unique () self . encoding_dict = dict () # leave 0 for padding/\"unseen\" categories idx = 1 for k , v in unique_column_vals . items (): self . encoding_dict [ k ] = { o : i + idx for i , o in enumerate ( unique_column_vals [ k ]) } idx = 1 self . inverse_encoding_dict = dict () for c in self . encoding_dict : self . inverse_encoding_dict [ c ] = { v : k for k , v in self . encoding_dict [ c ] . items () } self . inverse_encoding_dict [ c ][ 0 ] = \"unseen\" return self transform \u00b6 transform ( df ) Label Encoded the categories in columns_to_encode Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Source code in inference_model/preprocessing/label_encoder.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 def transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Label Encoded the categories in `columns_to_encode` Returns: pd.DataFrame: label-encoded dataframe \"\"\" try : self . encoding_dict except AttributeError : raise NotFittedError ( \"This LabelEncoder instance is not fitted yet. \" \"Call 'fit' with appropriate arguments before using this LabelEncoder.\" ) df_inp = df . copy () # sanity check to make sure all categorical columns are in an adequate # format for col in self . columns_to_encode : # type: ignore df_inp [ col ] = df_inp [ col ] . astype ( \"O\" ) for k , v in self . encoding_dict . items (): df_inp [ k ] = df_inp [ k ] . apply ( lambda x : v [ x ] if x in v . keys () else 0 ) return df_inp fit_transform \u00b6 fit_transform ( df ) Combines fit and transform Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd . DataFrame ({ 'col1' : [ 1 , 2 , 3 ], 'col2' : [ 'me' , 'you' , 'him' ]}) >>> columns_to_encode = [ 'col2' ] >>> encoder = LabelEncoder ( columns_to_encode ) >>> encoder . fit_transform ( df ) col1 col2 0 1 1 1 2 2 2 3 3 >>> encoder . encoding_dict {'col2': {'me': 1, 'you': 2, 'him': 3}} Source code in inference_model/preprocessing/label_encoder.py 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 def fit_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Combines `fit` and `transform` Returns: pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']}) >>> columns_to_encode = ['col2'] >>> encoder = LabelEncoder(columns_to_encode) >>> encoder.fit_transform(df) col1 col2 0 1 1 1 2 2 2 3 3 >>> encoder.encoding_dict {'col2': {'me': 1, 'you': 2, 'him': 3}} \"\"\" return self . fit ( df ) . transform ( df ) inverse_transform \u00b6 inverse_transform ( df ) Returns the original categories Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd . DataFrame ({ 'col1' : [ 1 , 2 , 3 ], 'col2' : [ 'me' , 'you' , 'him' ]}) >>> columns_to_encode = [ 'col2' ] >>> encoder = LabelEncoder ( columns_to_encode ) >>> df_enc = encoder . fit_transform ( df ) >>> encoder . inverse_transform ( df_enc ) col1 col2 0 1 me 1 2 you 2 3 him Source code in inference_model/preprocessing/label_encoder.py 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def inverse_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Returns the original categories Returns: pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']}) >>> columns_to_encode = ['col2'] >>> encoder = LabelEncoder(columns_to_encode) >>> df_enc = encoder.fit_transform(df) >>> encoder.inverse_transform(df_enc) col1 col2 0 1 me 1 2 you 2 3 him \"\"\" for k , v in self . inverse_encoding_dict . items (): df [ k ] = df [ k ] . apply ( lambda x : v [ x ]) return df preprocess_data \u00b6 drop_high_nan_cols \u00b6 drop_high_nan_cols ( df , threshold = 0.8 , verbose = False ) Returns dataframe without columns that have ratio of missingness above threshold. Parameters: df ( pd . DataFrame ) \u2013 input dataframe threshold ( float = 0.8 ) \u2013 ratio of missingness applied per column verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 def drop_high_nan_cols ( df : pd . DataFrame , threshold : float = 0.8 , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without columns that have ratio of missingness above threshold. Args: df (pd.DataFrame): input dataframe threshold (float = 0.8): ratio of missingness applied per column verbose (bool): whether the output should be verbose \"\"\" n_rows = df . shape [ 0 ] nan_fraction_per_col = df . apply ( lambda x : x . isna () . sum () / n_rows , axis = 0 ) high_nan_percentage_cols = nan_fraction_per_col [ nan_fraction_per_col > threshold ] . index . values df = df . drop ( high_nan_percentage_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} columns with fraction of NaN above threshold = {}. Affected columns: {} \"\"\" . format ( len ( high_nan_percentage_cols ), threshold , high_nan_percentage_cols ) ) return df drop_constant_cols \u00b6 drop_constant_cols ( df , verbose = False ) Returns dataframe without constant columns, i.e. those with just 1 unique value for all rows. Source code in inference_model/preprocessing/preprocess_data.py 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 def drop_constant_cols ( df : pd . DataFrame , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without constant columns, i.e. those with just 1 unique value for all rows.\"\"\" nunique_per_col = df . apply ( lambda x : x . nunique (), axis = 0 ) const_cols = nunique_per_col [ nunique_per_col == 1 ] . index . values df = df . drop ( const_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} constant columns. Affected columns: {} \"\"\" . format ( len ( const_cols ), const_cols ) ) return df drop_high_uq_cat_cols \u00b6 drop_high_uq_cat_cols ( df , cat_cols , uq_val_count , verbose = False ) Returns dataframe without categorical columns that have too many unique values Parameters: df ( pd . DataFrame ) \u2013 input dataframe cat_cols ( list ) \u2013 list of categorical columns uq_val_count ( int ) \u2013 unique value count verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 def drop_high_uq_cat_cols ( df : pd . DataFrame , cat_cols : list , uq_val_count : int , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without categorical columns that have too many unique values Args: df (pd.DataFrame): input dataframe cat_cols (list): list of categorical columns uq_val_count (int): unique value count verbose (bool): whether the output should be verbose \"\"\" nunique_per_col = df [ cat_cols ] . apply ( lambda x : x . nunique (), axis = 0 ) high_uq_value_cat_cols = nunique_per_col [ nunique_per_col > uq_val_count ] . index . values df = df . drop ( high_uq_value_cat_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} high unique value columns. Affected columns: {} \"\"\" . format ( len ( high_uq_value_cat_cols ), high_uq_value_cat_cols ) ) return df drop_highly_correlated_columns \u00b6 drop_highly_correlated_columns ( df , cont_cols , crosscorr_val = 0.95 , verbose = False ) Returns dataframe without highly correlated columns, cross correlation is evaluated with crosscorr_val. Parameters: df ( pd . DataFrame ) \u2013 input dataframe cont_cols ( list ) \u2013 list of columns to evaluate correlation for crosscorr_val ( float = 0.95 ) \u2013 threshold value of correlation verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 def drop_highly_correlated_columns ( df : pd . DataFrame , cont_cols : list , crosscorr_val : float = 0.95 , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Returns dataframe without highly correlated columns, cross correlation is evaluated with crosscorr_val. Args: df (pd.DataFrame): input dataframe cont_cols (list): list of columns to evaluate correlation for crosscorr_val (float = 0.95): threshold value of correlation verbose (bool): whether the output should be verbose \"\"\" corr_matrix = df [ cont_cols ] . corr () . abs () upper = corr_matrix . where ( np . triu ( np . ones ( corr_matrix . shape ), k = 1 ) . astype ( \"bool\" )) upper_above = upper . apply ( lambda x : any ( x > crosscorr_val ), axis = 1 ) to_drop = upper_above [ upper_above ] . index . values df = df . drop ( to_drop , axis = 1 ) if verbose : print ( \"\"\" Dropped {} highly correlated columns. Affected columns: {} \"\"\" . format ( len ( to_drop ), to_drop ) ) return df nan_with_unknown_imputer \u00b6 nan_with_unknown_imputer ( df , columns , fill_token = \"unknown\" , verbose = False ) Fills NAs with surrogate string, 'unknown' is default value used, it can be customized. Parameters: df ( pd . DataFrame ) \u2013 input dataframe columns ( List [ str ] ) \u2013 ist of columns that will be filled fillna_token ( str = \"unknown\" ) \u2013 string used to replace NAs verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 def nan_with_unknown_imputer ( df : pd . DataFrame , columns : List [ str ], fill_token : str = \"unknown\" , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Fills NAs with surrogate string, 'unknown' is default value used, it can be customized. Args: df (pd.DataFrame): input dataframe columns (List[str]): ist of columns that will be filled fillna_token (str = \"unknown\"): string used to replace NAs verbose (bool): whether the output should be verbose \"\"\" df = df . copy () if verbose : sum_nan_vals = df [ columns ] . isna () . sum () nans_cols = [] for c in columns : nans_cols . append (( c , df [ c ] . isna () . sum ())) for c in columns : df [ c ] = df [ c ] . astype ( object ) . fillna ( fill_token ) dfc = df . copy () dfc [ columns ] = dfc [ columns ] . apply ( lambda x : x . astype ( object ) . fillna ( fill_token )) if verbose : print ( \"\"\" Imputed {} NaN values with {}. Affected columns (col, num_NaNs): {} \"\"\" . format ( sum_nan_vals , fill_token , nans_cols ) ) return df nan_with_number_imputer \u00b6 nan_with_number_imputer ( df , columns , fill_number =- 1.0 , verbose = False ) Fills NAs with surrogate float, -1 is default value used, it can be customized. Parameters: df ( pd . DataFrame ) \u2013 input dataframe columns ( List [ str ] ) \u2013 list of columns that will be filled fill_number ( float = -1 ) \u2013 number used to replace NAs. Defaults to verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 def nan_with_number_imputer ( df : pd . DataFrame , columns : List [ str ], fill_number : float = - 1.0 , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Fills NAs with surrogate float, -1 is default value used, it can be customized. Args: df (pd.DataFrame): input dataframe columns (List[str]): list of columns that will be filled fill_number (float = -1): number used to replace NAs. Defaults to verbose (bool): whether the output should be verbose \"\"\" dfc = df . copy () if verbose : sum_nan_vals = df [ columns ] . isna () . sum () nans_cols = [] for c in columns : nans_cols . append (( c , df [ c ] . isna () . sum ())) dfc = df . copy () dfc [ columns ] = dfc [ columns ] . apply ( lambda x : x . astype ( float ) . fillna ( fill_number )) if verbose : print ( \"\"\" Imputed {} NaN values with {}. Affected columns (col, num_NaNs): {} \"\"\" . format ( sum_nan_vals , fill_number , nans_cols ) ) return dfc nuq_in_list_col \u00b6 nuq_in_list_col ( dfs ) Returns pd.Series with the number of unique values in the lists. Parameters: dfs ( pd . Series ) \u2013 input pandas series containing string list values Source code in inference_model/preprocessing/preprocess_data.py 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 def nuq_in_list_col ( dfs : pd . Series ): \"\"\"Returns pd.Series with the number of unique values in the lists. Args: dfs (pd.Series): input pandas series containing string list values \"\"\" dfsc = dfs . copy () dfsc . apply ( lambda string : _nunique ( list ( string . replace ( \"[\" , \"\" ) . replace ( \"]\" , \"\" ) . split ( \", \" )) ) if isinstance ( string , str ) else string ) return dfsc most_frequent_in_list_col \u00b6 most_frequent_in_list_col ( dfs ) Returns pd.Series with the most frequent values in the lists. Parameters: dfs ( pd . Series ) \u2013 input pandas series containing string list values Source code in inference_model/preprocessing/preprocess_data.py 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 def most_frequent_in_list_col ( dfs : pd . Series ): \"\"\"Returns pd.Series with the most frequent values in the lists. Args: dfs (pd.Series): input pandas series containing string list values \"\"\" dfsc = dfs . copy () dfsc . apply ( lambda string : _most_frequent ( list ( string . replace ( \"[\" , \"\" ) . replace ( \"]\" , \"\" ) . split ( \", \" )) ) if isinstance ( string , str ) else string ) return dfsc replace_rare_categories_with_str_other \u00b6 replace_rare_categories_with_str_other ( df , categorical_cols , quantile = 0.05 , surrogate_value = \"other\" , verbose = False , ) Replaces rare category value with surrogate string. Parameters: df ( pd . DataFrame ) \u2013 input dataframe categorical_cols ( List [ str ] ) \u2013 list of columns in dataframe to process. quantile ( float = 0.05 ) \u2013 determines what values are considered as rare surrogate_value ( str = \"other\" ) \u2013 string used to replace rare values Returns: Tuple [ pd . DataFrame , Dict ] \u2013 Tuple[pd.DataFrame, Dict]: New dataframe and a dict. with mapping between orig. and surrogate values. Source code in inference_model/preprocessing/preprocess_data.py 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 def replace_rare_categories_with_str_other ( df : pd . DataFrame , categorical_cols : List [ str ], quantile : float = 0.05 , surrogate_value : str = \"other\" , verbose : bool = False , ) -> Tuple [ pd . DataFrame , Dict ]: \"\"\"Replaces rare category value with surrogate string. Args: df (pd.DataFrame): input dataframe categorical_cols (List[str]): list of columns in dataframe to process. quantile (float = 0.05): determines what values are considered as rare surrogate_value (str = \"other\"): string used to replace rare values Returns: Tuple[pd.DataFrame, Dict]: New dataframe and a dict. with mapping between orig. and surrogate values. \"\"\" if surrogate_value in df [ categorical_cols ] . values : raise ValueError ( \"Surrogate string - \" + surrogate_value + \" - is already present, choose another one.\" ) dfc = df . copy () replace_dict_per_col = ( df [ categorical_cols ] . apply ( _replace_rare_dict , args = [ surrogate_value , quantile ]) . to_dict () ) dfc = dfc . replace ( replace_dict_per_col ) if verbose : sum_rare_cats = sum ( [ len ( replace_dict_per_col [ col ]) for col in replace_dict_per_col ] ) rare_cats_per_col = [ ( col , list ( replace_dict_per_col [ col ] . keys ())) for col in replace_dict_per_col ] print ( \"\"\" Replaced {} rare categories (val_count < {}) with {}. Affected columns (List[(col, rare_cats)]): {} \"\"\" . format ( sum_rare_cats , quantile , surrogate_value , rare_cats_per_col , ) ) return dfc , replace_dict_per_col preprocess \u00b6 PreprocessData \u00b6 PreprocessData ( target_col , id_cols , cat_cols = None , cont_cols = None ) Object to preprocess the dataset. Parameters: target_col ( str ) \u2013 target column name id_cols ( List [ str ] ) \u2013 id columns cat_cols ( Optional [ List [ str ]] ) \u2013 list of categorical column names cont_cols ( Optional [ List [ str ]] ) \u2013 list of continuous column names Source code in inference_model/preprocessing/preprocess.py 28 29 30 31 32 33 34 35 36 37 38 39 40 def __init__ ( self , target_col : str , id_cols : str , cat_cols : Optional [ List [ str ]] = None , cont_cols : Optional [ List [ str ]] = None , ): self . target_col = target_col self . id_cols = id_cols self . cat_cols = cat_cols self . cont_cols = cont_cols self . is_fitted = False fit_transform \u00b6 fit_transform ( df ) Fit peprocessor and transform dataset in training step. Source code in inference_model/preprocessing/preprocess.py 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 def fit_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Fit peprocessor and transform dataset in training step.\"\"\" dfc = df . drop ( columns = self . id_cols ) . copy () dfc = dfc . pipe ( drop_constant_cols ) self . cat_cols = intsec ( list ( dfc ), self . cat_cols ) if self . cat_cols is None : self . cat_cols = self . _infere_cat_cols ( dfc ) dfc_cat_cols = drop_high_nan_cols ( dfc [ self . cat_cols ]) dfc = dfc . drop ( columns = self . cat_cols ) dfc = pd . concat ([ dfc , dfc_cat_cols ], axis = 1 ) self . cat_cols = dfc_cat_cols . columns . values . tolist () if self . cont_cols is None : self . init_cont_cols = self . _infere_cont_cols ( dfc , self . cat_cols ) dfc_init_cont_cols = drop_high_nan_cols ( dfc [ self . init_cont_cols ]) dfc = dfc . drop ( columns = self . init_cont_cols ) dfc = pd . concat ([ dfc , dfc_init_cont_cols ], axis = 1 ) self . init_cont_cols = dfc_init_cont_cols . columns . values . tolist () dfc = nan_with_number_imputer ( dfc , self . init_cont_cols , - 9999.0 ) ( dfc , self . final_cont_cols , ) = self . _drop_highly_corr_cols_and_get_final_cont_cols ( dfc , self . init_cont_cols ) else : self . final_cont_cols = list ( set ( self . cont_cols ) . intersection ( set ( dfc . columns . values )) ) dfc = nan_with_number_imputer ( dfc , self . final_cont_cols , - 9999.0 ) dfc = nan_with_unknown_imputer ( dfc , self . cat_cols ) dfc = dfc [ self . cat_cols + self . final_cont_cols + [ self . target_col ]] self . label_encoder = LabelEncoder ( self . cat_cols ) dfc_le = self . label_encoder . fit_transform ( dfc ) dfc_le = self . _change_int_float_types ( dfc_le ) dfc_le [ self . id_cols ] = df [ self . id_cols ] self . is_fitted = True return dfc_le transform \u00b6 transform ( df ) Transform dataset in inference step. Source code in inference_model/preprocessing/preprocess.py 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 def transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Transform dataset in inference step.\"\"\" if not self . is_fitted : raise NotFittedError ( \"\"\"This instance of 'PreprocessData' has not been fitted yet. Please, run 'fit' first\"\"\" ) dfc = df . drop ( columns = self . id_cols ) . copy () # added as mlflow inference is receiving all the data as objects dfc [ self . cat_cols ] = dfc [ self . cat_cols ] . astype ( str ) dfc [ self . final_cont_cols ] = dfc [ self . final_cont_cols ] . astype ( float ) try : dfc = dfc [ self . cat_cols + self . final_cont_cols + [ self . target_col ]] except KeyError : dfc = dfc [ self . cat_cols + self . final_cont_cols ] # dfc = dfc.replace(self.replace_rare_categories_dict) dfc = nan_with_unknown_imputer ( dfc , self . cat_cols ) dfc = nan_with_number_imputer ( dfc , list ( set ( self . final_cont_cols )), - 9999.0 , # noqa ) dfc_le = self . label_encoder . transform ( dfc ) dfc_le = self . _change_int_float_types ( dfc_le ) dfc_le [ self . id_cols ] = df [ self . id_cols ] return dfc_le fit \u00b6 fit ( df ) Just to keep familiar naming convention with sklearn. Source code in inference_model/preprocessing/preprocess.py 134 135 136 def fit ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Just to keep familiar naming convention with sklearn.\"\"\" return self . fit_transform ( df ) scaler \u00b6 scaler_mapper \u00b6 scaler_mapper ( cont_cols , cat_cols , id_cols , scaler_mapper_def = None ) Function that maps scaler functions to appropriate columns. By default does not assign any scaler to continuous, categorical or identifier columns. The scalers must be set in scaler_mapper_def. Use sklearn scalers. Only columns defined in mapper object will be present in the transformed dataset. Parameters: cont_cols ( list ) \u2013 list of continuous feature columns in the dataset cat_cols ( list ) \u2013 list of categorical feature columns in the dataset id_cols ( list ) \u2013 identifier columns scaler_mapper_def ( dict ) \u2013 optional dictionary that contains keys ['cont_cols', 'cat_cols', 'id_cols'] with their corresponding scalers (defined by names, not instantiated) from sklearn library Returns: scaler ( DataFrameMapper ) \u2013 scaler object mapping sklearn scalers to columns in pandas dataframe Source code in inference_model/preprocessing/scaler.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 def scaler_mapper ( cont_cols : List [ str ], cat_cols : List [ str ], id_cols : List [ str ], scaler_mapper_def : Optional [ dict ] = None , ) -> DataFrameMapper : \"\"\"Function that maps scaler functions to appropriate columns. By default does not assign any scaler to continuous, categorical or identifier columns. The scalers must be set in scaler_mapper_def. Use sklearn scalers. Only columns defined in mapper object will be present in the transformed dataset. Args: cont_cols (list): list of continuous feature columns in the dataset cat_cols (list): list of categorical feature columns in the dataset id_cols (list): identifier columns scaler_mapper_def (dict): optional dictionary that contains keys ['cont_cols', 'cat_cols', 'id_cols'] with their corresponding scalers (defined by names, not instantiated) from sklearn library Returns: scaler (DataFrameMapper): scaler object mapping sklearn scalers to columns in pandas dataframe \"\"\" if scaler_mapper_def : cont_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cont_cols )), classes = [ scaler_mapper_def [ \"cont_cols\" ]], ) cat_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cat_cols )), classes = [ scaler_mapper_def [ \"cat_cols\" ]], ) id_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], id_cols )), classes = [ scaler_mapper_def [ \"id_cols\" ]], ) else : cont_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cont_cols )), classes = [ None ] ) cat_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cat_cols )), classes = [ None ] ) id_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], id_cols )), classes = [ None ] ) scaler = DataFrameMapper ( cont_cols_def + cat_cols_def + id_cols_def , df_out = True ) return scaler","title":"preprocessing"},{"location":"inference_model/preprocessing.html#preprocess-data","text":"","title":"Preprocess data"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.label_encoder","text":"","title":"label_encoder"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.preprocess_data","text":"","title":"preprocess_data"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.preprocess","text":"","title":"preprocess"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.scaler","text":"","title":"scaler"},{"location":"inference_model/training.html","text":"Training \u00b6 metrics \u00b6 smape \u00b6 smape ( actual , predicted ) Symmetric Mean Absolute Percentage Error https://vedexcel.com/how-to-calculate-smape-in-python/ Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Returns: smape ( float ) \u2013 symmetric mean absolute percentage error Source code in inference_model/training/metrics.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 def smape ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Symmetric Mean Absolute Percentage Error https://vedexcel.com/how-to-calculate-smape-in-python/ Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Returns: smape (float): symmetric mean absolute percentage error \"\"\" return ( 100 / len ( actual ) * np . sum ( 2 * np . abs ( predicted - actual ) / ( np . abs ( actual ) + np . abs ( predicted ))) ) ndcg \u00b6 ndcg ( actual , predicted ) Normalized Discounted Cumulative Gain Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Return ndcg (float): normalized discounted cumulative gain Source code in inference_model/training/metrics.py 32 33 34 35 36 37 38 39 40 41 42 43 44 def ndcg ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Normalized Discounted Cumulative Gain Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Return: ndcg (float): normalized discounted cumulative gain \"\"\" return ndcg_score ( [ rankdata ( actual , method = \"ordinal\" )], [ rankdata ( predicted , method = \"ordinal\" )], ) rmse \u00b6 rmse ( actual , predicted ) Root Mean Squared Error Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Returns: rmse ( float ) \u2013 root mean square error Source code in inference_model/training/metrics.py 47 48 49 50 51 52 53 54 55 56 def rmse ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Root Mean Squared Error Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Returns: rmse (float): root mean square error \"\"\" return mean_squared_error ( actual , predicted , squared = False ) nacil \u00b6 nacil ( actual , high_quantile_predicted , low_quantile_predicted ) Normalized Average Confidence Interval Length for Quantile Regression. The value is normalized to actual(expected) value. Parameters: actual ( np . ndarray ) \u2013 actual values high_quantile_predicted ( np . ndarray ) \u2013 high quantile predicted values low_quantile_predicted ( np . ndarray ) \u2013 low quantile predicted values Returns: nacil ( float ) \u2013 normalized average confidence interval length Source code in inference_model/training/metrics.py 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def nacil ( actual : np . ndarray , high_quantile_predicted : np . ndarray , low_quantile_predicted : np . ndarray , ): \"\"\"Normalized Average Confidence Interval Length for Quantile Regression. The value is normalized to actual(expected) value. Args: actual (np.ndarray): actual values high_quantile_predicted (np.ndarray): high quantile predicted values low_quantile_predicted (np.ndarray): low quantile predicted values Returns: nacil (float): normalized average confidence interval length \"\"\" return np . mean (( high_quantile_predicted - low_quantile_predicted ) / actual ) aiqc \u00b6 aiqc ( actual , high_quantile , low_quantile ) Average InterQuantile Coverage for Quantile Regression. Check if the interquantile coverage is close to the coverage of in the test dataset. Parameters: actual ( np . ndarray ) \u2013 actual values high_quantile_predicted ( np . ndarray ) \u2013 high quantile predicted values low_quantile_predicted ( np . ndarray ) \u2013 low quantile predicted values Returns: aiqc ( float ) \u2013 average interquantile coverage Source code in inference_model/training/metrics.py 77 78 79 80 81 82 83 84 85 86 87 88 def aiqc ( actual : np . ndarray , high_quantile : np . ndarray , low_quantile : np . ndarray ): \"\"\"Average InterQuantile Coverage for Quantile Regression. Check if the interquantile coverage is close to the coverage of in the test dataset. Args: actual (np.ndarray): actual values high_quantile_predicted (np.ndarray): high quantile predicted values low_quantile_predicted (np.ndarray): low quantile predicted values Returns: aiqc (float): average interquantile coverage \"\"\" return np . sum (( actual < high_quantile ) & ( actual > low_quantile )) / len ( actual ) optuna_optimizer \u00b6 LGBOptunaOptimizer \u00b6 LGBOptunaOptimizer ( objective , n_class = None ) Bases: BaseOptimizer Fallback/backup Optuna optimizer. Development is focused on Raytune. Kepping this code as backup. Parameters: objective ( str ) \u2013 objective of the model n_class ( int ) \u2013 number of classes in the dataset Source code in inference_model/training/optuna_optimizer.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def __init__ ( self , objective : Literal [ \"binary\" , \"multiclass\" , \"regression\" ], n_class : Optional [ int ] = None , ): \"\"\"Fallback/backup Optuna optimizer. Development is focused on Raytune. Kepping this code as backup. Args: objective (str): objective of the model n_class (int): number of classes in the dataset \"\"\" # Optuna does not support original lighgbm implemenattion and with a workaroud it # is possible to get the binarry classfier with focal_loss(any custom loss) # running but the multiclass requires changes to Optuna code, eg. this post: # https://lightrun.com/answers/optuna-optuna-error-when-using-custom-metrics-in-optunaintegrationlightgbm # noqa # loss = None super ( LGBOptunaOptimizer , self ) . __init__ ( objective , n_class ) self . params = self . base_params optimize \u00b6 optimize ( dtrain , deval ) Optimize LGBM model on provided datasets. Parameters: dtrain ( lgbDataset ) \u2013 training lgb dataset deval ( lgbDataset ) \u2013 evaluation lgb dataset Source code in inference_model/training/optuna_optimizer.py 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 def optimize ( self , dtrain : lgbDataset , deval : lgbDataset ): \"\"\"Optimize LGBM model on provided datasets. Args: dtrain (lgbDataset): training lgb dataset deval (lgbDataset): evaluation lgb dataset \"\"\" dtrain_copy = deepcopy ( dtrain ) deval_copy = deepcopy ( deval ) if deval is not None else None study = create_study ( study_name = \"LightGBMTuner\" ) tuner = LightGBMTuner ( params = self . params , train_set = dtrain_copy , valid_sets = deval_copy , num_boost_round = 1000 , callbacks = [ lgb . early_stopping ( stopping_rounds = 50 )], feval = self . feval , study = study , # fobj=self.fobj, ) tuner . run () self . study = study self . best = tuner . best_params # since n_estimators is not among the params that Optuna optimizes we # need to add it manually. We add a high value since it will be used # with early_stopping_rounds self . best [ \"n_estimators\" ] = 1000 # type: ignore trainer \u00b6 Trainer \u00b6 Trainer ( cat_cols , target_col , id_cols , objective , optimizer = None , n_class = None , preprocessors = None , ) Bases: BaseTrainer Objects that governs training and parameter optimization of the lgbm model. Parameters: cat_cols ( list ) \u2013 list of categorical feature column names target_col ( str ) \u2013 column name that represents target id_cols ( list ) \u2013 identification column names objective ( str ) \u2013 type of the task/objective optimizer ( BaseOptimizer ) \u2013 parameter optimizer object Source code in inference_model/training/trainer.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 def __init__ ( self , cat_cols : List [ str ], target_col : str , id_cols : List [ str ], objective : Literal [ \"binary\" ], optimizer : Union [ Any , BaseOptimizer , None ] = None , n_class : Optional [ int ] = None , preprocessors : Optional [ List [ Union [ Any , PreprocessData ]]] = None , ): \"\"\"Objects that governs training and parameter optimization of the lgbm model. Args: cat_cols (list): list of categorical feature column names target_col (str): column name that represents target id_cols (list): identification column names objective (str): type of the task/objective optimizer (BaseOptimizer): parameter optimizer object \"\"\" super ( Trainer , self ) . __init__ ( cat_cols = cat_cols , target_col = target_col , id_cols = id_cols , objective = objective , n_class = n_class , preprocessors = preprocessors , ) if optimizer is not None : if not hasattr ( optimizer , \"optimize\" ): raise AttributeError ( \" {} optimizer must have {} method\" . format ( optimizer , \"optimize\" ) ) self . optimizer = optimizer train \u00b6 train ( df_train , params = None , df_valid = None ) Train the model with the parameters. Parameters: df_train ( pd . DataFrame ) \u2013 training dataset params ( dict ) \u2013 model paramaters df_valid ( pd . DataFrame ) \u2013 optional validation dataset Returns: model ( lgb . basic . Booster ) \u2013 trained model Source code in inference_model/training/trainer.py 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def train ( self , df_train : pd . DataFrame , params : Optional [ Dict ] = None , df_valid : Optional [ pd . DataFrame ] = None , ): \"\"\"Train the model with the parameters. Args: df_train (pd.DataFrame): training dataset params (dict): model paramaters df_valid (pd.DataFrame): optional validation dataset Returns: model (lgb.basic.Booster): trained model \"\"\" if self . preprocessors : for prep in self . preprocessors : df_train_prep = prep . transform ( df_train . drop ( columns = [ self . target_col ])) df_valid_prep = ( prep . transform ( df_valid . drop ( columns = [ self . target_col ])) if df_valid is not None else None ) if self . objective in [ \"binary\" ]: df_train_prep [ self . target_col ] = df_train [ self . target_col ] . astype ( int ) if df_valid is not None : df_valid_prep [ self . target_col ] = df_valid [ self . target_col ] . astype ( int ) else : raise NotImplementedError else : df_train_prep = df_train . copy () df_valid_prep = df_valid . copy () if df_valid is not None else None cat_cols = self . cat_cols if params : config = params elif ( params is None ) and ( self . optimizer ): config = self . optimizer . best if hasattr ( self . optimizer , \"best_to_drop\" ): df_train_prep . drop ( columns = self . optimizer . best_to_drop , # type: ignore inplace = True , ) if df_valid is not None : df_valid_prep . drop ( columns = self . optimizer . best_to_drop , # type: ignore inplace = True , ) cat_cols = intsec ( self . cat_cols , df_train_prep . columns . values ) elif ( params is None ) and ( self . optimizer is None ): config = self . base_params lgb_train , lgb_valid = to_lgbdataset ( train = df_train_prep , cat_cols = cat_cols , target_col = self . target_col , id_cols = self . id_cols , valid = df_valid_prep , ) self . model = lgb_train_function ( config = config , lgbtrain = lgb_train , lgbeval = lgb_valid , feval = self . feval , ) fit \u00b6 fit ( df_train , df_valid , df_test , random_state = 1 ) Train the model and optimize the parameters. Parameters: df_train ( pd . DataFrame ) \u2013 training dataset df_valid ( pd . DataFrame ) \u2013 validation dataset df_test ( pd . DataFrame ) \u2013 testing dataset Returns: model ( lgb . basic . Booster ) \u2013 trained mdoel Source code in inference_model/training/trainer.py 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 def fit ( self , df_train : pd . DataFrame , df_valid : pd . DataFrame , df_test : pd . DataFrame , random_state : int = 1 , ) -> pd . DataFrame : \"\"\"Train the model and optimize the parameters. Args: df_train (pd.DataFrame): training dataset df_valid (pd.DataFrame): validation dataset df_test (pd.DataFrame): testing dataset Returns: model (lgb.basic.Booster): trained mdoel \"\"\" if self . preprocessors : # this should be # df_train_prep = prep.transform(df_train_prep.drop(columns=[self.target_col])) for prep in self . preprocessors : df_train_prep = prep . transform ( df_train . drop ( columns = [ self . target_col ])) df_valid_prep = prep . transform ( df_valid . drop ( columns = [ self . target_col ])) if self . objective in [ \"binary\" ]: df_train_prep [ self . target_col ] = df_train [ self . target_col ] . astype ( int ) df_valid_prep [ self . target_col ] = df_valid [ self . target_col ] . astype ( int ) else : raise NotImplementedError else : df_train_prep = df_train . copy () df_valid_prep = df_valid . copy () lgb_train , lgb_valid = to_lgbdataset ( train = df_train_prep , cat_cols = self . cat_cols , target_col = self . target_col , id_cols = self . id_cols , valid = df_valid_prep , ) self . optimizer . optimize ( dtrain = lgb_train , deval = lgb_valid ) df_train_valid = pd . concat ([ df_train , df_valid ], ignore_index = True ) df_train_valid = df_train_valid . reset_index ( drop = True ) self . train ( df_train = df_train_valid ) metrics_dict = self . compute_metrics ( df_test ) return metrics_dict utils \u00b6 get_or_create_experiment \u00b6 get_or_create_experiment ( experiment_name ) Retrieve the ID of an existing MLflow experiment or create a new one if it doesn't exist. This function checks if an experiment with the given name exists within MLflow. If it does, the function returns its ID. If not, it creates a new experiment with the provided name and returns its ID. experiment_name (str): Name of the MLflow experiment. str: ID of the existing or newly created MLflow experiment. Source code in inference_model/training/utils.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 def get_or_create_experiment ( experiment_name ): \"\"\" Retrieve the ID of an existing MLflow experiment or create a new one if it doesn't exist. This function checks if an experiment with the given name exists within MLflow. If it does, the function returns its ID. If not, it creates a new experiment with the provided name and returns its ID. Parameters: - experiment_name (str): Name of the MLflow experiment. Returns: - str: ID of the existing or newly created MLflow experiment. \"\"\" if experiment := mlflow . get_experiment_by_name ( experiment_name ): return experiment . experiment_id else : return mlflow . create_experiment ( experiment_name ) to_lgbdataset \u00b6 to_lgbdataset ( train , cat_cols , target_col , id_cols = [], valid = None ) Transform pandas dataframe to lgbm dataset, or datasets(eval). Parameters: train ( pd . DataFrame ) \u2013 training dataset cat_cols ( list ) \u2013 list of categorical columns target_col ( str ) \u2013 target column in the dataset id_cols ( list ) \u2013 list of identifier columns valid ( pd . DataFrame ) \u2013 validation dataset Returns: lgb_train ( lgbDataset ) \u2013 lgbm training dataset lgb_valid ( lgbDataset ) \u2013 lgbm valid dataset Source code in inference_model/training/utils.py 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 def to_lgbdataset ( train : pd . DataFrame , cat_cols : List [ str ], target_col : str , id_cols : List [ str ] = [], valid : Optional [ pd . DataFrame ] = None , ) -> Tuple [ lgbDataset , Optional [ lgbDataset ]]: \"\"\"Transform pandas dataframe to lgbm dataset, or datasets(eval). Args: train (pd.DataFrame): training dataset cat_cols (list): list of categorical columns target_col (str): target column in the dataset id_cols (list): list of identifier columns valid (pd.DataFrame): validation dataset Returns: lgb_train (lgbDataset): lgbm training dataset lgb_valid (lgbDataset): lgbm valid dataset \"\"\" lgb_train = lgbDataset ( train . drop ( columns = [ target_col ] + id_cols ), train [ target_col ], categorical_feature = cat_cols , free_raw_data = False , ) if valid is not None : lgb_valid = lgbDataset ( valid . drop ( columns = [ target_col ] + id_cols ), valid [ target_col ], reference = lgb_train , free_raw_data = False , ) else : lgb_valid = None return lgb_train , lgb_valid predict_proba_lgbm_from_raw \u00b6 predict_proba_lgbm_from_raw ( preds_raw , task , binary2d = False ) Apply softmax to array of arrays that is an output of lightgbm.predct(). This replaces predict_proba(). Parameters: predicted_raw ( ndarray ) \u2013 1D numpy array of arrays task ( str ) \u2013 type of task/objective binary2d ( boolean ) \u2013 wether the output of binary classification should be 1 or 2d vector Returns: predicted_probs ( ndarray ) \u2013 array with predicted probabilities Source code in inference_model/training/utils.py 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 def predict_proba_lgbm_from_raw ( preds_raw : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ], binary2d : Optional [ bool ] = False , ) -> np . ndarray : \"\"\"Apply softmax to array of arrays that is an output of lightgbm.predct(). This replaces predict_proba(). Args: predicted_raw (ndarray): 1D numpy array of arrays task (str): type of task/objective binary2d (boolean): wether the output of binary classification should be 1 or 2d vector Returns: predicted_probs (ndarray): array with predicted probabilities \"\"\" if task == \"binary\" : predicted_probs = _sigmoid ( preds_raw ) if binary2d : predicted_probs = np . apply_along_axis ( _binary_margin_to_prob , 1 , np . vstack ( tuple ( predicted_probs )) ) elif task == \"multiclass\" : predicted_probs = np . apply_along_axis ( _softmax , 1 , np . stack ( tuple ( preds_raw ))) return predicted_probs predict_cls_lgbm_from_raw \u00b6 predict_cls_lgbm_from_raw ( preds_raw , task ) Helper function to convert raw margin predictions through a sigmoid to represent a probability. Parameters: preds_raw ( ndarray ) \u2013 predictions lgbDataset ( lightgbm . Dataset ) \u2013 dataset, containing labels, used for prediction Returns: y_true , preds \u2013 tuple containg labels and predictions for further evaluation Source code in inference_model/training/utils.py 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 def predict_cls_lgbm_from_raw ( preds_raw : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ] ) -> np . ndarray : \"\"\"Helper function to convert raw margin predictions through a sigmoid to represent a probability. Args: preds_raw (ndarray): predictions lgbDataset (lightgbm.Dataset): dataset, containing labels, used for prediction Returns: (y_true, preds): tuple containg labels and predictions for further evaluation \"\"\" predicted_probs = predict_proba_lgbm_from_raw ( preds_raw = preds_raw , task = task ) if task == \"binary\" : pred_cls = np . array ([ int ( p > 0.5 ) for p in predicted_probs ]) elif task == \"multiclass\" : pred_cls = predicted_probs . argmax ( axis = 1 ) return pred_cls get_feature_importance \u00b6 get_feature_importance ( model ) Extract model feature importances and return sorted dataframe. Parameters: model ( lgb . basic . Booster ) \u2013 LightGBM model Returns: feature_imp ( pd . DataFrame ) \u2013 sorted dataframe with features and their importances Source code in inference_model/training/utils.py 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 def get_feature_importance ( model : lgb . basic . Booster ) -> pd . DataFrame : \"\"\"Extract model feature importances and return sorted dataframe. Args: model (lgb.basic.Booster): LightGBM model Returns: feature_imp (pd.DataFrame): sorted dataframe with features and their importances \"\"\" feature_imp = pd . DataFrame ( { \"value\" : model . feature_importance (), \"feature\" : model . feature_name ()} ) feature_imp . sort_values ( by = \"value\" , inplace = True ) feature_imp . reset_index ( drop = True , inplace = True ) return feature_imp flatten_dict \u00b6 flatten_dict ( d , parent_key = '' , sep = '_' ) fastest according to https://www.freecodecamp.org/news/how-to-flatten-a-dictionary-in-python-in-4-different-ways/ Source code in inference_model/training/utils.py 184 185 186 187 188 189 190 191 192 193 194 195 196 197 def flatten_dict ( d : MutableMapping , parent_key : str = \"\" , sep : str = \"_\" ) -> MutableMapping : \"\"\" fastest according to https://www.freecodecamp.org/news/how-to-flatten-a-dictionary-in-python-in-4-different-ways/ \"\"\" items : List [ Any ] = [] for k , v in d . items (): new_key = parent_key + sep + k if parent_key else k if isinstance ( v , MutableMapping ): items . extend ( flatten_dict ( v , new_key , sep = sep ) . items ()) else : items . append (( new_key , v )) return dict ( items )","title":"training"},{"location":"inference_model/training.html#training","text":"","title":"Training"},{"location":"inference_model/training.html#inference_model.training.metrics","text":"","title":"metrics"},{"location":"inference_model/training.html#inference_model.training.optuna_optimizer","text":"","title":"optuna_optimizer"},{"location":"inference_model/training.html#inference_model.training.trainer","text":"","title":"trainer"},{"location":"inference_model/training.html#inference_model.training.utils","text":"","title":"utils"},{"location":"inference_model/utils.html","text":"Utils \u00b6 utils \u00b6 intsec \u00b6 intsec ( list1 , list2 ) Simple intesection of two lists. Parameters: list1 ( list ) \u2013 list1 list2 ( list ) \u2013 list2 Returns: list ( list ) \u2013 intersection of lists Source code in inference_model/utils.py 7 8 9 10 11 12 13 14 15 def intsec ( list1 : list , list2 : list ) -> list : \"\"\"Simple intesection of two lists. Args: list1 (list): list1 list2 (list): list2 Returns: list (list): intersection of lists \"\"\" return list ( set . intersection ( set ( list1 ), set ( list2 ))) dill_load \u00b6 dill_load ( file_loc ) Helper function to open/close dill file, otherwise the python outputs warning that the file remains opened Parameters: file_loc ( str ) \u2013 location of the file Returns: content ( dict ) \u2013 content of dill file, usually dictionary Source code in inference_model/utils.py 18 19 20 21 22 23 24 25 26 27 28 29 def dill_load ( file_loc : Union [ str , Path ]) -> Any : \"\"\"Helper function to open/close dill file, otherwise the python outputs warning that the file remains opened Args: file_loc (str): location of the file Returns: content (dict): content of dill file, usually dictionary \"\"\" with open ( file_loc , \"rb\" ) as f : content = dill . load ( f ) return content dill_dump \u00b6 dill_dump ( file_loc , content ) Helper function to open/close dill file and dump content into it, otherwise the python outputs warning that the file remains opened Parameters: file_loc ( str ) \u2013 location of the file content ( object ) \u2013 data that will be saved to dill, usually dictionary Source code in inference_model/utils.py 32 33 34 35 36 37 38 39 40 41 def dill_dump ( file_loc : Union [ str , Path ], content : object ): \"\"\"Helper function to open/close dill file and dump content into it, otherwise the python outputs warning that the file remains opened Args: file_loc (str): location of the file content (object): data that will be saved to dill, usually dictionary \"\"\" with open ( file_loc , \"wb\" ) as f : dill . dump ( content , f )","title":"utils"},{"location":"inference_model/utils.html#utils","text":"","title":"Utils"},{"location":"inference_model/utils.html#inference_model.utils","text":"","title":"utils"},{"location":"notebooks/01_feature_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. 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In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Feature analysis \u00b6 Purpose : analyse dataset features without inclusion of target value if it looks healthy duplicate values NA values zero/0 values if the values are \"too constant\", ie. most [robably bad features for classifcation/regression small std values entropy is too high/too low(not sure right now?) - \"Entropy is a measure of disorder or uncertainty\" number of unique values in categorical features is too high - most probably they are unable to identify any group of targets drop highly correlated values to improve performance of the model training to decrease dataset size for further exploration Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing , zero , nunique , init_check , std , entropy , nunique , ) pd . set_option ( \"display.max_columns\" , 100 ) pd . set_option ( \"display.max_rows\" , 300 ) from inference_model.eda.features.plotting import cross_correlation , distributions import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing, zero, nunique, init_check, std, entropy, nunique, ) pd.set_option(\"display.max_columns\", 100) pd.set_option(\"display.max_rows\", 300) from inference_model.eda.features.plotting import cross_correlation, distributions import ast Dataset \u00b6 In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None Features \u00b6 Analysis \u00b6 In [4]: Copied! duplicated_ids , cont_cols_desc , cat_cols_desc = init_check ( df = df_pd , identifier = id_cols , cat_cols = cat_cols , cont_cols = cont_cols , verbose = True , ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() duplicated_ids, cont_cols_desc, cat_cols_desc = init_check( df=df_pd, identifier=id_cols, cat_cols=cat_cols, cont_cols=cont_cols, verbose=True, ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() [CHECK] Number of duplicated ids: 1034 [CHECK] Numerical columns count mean std min 25% 50% \\ maf 1035.0 11.00 3.40 0.03 9.77 10.19 vehicle_speed 1035.0 0.95 3.70 0.00 0.00 0.00 engine_load 1035.0 46.29 21.59 30.59 35.69 37.25 barometric_pressure 1035.0 101.00 0.00 101.00 101.00 101.00 intake_air_temp 1035.0 28.56 18.16 -6.00 20.00 26.00 throttle_position 1035.0 83.21 0.15 83.14 83.14 83.14 engine_coolant_temp 1035.0 76.66 21.89 17.00 74.50 88.00 engine_speed 1035.0 929.39 270.78 3.20 893.00 896.50 fuel_rg_pressure 1035.0 35967.15 14377.15 110.00 31220.00 34050.00 control_voltage 1035.0 14.32 2.14 0.05 14.76 14.78 75% max maf 11.99 31.83 vehicle_speed 0.00 20.00 engine_load 38.82 100.00 barometric_pressure 101.00 101.00 intake_air_temp 32.00 145.00 throttle_position 83.14 83.53 engine_coolant_temp 89.00 91.00 engine_speed 899.50 2282.50 fuel_rg_pressure 35390.00 108770.00 control_voltage 14.80 14.84 In [5]: Copied! missing_val_frac , fig = missing ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) missing_val_frac . head () missing_val_frac, fig = missing( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) missing_val_frac.head() Out[5]: engine_load 0.0 engine_coolant_temp 0.0 engine_speed 0.0 vehicle_speed 0.0 intake_air_temp 0.0 dtype: float64 In [6]: Copied! zero_val_frac , fig = zero ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) zero_val_frac . head () zero_val_frac, fig = zero( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) zero_val_frac.head() Out[6]: vehicle_speed 0.927536 class 0.902415 engine_load 0.000000 engine_coolant_temp 0.000000 engine_speed 0.000000 dtype: float64 In [7]: Copied! data_nunique , fig = nunique ( df = df_pd . drop ( columns = id_cols ), scale = \"log\" , plot = True , ) data_nunique . head () data_nunique, fig = nunique( df=df_pd.drop(columns=id_cols), scale=\"log\", plot=True, ) data_nunique.head() Out[7]: barometric_pressure 1 throttle_position 2 class 2 vehicle_speed 21 control_voltage 48 dtype: int64 In [8]: Copied! data_std , fig = std ( df = df_pd [ cont_cols ], scale = \"log\" , plot = True , ) data_std . head () data_std, fig = std( df=df_pd[cont_cols], scale=\"log\", plot=True, ) data_std.head() Out[8]: fuel_rg_pressure 14377.152211 engine_speed 270.781548 engine_coolant_temp 21.888726 engine_load 21.587427 intake_air_temp 18.163539 dtype: float64 In [9]: Copied! col_entropies , fig = entropy ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) col_entropies . head () col_entropies, fig = entropy( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) col_entropies.head() Out[9]: maf 4.800777 engine_speed 3.981783 fuel_rg_pressure 3.797527 intake_air_temp 3.612889 engine_load 3.253640 dtype: float64 Plotting \u00b6 In [10]: Copied! fig = cross_correlation ( df = df_pd [ cont_cols ], n = 10 , verbose = True ) fig = cross_correlation(df=df_pd[cont_cols], n=10, verbose=True) Top 10 absolute correlations vehicle_speed fuel_rg_pressure 0.648543 maf 0.544904 engine_load engine_coolant_temp 0.531108 engine_speed vehicle_speed 0.525832 engine_coolant_temp throttle_position 0.491290 engine_speed maf 0.398281 maf fuel_rg_pressure 0.331396 engine_speed fuel_rg_pressure 0.329811 engine_coolant_temp intake_air_temp 0.305559 engine_load intake_air_temp 0.207530 dtype: float64 In [11]: Copied! fig = distributions ( df = df_pd [ cont_cols ], low_per_cut = 0 , high_per_cut = 1 , type = \"box\" , ) fig = distributions( df=df_pd[cont_cols], low_per_cut=0, high_per_cut=1, type=\"box\", ) /home/jovyan/success6g-edge/inference_model/eda/features/plotting.py:98: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding. ax[i].set_xlim(data[col].quantile(low_per_cut), data[col].quantile(high_per_cut))","title":"01_feature_analysis"},{"location":"notebooks/01_feature_analysis.html#feature-analysis","text":"Purpose : analyse dataset features without inclusion of target value if it looks healthy duplicate values NA values zero/0 values if the values are \"too constant\", ie. most [robably bad features for classifcation/regression small std values entropy is too high/too low(not sure right now?) - \"Entropy is a measure of disorder or uncertainty\" number of unique values in categorical features is too high - most probably they are unable to identify any group of targets drop highly correlated values to improve performance of the model training to decrease dataset size for further exploration","title":"Feature analysis"},{"location":"notebooks/01_feature_analysis.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing , zero , nunique , init_check , std , entropy , nunique , ) pd . set_option ( \"display.max_columns\" , 100 ) pd . set_option ( \"display.max_rows\" , 300 ) from inference_model.eda.features.plotting import cross_correlation , distributions import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing, zero, nunique, init_check, std, entropy, nunique, ) pd.set_option(\"display.max_columns\", 100) pd.set_option(\"display.max_rows\", 300) from inference_model.eda.features.plotting import cross_correlation, distributions import ast","title":"Imports"},{"location":"notebooks/01_feature_analysis.html#dataset","text":"In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None","title":"Dataset"},{"location":"notebooks/01_feature_analysis.html#features","text":"","title":"Features"},{"location":"notebooks/01_feature_analysis.html#analysis","text":"In [4]: Copied! duplicated_ids , cont_cols_desc , cat_cols_desc = init_check ( df = df_pd , identifier = id_cols , cat_cols = cat_cols , cont_cols = cont_cols , verbose = True , ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() duplicated_ids, cont_cols_desc, cat_cols_desc = init_check( df=df_pd, identifier=id_cols, cat_cols=cat_cols, cont_cols=cont_cols, verbose=True, ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() [CHECK] Number of duplicated ids: 1034 [CHECK] Numerical columns count mean std min 25% 50% \\ maf 1035.0 11.00 3.40 0.03 9.77 10.19 vehicle_speed 1035.0 0.95 3.70 0.00 0.00 0.00 engine_load 1035.0 46.29 21.59 30.59 35.69 37.25 barometric_pressure 1035.0 101.00 0.00 101.00 101.00 101.00 intake_air_temp 1035.0 28.56 18.16 -6.00 20.00 26.00 throttle_position 1035.0 83.21 0.15 83.14 83.14 83.14 engine_coolant_temp 1035.0 76.66 21.89 17.00 74.50 88.00 engine_speed 1035.0 929.39 270.78 3.20 893.00 896.50 fuel_rg_pressure 1035.0 35967.15 14377.15 110.00 31220.00 34050.00 control_voltage 1035.0 14.32 2.14 0.05 14.76 14.78 75% max maf 11.99 31.83 vehicle_speed 0.00 20.00 engine_load 38.82 100.00 barometric_pressure 101.00 101.00 intake_air_temp 32.00 145.00 throttle_position 83.14 83.53 engine_coolant_temp 89.00 91.00 engine_speed 899.50 2282.50 fuel_rg_pressure 35390.00 108770.00 control_voltage 14.80 14.84 In [5]: Copied! missing_val_frac , fig = missing ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) missing_val_frac . head () missing_val_frac, fig = missing( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) missing_val_frac.head() Out[5]: engine_load 0.0 engine_coolant_temp 0.0 engine_speed 0.0 vehicle_speed 0.0 intake_air_temp 0.0 dtype: float64 In [6]: Copied! zero_val_frac , fig = zero ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) zero_val_frac . head () zero_val_frac, fig = zero( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) zero_val_frac.head() Out[6]: vehicle_speed 0.927536 class 0.902415 engine_load 0.000000 engine_coolant_temp 0.000000 engine_speed 0.000000 dtype: float64 In [7]: Copied! data_nunique , fig = nunique ( df = df_pd . drop ( columns = id_cols ), scale = \"log\" , plot = True , ) data_nunique . head () data_nunique, fig = nunique( df=df_pd.drop(columns=id_cols), scale=\"log\", plot=True, ) data_nunique.head() Out[7]: barometric_pressure 1 throttle_position 2 class 2 vehicle_speed 21 control_voltage 48 dtype: int64 In [8]: Copied! data_std , fig = std ( df = df_pd [ cont_cols ], scale = \"log\" , plot = True , ) data_std . head () data_std, fig = std( df=df_pd[cont_cols], scale=\"log\", plot=True, ) data_std.head() Out[8]: fuel_rg_pressure 14377.152211 engine_speed 270.781548 engine_coolant_temp 21.888726 engine_load 21.587427 intake_air_temp 18.163539 dtype: float64 In [9]: Copied! col_entropies , fig = entropy ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) col_entropies . head () col_entropies, fig = entropy( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) col_entropies.head() Out[9]: maf 4.800777 engine_speed 3.981783 fuel_rg_pressure 3.797527 intake_air_temp 3.612889 engine_load 3.253640 dtype: float64","title":"Analysis"},{"location":"notebooks/01_feature_analysis.html#plotting","text":"In [10]: Copied! fig = cross_correlation ( df = df_pd [ cont_cols ], n = 10 , verbose = True ) fig = cross_correlation(df=df_pd[cont_cols], n=10, verbose=True) Top 10 absolute correlations vehicle_speed fuel_rg_pressure 0.648543 maf 0.544904 engine_load engine_coolant_temp 0.531108 engine_speed vehicle_speed 0.525832 engine_coolant_temp throttle_position 0.491290 engine_speed maf 0.398281 maf fuel_rg_pressure 0.331396 engine_speed fuel_rg_pressure 0.329811 engine_coolant_temp intake_air_temp 0.305559 engine_load intake_air_temp 0.207530 dtype: float64 In [11]: Copied! fig = distributions ( df = df_pd [ cont_cols ], low_per_cut = 0 , high_per_cut = 1 , type = \"box\" , ) fig = distributions( df=df_pd[cont_cols], low_per_cut=0, high_per_cut=1, type=\"box\", ) /home/jovyan/success6g-edge/inference_model/eda/features/plotting.py:98: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding. ax[i].set_xlim(data[col].quantile(low_per_cut), data[col].quantile(high_per_cut))","title":"Plotting"},{"location":"notebooks/02_target_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? 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var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Target analysis \u00b6 Purpose : put features into context with predicted or actual target values analyse feature distribution per group in binary classification check correlation of features with target value check 'certainty' of the model predictions by plotting prob_distrib_per_class to see how certain the model is in predicting each class visually check predicted vs actual(ground truth values) by simple scatter plot by 'improved scatter plot' ( joint_dist ) - this can be usefull if the actual values form a cluster (eg. regression applied to clusters of users) and we want check if the model predictions also form a nice cluster with similar distribution as actual values Intended use : check how your model is performing check the possible connection between feature and target values (eg. if the distributions of features are different for each class) Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls , ) import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls, ) import ast Dataset \u00b6 In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None Analysis \u00b6 In [4]: Copied! sorted_corr_cols , fig = correlation ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], scale = \"linear\" , plot = True , ) sorted_corr_cols . head () sorted_corr_cols, fig = correlation( df=df_pd[cont_cols], target=df_pd[target_col], scale=\"linear\", plot=True, ) sorted_corr_cols.head() Out[4]: engine_load 0.818614 throttle_position 0.245668 maf 0.087101 control_voltage 0.042763 engine_speed -0.062052 Name: class, dtype: float64 Plotting \u00b6 Predicted and actual/ground_truth values were gathered using LightGBM with Optuna optimizer on the dataset In [5]: Copied! fig = distributions_in_binary_cls ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], low_per_cut = 0 , high_per_cut = 1 , ) fig = distributions_in_binary_cls( df=df_pd[cont_cols], target=df_pd[target_col], low_per_cut=0, high_per_cut=1, ) /home/jovyan/success6g-edge/inference_model/eda/target/plotting.py:123: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding. ax[i].set_xlim(","title":"02_target_analysis"},{"location":"notebooks/02_target_analysis.html#target-analysis","text":"Purpose : put features into context with predicted or actual target values analyse feature distribution per group in binary classification check correlation of features with target value check 'certainty' of the model predictions by plotting prob_distrib_per_class to see how certain the model is in predicting each class visually check predicted vs actual(ground truth values) by simple scatter plot by 'improved scatter plot' ( joint_dist ) - this can be usefull if the actual values form a cluster (eg. regression applied to clusters of users) and we want check if the model predictions also form a nice cluster with similar distribution as actual values Intended use : check how your model is performing check the possible connection between feature and target values (eg. if the distributions of features are different for each class)","title":"Target analysis"},{"location":"notebooks/02_target_analysis.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls , ) import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls, ) import ast","title":"Imports"},{"location":"notebooks/02_target_analysis.html#dataset","text":"In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None","title":"Dataset"},{"location":"notebooks/02_target_analysis.html#analysis","text":"In [4]: Copied! sorted_corr_cols , fig = correlation ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], scale = \"linear\" , plot = True , ) sorted_corr_cols . head () sorted_corr_cols, fig = correlation( df=df_pd[cont_cols], target=df_pd[target_col], scale=\"linear\", plot=True, ) sorted_corr_cols.head() Out[4]: engine_load 0.818614 throttle_position 0.245668 maf 0.087101 control_voltage 0.042763 engine_speed -0.062052 Name: class, dtype: float64","title":"Analysis"},{"location":"notebooks/02_target_analysis.html#plotting","text":"Predicted and actual/ground_truth values were gathered using LightGBM with Optuna optimizer on the dataset In [5]: Copied! fig = distributions_in_binary_cls ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], low_per_cut = 0 , high_per_cut = 1 , ) fig = distributions_in_binary_cls( df=df_pd[cont_cols], target=df_pd[target_col], low_per_cut=0, high_per_cut=1, ) /home/jovyan/success6g-edge/inference_model/eda/target/plotting.py:123: UserWarning: Attempting to set identical low and high xlims makes transformation singular; 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Models \u00b6 Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [2]: Copied! import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report , RocCurveDisplay , roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw , predict_proba_lgbm_from_raw , ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, RocCurveDisplay, roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw, predict_proba_lgbm_from_raw, ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() Dataset \u00b6 In [3]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True In [4]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [8]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) check possible class imbalance In [9]: Copied! df_pd [ target_col ] . value_counts () df_pd[target_col].value_counts() Out[9]: class 0 934 1 101 Name: count, dtype: int64 Preprocessing \u00b6 divide dataset into train, test, valid scale continuous columns by standard scaler(not needed for LightGBM but for other mdoels...) In [10]: Copied! df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) df_train . reset_index ( inplace = True , drop = True ) df_valid . reset_index ( inplace = True , drop = True ) df_test . reset_index ( inplace = True , drop = True ) scaler_mapper_def = { \"cont_cols\" : StandardScaler , \"cat_cols\" : None , \"id_cols\" : None , } scaler = scaler_mapper ( cont_cols = cont_cols , cat_cols = cat_cols , id_cols = [ target_col ] + id_cols , scaler_mapper_def = scaler_mapper_def , ) df_train_scaled = scaler . fit_transform ( df_train ) df_test_scaled = scaler . transform ( df_test ) df_valid_scaled = scaler . transform ( df_valid ) df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) df_train.reset_index(inplace=True, drop=True) df_valid.reset_index(inplace=True, drop=True) df_test.reset_index(inplace=True, drop=True) scaler_mapper_def = { \"cont_cols\": StandardScaler, \"cat_cols\": None, \"id_cols\": None, } scaler = scaler_mapper( cont_cols=cont_cols, cat_cols=cat_cols, id_cols=[target_col] + id_cols, scaler_mapper_def=scaler_mapper_def, ) df_train_scaled = scaler.fit_transform(df_train) df_test_scaled = scaler.transform(df_test) df_valid_scaled = scaler.transform(df_valid) Logistic Regression \u00b6 In [11]: Copied! LR_clf = LogisticRegression ( class_weight = \"balanced\" ) LR_clf . fit ( df_train_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ), df_train_scaled [ target_col ], ) LR_predicted = LR_clf . predict ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) print ( \"LR classification report : \\n \" + str ( classification_report ( df_test_scaled [ target_col ], LR_predicted )) ) LR_clf = LogisticRegression(class_weight=\"balanced\") LR_clf.fit( df_train_scaled.drop(columns=[target_col] + id_cols + cat_cols), df_train_scaled[target_col], ) LR_predicted = LR_clf.predict( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) print( \"LR classification report :\\n\" + str(classification_report(df_test_scaled[target_col], LR_predicted)) ) LR classification report : precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [12]: Copied! # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np . arange ( 0.2 , 0.8 , 0.01 ): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) )[:, 1 ] >= t ) . astype ( int ) threshold_score . append ( ( t , criterion ( df_test_scaled [ target_col ], preds_bin , average = \"weighted\" )) ) threshold_score = sorted ( threshold_score , key = lambda x : x [ 1 ], reverse = True ) best_threshold , best_score = threshold_score [ 0 ][ 0 ], threshold_score [ 0 ][ 1 ] print ( f \"The best threshold \\n { best_threshold } \\n , with score: \\n { best_score } \" ) # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np.arange(0.2, 0.8, 0.01): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) )[:, 1] >= t ).astype(int) threshold_score.append( (t, criterion(df_test_scaled[target_col], preds_bin, average=\"weighted\")) ) threshold_score = sorted(threshold_score, key=lambda x: x[1], reverse=True) best_threshold, best_score = threshold_score[0][0], threshold_score[0][1] print(f\"The best threshold\\n{best_threshold}\\n, with score:\\n{best_score}\") The best threshold 0.2 , with score: 1.0 In [13]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) In [14]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0 H2O AutoML \u00b6 NOTE : java is required : sudo apt install openjdk-17-jdk In [16]: Copied! # initialize H2O h2o . init ( log_dir = \"h2o_logs\" , log_level = \"WARN\" ) # read as h2o file h2o_train = h2o . H2OFrame ( df_train_scaled . drop ( columns = id_cols )) h2o_valid = h2o . H2OFrame ( df_valid_scaled . drop ( columns = id_cols )) h2o_test = h2o . H2OFrame ( df_test_scaled . drop ( columns = id_cols )) # For binary classification, response should be a factor h2o_train [ target_col ] = h2o_train [ target_col ] . asfactor () h2o_valid [ target_col ] = h2o_valid [ target_col ] . asfactor () h2o_test [ target_col ] = h2o_test [ target_col ] . asfactor () # Define AML task aml = H2OAutoML ( seed = random_state , max_runtime_secs = 180 ) # over/under sample for classification tasks aml . balance_classes = True # Run it _ = aml . train ( x = list ( cont_cols + cat_cols ), y = target_col , training_frame = h2o_train , leaderboard_frame = h2o_valid , ) m = aml . get_best_model () # Leaderboard, show and save lb = h2o . automl . get_leaderboard ( aml , extra_columns = \"ALL\" ) print ( lb ) print ( m . model_performance ( h2o_valid )) predictions = m . predict ( h2o_test ) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") # initialize H2O h2o.init(log_dir=\"h2o_logs\", log_level=\"WARN\") # read as h2o file h2o_train = h2o.H2OFrame(df_train_scaled.drop(columns=id_cols)) h2o_valid = h2o.H2OFrame(df_valid_scaled.drop(columns=id_cols)) h2o_test = h2o.H2OFrame(df_test_scaled.drop(columns=id_cols)) # For binary classification, response should be a factor h2o_train[target_col] = h2o_train[target_col].asfactor() h2o_valid[target_col] = h2o_valid[target_col].asfactor() h2o_test[target_col] = h2o_test[target_col].asfactor() # Define AML task aml = H2OAutoML(seed=random_state, max_runtime_secs=180) # over/under sample for classification tasks aml.balance_classes = True # Run it _ = aml.train( x=list(cont_cols + cat_cols), y=target_col, training_frame=h2o_train, leaderboard_frame=h2o_valid, ) m = aml.get_best_model() # Leaderboard, show and save lb = h2o.automl.get_leaderboard(aml, extra_columns=\"ALL\") print(lb) print(m.model_performance(h2o_valid)) predictions = m.predict(h2o_test) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") Checking whether there is an H2O instance running at http://localhost:54321..... not found. Attempting to start a local H2O server... Java Version: openjdk version \"17.0.10\" 2024-01-16; OpenJDK Runtime Environment (build 17.0.10+7-Ubuntu-122.04.1); OpenJDK 64-Bit Server VM (build 17.0.10+7-Ubuntu-122.04.1, mixed mode, sharing) Starting server from /opt/conda/lib/python3.10/site-packages/h2o/backend/bin/h2o.jar Ice root: /tmp/tmp0jm6weez JVM stdout: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.out JVM stderr: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.err Server is running at http://127.0.0.1:54321 Connecting to H2O server at http://127.0.0.1:54321 ... successful. #h2o-table-1.h2o-container { overflow-x: auto; } #h2o-table-1 .h2o-table { /* width: 100%; */ margin-top: 1em; margin-bottom: 1em; } #h2o-table-1 .h2o-table caption { white-space: nowrap; caption-side: top; text-align: left; /* margin-left: 1em; */ margin: 0; font-size: larger; } #h2o-table-1 .h2o-table thead { white-space: nowrap; position: sticky; top: 0; box-shadow: 0 -1px inset; } #h2o-table-1 .h2o-table tbody { overflow: auto; } #h2o-table-1 .h2o-table th, #h2o-table-1 .h2o-table td { text-align: right; /* border: 1px solid; */ } #h2o-table-1 .h2o-table tr:nth-child(even) { /* background: #F5F5F5 */ } H2O_cluster_uptime: 01 secs H2O_cluster_timezone: Etc/UTC H2O_data_parsing_timezone: UTC H2O_cluster_version: 3.46.0.2 H2O_cluster_version_age: 8 days H2O_cluster_name: H2O_from_python_unknownUser_maoqtf H2O_cluster_total_nodes: 1 H2O_cluster_free_memory: 16 Gb H2O_cluster_total_cores: 12 H2O_cluster_allowed_cores: 12 H2O_cluster_status: locked, healthy H2O_connection_url: http://127.0.0.1:54321 H2O_connection_proxy: {\"http\": null, \"https\": null} H2O_internal_security: False Python_version: 3.10.11 final Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% AutoML progress: | 13:16:41.801: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:44.939: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:46.154: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:16:59.224: _train param, Dropping unused columns: [barometric_pressure] 13:16:59.913: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:03.240: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:05.128: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:16.407: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588 13:17:27.855: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:17:40.196: _train param, Dropping unused columns: [barometric_pressure] 13:17:40.764: _train param, Dropping unused columns: [barometric_pressure] 13:17:41.338: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:44.575: _train param, Dropping bad and constant columns: [barometric_pressure] 13:17:45.413: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:53.937: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:55.712: _train param, Dropping unused columns: [barometric_pressure] 13:17:56.367: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 13:19:35.448: _train param, Dropping unused columns: [barometric_pressure] \u2588 13:19:36.22: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588| (done) 100% model_id auc logloss aucpr mean_per_class_error rmse mse training_time_ms predict_time_per_row_ms algo DeepLearning_grid_1_AutoML_1_20240522_131641_model_8 1 0.0177159 1 0 0.0582198 0.00338955 628 0.127786 DeepLearning DeepLearning_grid_1_AutoML_1_20240522_131641_model_4 1 0.0155053 1 0 0.0481989 0.00232314 117 0.1101 DeepLearning XGBoost_1_AutoML_1_20240522_131641 1 0.0347672 1 0 0.0789026 0.00622562 464 0.078661 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_12 1 0.0651884 1 0 0.106454 0.0113325 328 0.072441 XGBoost DeepLearning_grid_1_AutoML_1_20240522_131641_model_1 1 0.0235103 1 0 0.0600679 0.00360815 99 0.098096 DeepLearning XGBoost_grid_1_AutoML_1_20240522_131641_model_11 1 0.00556141 1 0 0.0320985 0.00103031 996 0.105708 XGBoost GBM_4_AutoML_1_20240522_131641 1 9.26741e-06 1 0 9.39779e-05 8.83184e-09 4293 0.280377 GBM XGBoost_grid_1_AutoML_1_20240522_131641_model_6 1 0.0141646 1 0 0.0244465 0.000597631 630 0.057813 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_13 1 0.0318374 1 0 0.0810283 0.00656559 745 0.069021 XGBoost GBM_3_AutoML_1_20240522_131641 1 2.55592e-05 1 0 0.000259039 6.7101e-08 4037 0.153118 GBM [47 rows x 10 columns] ModelMetricsBinomial: deeplearning ** Reported on test data. ** MSE: 0.0033895466349161047 RMSE: 0.05821981307867713 LogLoss: 0.017715874610611436 Mean Per-Class Error: 0.0 AUC: 1.0 AUCPR: 1.0 Gini: 1.0 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.9979014979275557 0 1 Error Rate ----- --- --- ------- ----------- 0 93 0 0 (0.0/93.0) 1 0 10 0 (0.0/10.0) Total 93 10 0 (0.0/103.0) Maximum Metrics: Maximum metrics at their respective thresholds metric threshold value idx --------------------------- ----------- ------- ----- max f1 0.997901 1 9 max f2 0.997901 1 9 max f0point5 0.997901 1 9 max accuracy 0.997901 1 9 max precision 0.999961 1 0 max recall 0.997901 1 9 max specificity 0.999961 1 0 max absolute_mcc 0.997901 1 9 max min_per_class_accuracy 0.997901 1 9 max mean_per_class_accuracy 0.997901 1 9 max tns 0.999961 93 0 max fns 0.999961 9 0 max fps 3.49717e-09 93 102 max tps 0.997901 10 9 max tnr 0.999961 1 0 max fnr 0.999961 0.9 0 max fpr 3.49717e-09 1 102 max tpr 0.997901 1 9 Gains/Lift Table: Avg response rate: 9.71 %, avg score: 11.26 % group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain kolmogorov_smirnov ------- -------------------------- ----------------- ------ ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------ ----------------- -------------------- 1 0.0194175 0.999958 10.3 10.3 1 0.999959 1 0.999959 0.2 0.2 930 930 0.2 2 0.0291262 0.999947 10.3 10.3 1 0.999947 1 0.999955 0.1 0.3 930 930 0.3 3 0.038835 0.999937 10.3 10.3 1 0.999938 1 0.999951 0.1 0.4 930 930 0.4 4 0.0485437 0.999927 10.3 10.3 1 0.99993 1 0.999947 0.1 0.5 930 930 0.5 5 0.0582524 0.999888 10.3 10.3 1 0.999889 1 0.999937 0.1 0.6 930 930 0.6 6 0.106796 0.372647 8.24 9.36364 0.8 0.879862 0.909091 0.945358 0.4 1 724 836.364 0.989247 7 0.15534 0.10825 0 6.4375 0 0.168191 0.625 0.702493 0 1 -100 543.75 0.935484 8 0.203883 0.00316095 0 4.90476 0 0.0682301 0.47619 0.551478 0 1 -100 390.476 0.88172 9 0.300971 0.000343083 0 3.32258 0 0.00100323 0.322581 0.373906 0 1 -100 232.258 0.774194 10 0.398058 0.000119786 0 2.5122 0 0.000202804 0.243902 0.282759 0 1 -100 151.22 0.666667 11 0.504854 8.52118e-05 0 1.98077 0 0.000102754 0.192308 0.222966 0 1 -100 98.0769 0.548387 12 0.601942 6.24023e-05 0 1.66129 0 7.12997e-05 0.16129 0.187015 0 1 -100 66.129 0.44086 13 0.699029 4.02512e-05 0 1.43056 0 5.12346e-05 0.138889 0.161048 0 1 -100 43.0556 0.333333 14 0.796117 2.31994e-05 0 1.2561 0 3.1514e-05 0.121951 0.141412 0 1 -100 25.6098 0.225806 15 0.893204 1.36306e-05 0 1.11957 0 1.91168e-05 0.108696 0.126043 0 1 -100 11.9565 0.11828 16 1 3.49717e-09 0 1 0 5.41845e-06 0.0970874 0.112583 0 1 -100 0 0 deeplearning prediction progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% In [17]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = h2o . as_list ( predictions . drop ([ \"predict\" ])) . values micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = h2o.as_list(predictions.drop([\"predict\"])).values micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" In [18]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0 In [19]: Copied! fig , ax = plt . subplots ( figsize = ( 6 , 6 )) n_classes = 2 for class_id in range ( n_classes ): RocCurveDisplay . from_predictions ( y_onehot_test [:, class_id ], y_score [:, class_id ], name = f \"ROC curve for class { class_id } \" , ax = ax , # plot_chance_level=(class_id == 2), ) fig, ax = plt.subplots(figsize=(6, 6)) n_classes = 2 for class_id in range(n_classes): RocCurveDisplay.from_predictions( y_onehot_test[:, class_id], y_score[:, class_id], name=f\"ROC curve for class {class_id}\", ax=ax, # plot_chance_level=(class_id == 2), ) In [20]: Copied! print ( \"Classification report: \\n {} \" . format ( classification_report ( df_test [ target_col ], h2o . as_list ( predictions [ \"predict\" ])) ) ) print( \"Classification report:\\n{}\".format( classification_report(df_test[target_col], h2o.as_list(predictions[\"predict\"])) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" LightGBM \u00b6 In [21]: Copied! config = {} config [ \"objective\" ] = \"binary\" df_train_scaled_enc = df_train_scaled . copy () df_valid_scaled_enc = df_valid_scaled . copy () df_test_scaled_enc = df_test_scaled . copy () lgb_cat_cols = cat_cols label_encoder = LabelEncoder ( lgb_cat_cols ) df_train_scaled_enc = label_encoder . fit_transform ( df_train_scaled_enc ) df_valid_scaled_enc = label_encoder . transform ( df_valid_scaled_enc ) df_test_scaled_enc = label_encoder . transform ( df_test_scaled_enc ) lgbtrain = lgbm . Dataset ( df_train_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_train_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbvalid = lgbm . Dataset ( df_valid_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_valid_scaled_enc [ target_col ], reference = lgbtrain , free_raw_data = False , ) # Final TRAIN/TEST ftrain = pd . concat ([ df_train_scaled_enc , df_valid_scaled_enc ]) . reset_index ( drop = True ) flgbtrain = lgbm . Dataset ( ftrain . drop ( columns = [ target_col ] + id_cols ), ftrain [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbtest = lgbm . Dataset ( df_test_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_test_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , reference = flgbtrain , free_raw_data = False , ) config = {} config[\"objective\"] = \"binary\" df_train_scaled_enc = df_train_scaled.copy() df_valid_scaled_enc = df_valid_scaled.copy() df_test_scaled_enc = df_test_scaled.copy() lgb_cat_cols = cat_cols label_encoder = LabelEncoder(lgb_cat_cols) df_train_scaled_enc = label_encoder.fit_transform(df_train_scaled_enc) df_valid_scaled_enc = label_encoder.transform(df_valid_scaled_enc) df_test_scaled_enc = label_encoder.transform(df_test_scaled_enc) lgbtrain = lgbm.Dataset( df_train_scaled_enc.drop(columns=[target_col] + id_cols), df_train_scaled_enc[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbvalid = lgbm.Dataset( df_valid_scaled_enc.drop(columns=[target_col] + id_cols), df_valid_scaled_enc[target_col], reference=lgbtrain, free_raw_data=False, ) # Final TRAIN/TEST ftrain = pd.concat([df_train_scaled_enc, df_valid_scaled_enc]).reset_index(drop=True) flgbtrain = lgbm.Dataset( ftrain.drop(columns=[target_col] + id_cols), ftrain[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbtest = lgbm.Dataset( df_test_scaled_enc.drop(columns=[target_col] + id_cols), df_test_scaled_enc[target_col], categorical_feature=lgb_cat_cols, reference=flgbtrain, free_raw_data=False, ) In [22]: Copied! model = lgbm . train ( config , lgbtrain , valid_sets = [ lgbvalid ], valid_names = [ \"\" ], feval = None , ) model = lgbm.train( config, lgbtrain, valid_sets=[lgbvalid], valid_names=[\"\"], feval=None, ) [LightGBM] [Info] Number of positive: 81, number of negative: 747 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002491 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 467 [LightGBM] [Info] Number of data points in the train set: 828, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097826 -> initscore=-2.221616 [LightGBM] [Info] Start training from score -2.221616 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf In [23]: Copied! res = predict_cls_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data , raw_score = True ), task = \"binary\" , ) result = pd . DataFrame ( { \"predicted\" : res , \"ground_truth\" : df_test [ target_col ] . values , } ) print ( \"Classification report: \\n {} \" . format ( classification_report ( result [ \"ground_truth\" ], result [ \"predicted\" ]) ) ) res = predict_cls_lgbm_from_raw( preds_raw=model.predict(lgbtest.data, raw_score=True), task=\"binary\", ) result = pd.DataFrame( { \"predicted\": res, \"ground_truth\": df_test[target_col].values, } ) print( \"Classification report:\\n{}\".format( classification_report(result[\"ground_truth\"], result[\"predicted\"]) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [24]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = predict_proba_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data ), task = \"binary\" , binary2d = True , ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = predict_proba_lgbm_from_raw( preds_raw=model.predict(lgbtest.data), task=\"binary\", binary2d=True, ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0","title":"03_models"},{"location":"notebooks/03_models.html#models","text":"","title":"Models"},{"location":"notebooks/03_models.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [2]: Copied! import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report , RocCurveDisplay , roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw , predict_proba_lgbm_from_raw , ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, RocCurveDisplay, roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw, predict_proba_lgbm_from_raw, ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start()","title":"Imports"},{"location":"notebooks/03_models.html#dataset","text":"In [3]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True In [4]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [8]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) check possible class imbalance In [9]: Copied! df_pd [ target_col ] . value_counts () df_pd[target_col].value_counts() Out[9]: class 0 934 1 101 Name: count, dtype: int64","title":"Dataset"},{"location":"notebooks/03_models.html#preprocessing","text":"divide dataset into train, test, valid scale continuous columns by standard scaler(not needed for LightGBM but for other mdoels...) In [10]: Copied! df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) df_train . reset_index ( inplace = True , drop = True ) df_valid . reset_index ( inplace = True , drop = True ) df_test . reset_index ( inplace = True , drop = True ) scaler_mapper_def = { \"cont_cols\" : StandardScaler , \"cat_cols\" : None , \"id_cols\" : None , } scaler = scaler_mapper ( cont_cols = cont_cols , cat_cols = cat_cols , id_cols = [ target_col ] + id_cols , scaler_mapper_def = scaler_mapper_def , ) df_train_scaled = scaler . fit_transform ( df_train ) df_test_scaled = scaler . transform ( df_test ) df_valid_scaled = scaler . transform ( df_valid ) df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) df_train.reset_index(inplace=True, drop=True) df_valid.reset_index(inplace=True, drop=True) df_test.reset_index(inplace=True, drop=True) scaler_mapper_def = { \"cont_cols\": StandardScaler, \"cat_cols\": None, \"id_cols\": None, } scaler = scaler_mapper( cont_cols=cont_cols, cat_cols=cat_cols, id_cols=[target_col] + id_cols, scaler_mapper_def=scaler_mapper_def, ) df_train_scaled = scaler.fit_transform(df_train) df_test_scaled = scaler.transform(df_test) df_valid_scaled = scaler.transform(df_valid)","title":"Preprocessing"},{"location":"notebooks/03_models.html#logistic-regression","text":"In [11]: Copied! LR_clf = LogisticRegression ( class_weight = \"balanced\" ) LR_clf . fit ( df_train_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ), df_train_scaled [ target_col ], ) LR_predicted = LR_clf . predict ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) print ( \"LR classification report : \\n \" + str ( classification_report ( df_test_scaled [ target_col ], LR_predicted )) ) LR_clf = LogisticRegression(class_weight=\"balanced\") LR_clf.fit( df_train_scaled.drop(columns=[target_col] + id_cols + cat_cols), df_train_scaled[target_col], ) LR_predicted = LR_clf.predict( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) print( \"LR classification report :\\n\" + str(classification_report(df_test_scaled[target_col], LR_predicted)) ) LR classification report : precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [12]: Copied! # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np . arange ( 0.2 , 0.8 , 0.01 ): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) )[:, 1 ] >= t ) . astype ( int ) threshold_score . append ( ( t , criterion ( df_test_scaled [ target_col ], preds_bin , average = \"weighted\" )) ) threshold_score = sorted ( threshold_score , key = lambda x : x [ 1 ], reverse = True ) best_threshold , best_score = threshold_score [ 0 ][ 0 ], threshold_score [ 0 ][ 1 ] print ( f \"The best threshold \\n { best_threshold } \\n , with score: \\n { best_score } \" ) # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np.arange(0.2, 0.8, 0.01): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) )[:, 1] >= t ).astype(int) threshold_score.append( (t, criterion(df_test_scaled[target_col], preds_bin, average=\"weighted\")) ) threshold_score = sorted(threshold_score, key=lambda x: x[1], reverse=True) best_threshold, best_score = threshold_score[0][0], threshold_score[0][1] print(f\"The best threshold\\n{best_threshold}\\n, with score:\\n{best_score}\") The best threshold 0.2 , with score: 1.0 In [13]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) In [14]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0","title":"Logistic Regression"},{"location":"notebooks/03_models.html#h2o-automl","text":"NOTE : java is required : sudo apt install openjdk-17-jdk In [16]: Copied! # initialize H2O h2o . init ( log_dir = \"h2o_logs\" , log_level = \"WARN\" ) # read as h2o file h2o_train = h2o . H2OFrame ( df_train_scaled . drop ( columns = id_cols )) h2o_valid = h2o . H2OFrame ( df_valid_scaled . drop ( columns = id_cols )) h2o_test = h2o . H2OFrame ( df_test_scaled . drop ( columns = id_cols )) # For binary classification, response should be a factor h2o_train [ target_col ] = h2o_train [ target_col ] . asfactor () h2o_valid [ target_col ] = h2o_valid [ target_col ] . asfactor () h2o_test [ target_col ] = h2o_test [ target_col ] . asfactor () # Define AML task aml = H2OAutoML ( seed = random_state , max_runtime_secs = 180 ) # over/under sample for classification tasks aml . balance_classes = True # Run it _ = aml . train ( x = list ( cont_cols + cat_cols ), y = target_col , training_frame = h2o_train , leaderboard_frame = h2o_valid , ) m = aml . get_best_model () # Leaderboard, show and save lb = h2o . automl . get_leaderboard ( aml , extra_columns = \"ALL\" ) print ( lb ) print ( m . model_performance ( h2o_valid )) predictions = m . predict ( h2o_test ) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") # initialize H2O h2o.init(log_dir=\"h2o_logs\", log_level=\"WARN\") # read as h2o file h2o_train = h2o.H2OFrame(df_train_scaled.drop(columns=id_cols)) h2o_valid = h2o.H2OFrame(df_valid_scaled.drop(columns=id_cols)) h2o_test = h2o.H2OFrame(df_test_scaled.drop(columns=id_cols)) # For binary classification, response should be a factor h2o_train[target_col] = h2o_train[target_col].asfactor() h2o_valid[target_col] = h2o_valid[target_col].asfactor() h2o_test[target_col] = h2o_test[target_col].asfactor() # Define AML task aml = H2OAutoML(seed=random_state, max_runtime_secs=180) # over/under sample for classification tasks aml.balance_classes = True # Run it _ = aml.train( x=list(cont_cols + cat_cols), y=target_col, training_frame=h2o_train, leaderboard_frame=h2o_valid, ) m = aml.get_best_model() # Leaderboard, show and save lb = h2o.automl.get_leaderboard(aml, extra_columns=\"ALL\") print(lb) print(m.model_performance(h2o_valid)) predictions = m.predict(h2o_test) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") Checking whether there is an H2O instance running at http://localhost:54321..... not found. Attempting to start a local H2O server... Java Version: openjdk version \"17.0.10\" 2024-01-16; OpenJDK Runtime Environment (build 17.0.10+7-Ubuntu-122.04.1); OpenJDK 64-Bit Server VM (build 17.0.10+7-Ubuntu-122.04.1, mixed mode, sharing) Starting server from /opt/conda/lib/python3.10/site-packages/h2o/backend/bin/h2o.jar Ice root: /tmp/tmp0jm6weez JVM stdout: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.out JVM stderr: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.err Server is running at http://127.0.0.1:54321 Connecting to H2O server at http://127.0.0.1:54321 ... successful. #h2o-table-1.h2o-container { overflow-x: auto; } #h2o-table-1 .h2o-table { /* width: 100%; */ margin-top: 1em; margin-bottom: 1em; } #h2o-table-1 .h2o-table caption { white-space: nowrap; caption-side: top; text-align: left; /* margin-left: 1em; */ margin: 0; font-size: larger; } #h2o-table-1 .h2o-table thead { white-space: nowrap; position: sticky; top: 0; box-shadow: 0 -1px inset; } #h2o-table-1 .h2o-table tbody { overflow: auto; } #h2o-table-1 .h2o-table th, #h2o-table-1 .h2o-table td { text-align: right; /* border: 1px solid; */ } #h2o-table-1 .h2o-table tr:nth-child(even) { /* background: #F5F5F5 */ } H2O_cluster_uptime: 01 secs H2O_cluster_timezone: Etc/UTC H2O_data_parsing_timezone: UTC H2O_cluster_version: 3.46.0.2 H2O_cluster_version_age: 8 days H2O_cluster_name: H2O_from_python_unknownUser_maoqtf H2O_cluster_total_nodes: 1 H2O_cluster_free_memory: 16 Gb H2O_cluster_total_cores: 12 H2O_cluster_allowed_cores: 12 H2O_cluster_status: locked, healthy H2O_connection_url: http://127.0.0.1:54321 H2O_connection_proxy: {\"http\": null, \"https\": null} H2O_internal_security: False Python_version: 3.10.11 final Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% AutoML progress: | 13:16:41.801: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:44.939: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:46.154: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:16:59.224: _train param, Dropping unused columns: [barometric_pressure] 13:16:59.913: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:03.240: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:05.128: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:16.407: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588 13:17:27.855: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:17:40.196: _train param, Dropping unused columns: [barometric_pressure] 13:17:40.764: _train param, Dropping unused columns: [barometric_pressure] 13:17:41.338: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:44.575: _train param, Dropping bad and constant columns: [barometric_pressure] 13:17:45.413: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:53.937: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:55.712: _train param, Dropping unused columns: [barometric_pressure] 13:17:56.367: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 13:19:35.448: _train param, Dropping unused columns: [barometric_pressure] \u2588 13:19:36.22: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588| (done) 100% model_id auc logloss aucpr mean_per_class_error rmse mse training_time_ms predict_time_per_row_ms algo DeepLearning_grid_1_AutoML_1_20240522_131641_model_8 1 0.0177159 1 0 0.0582198 0.00338955 628 0.127786 DeepLearning DeepLearning_grid_1_AutoML_1_20240522_131641_model_4 1 0.0155053 1 0 0.0481989 0.00232314 117 0.1101 DeepLearning XGBoost_1_AutoML_1_20240522_131641 1 0.0347672 1 0 0.0789026 0.00622562 464 0.078661 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_12 1 0.0651884 1 0 0.106454 0.0113325 328 0.072441 XGBoost DeepLearning_grid_1_AutoML_1_20240522_131641_model_1 1 0.0235103 1 0 0.0600679 0.00360815 99 0.098096 DeepLearning XGBoost_grid_1_AutoML_1_20240522_131641_model_11 1 0.00556141 1 0 0.0320985 0.00103031 996 0.105708 XGBoost GBM_4_AutoML_1_20240522_131641 1 9.26741e-06 1 0 9.39779e-05 8.83184e-09 4293 0.280377 GBM XGBoost_grid_1_AutoML_1_20240522_131641_model_6 1 0.0141646 1 0 0.0244465 0.000597631 630 0.057813 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_13 1 0.0318374 1 0 0.0810283 0.00656559 745 0.069021 XGBoost GBM_3_AutoML_1_20240522_131641 1 2.55592e-05 1 0 0.000259039 6.7101e-08 4037 0.153118 GBM [47 rows x 10 columns] ModelMetricsBinomial: deeplearning ** Reported on test data. ** MSE: 0.0033895466349161047 RMSE: 0.05821981307867713 LogLoss: 0.017715874610611436 Mean Per-Class Error: 0.0 AUC: 1.0 AUCPR: 1.0 Gini: 1.0 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.9979014979275557 0 1 Error Rate ----- --- --- ------- ----------- 0 93 0 0 (0.0/93.0) 1 0 10 0 (0.0/10.0) Total 93 10 0 (0.0/103.0) Maximum Metrics: Maximum metrics at their respective thresholds metric threshold value idx --------------------------- ----------- ------- ----- max f1 0.997901 1 9 max f2 0.997901 1 9 max f0point5 0.997901 1 9 max accuracy 0.997901 1 9 max precision 0.999961 1 0 max recall 0.997901 1 9 max specificity 0.999961 1 0 max absolute_mcc 0.997901 1 9 max min_per_class_accuracy 0.997901 1 9 max mean_per_class_accuracy 0.997901 1 9 max tns 0.999961 93 0 max fns 0.999961 9 0 max fps 3.49717e-09 93 102 max tps 0.997901 10 9 max tnr 0.999961 1 0 max fnr 0.999961 0.9 0 max fpr 3.49717e-09 1 102 max tpr 0.997901 1 9 Gains/Lift Table: Avg response rate: 9.71 %, avg score: 11.26 % group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain kolmogorov_smirnov ------- -------------------------- ----------------- ------ ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------ ----------------- -------------------- 1 0.0194175 0.999958 10.3 10.3 1 0.999959 1 0.999959 0.2 0.2 930 930 0.2 2 0.0291262 0.999947 10.3 10.3 1 0.999947 1 0.999955 0.1 0.3 930 930 0.3 3 0.038835 0.999937 10.3 10.3 1 0.999938 1 0.999951 0.1 0.4 930 930 0.4 4 0.0485437 0.999927 10.3 10.3 1 0.99993 1 0.999947 0.1 0.5 930 930 0.5 5 0.0582524 0.999888 10.3 10.3 1 0.999889 1 0.999937 0.1 0.6 930 930 0.6 6 0.106796 0.372647 8.24 9.36364 0.8 0.879862 0.909091 0.945358 0.4 1 724 836.364 0.989247 7 0.15534 0.10825 0 6.4375 0 0.168191 0.625 0.702493 0 1 -100 543.75 0.935484 8 0.203883 0.00316095 0 4.90476 0 0.0682301 0.47619 0.551478 0 1 -100 390.476 0.88172 9 0.300971 0.000343083 0 3.32258 0 0.00100323 0.322581 0.373906 0 1 -100 232.258 0.774194 10 0.398058 0.000119786 0 2.5122 0 0.000202804 0.243902 0.282759 0 1 -100 151.22 0.666667 11 0.504854 8.52118e-05 0 1.98077 0 0.000102754 0.192308 0.222966 0 1 -100 98.0769 0.548387 12 0.601942 6.24023e-05 0 1.66129 0 7.12997e-05 0.16129 0.187015 0 1 -100 66.129 0.44086 13 0.699029 4.02512e-05 0 1.43056 0 5.12346e-05 0.138889 0.161048 0 1 -100 43.0556 0.333333 14 0.796117 2.31994e-05 0 1.2561 0 3.1514e-05 0.121951 0.141412 0 1 -100 25.6098 0.225806 15 0.893204 1.36306e-05 0 1.11957 0 1.91168e-05 0.108696 0.126043 0 1 -100 11.9565 0.11828 16 1 3.49717e-09 0 1 0 5.41845e-06 0.0970874 0.112583 0 1 -100 0 0 deeplearning prediction progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% In [17]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = h2o . as_list ( predictions . drop ([ \"predict\" ])) . values micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = h2o.as_list(predictions.drop([\"predict\"])).values micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" In [18]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0 In [19]: Copied! fig , ax = plt . subplots ( figsize = ( 6 , 6 )) n_classes = 2 for class_id in range ( n_classes ): RocCurveDisplay . from_predictions ( y_onehot_test [:, class_id ], y_score [:, class_id ], name = f \"ROC curve for class { class_id } \" , ax = ax , # plot_chance_level=(class_id == 2), ) fig, ax = plt.subplots(figsize=(6, 6)) n_classes = 2 for class_id in range(n_classes): RocCurveDisplay.from_predictions( y_onehot_test[:, class_id], y_score[:, class_id], name=f\"ROC curve for class {class_id}\", ax=ax, # plot_chance_level=(class_id == 2), ) In [20]: Copied! print ( \"Classification report: \\n {} \" . format ( classification_report ( df_test [ target_col ], h2o . as_list ( predictions [ \"predict\" ])) ) ) print( \"Classification report:\\n{}\".format( classification_report(df_test[target_col], h2o.as_list(predictions[\"predict\"])) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\"","title":"H2O AutoML"},{"location":"notebooks/03_models.html#lightgbm","text":"In [21]: Copied! config = {} config [ \"objective\" ] = \"binary\" df_train_scaled_enc = df_train_scaled . copy () df_valid_scaled_enc = df_valid_scaled . copy () df_test_scaled_enc = df_test_scaled . copy () lgb_cat_cols = cat_cols label_encoder = LabelEncoder ( lgb_cat_cols ) df_train_scaled_enc = label_encoder . fit_transform ( df_train_scaled_enc ) df_valid_scaled_enc = label_encoder . transform ( df_valid_scaled_enc ) df_test_scaled_enc = label_encoder . transform ( df_test_scaled_enc ) lgbtrain = lgbm . Dataset ( df_train_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_train_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbvalid = lgbm . Dataset ( df_valid_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_valid_scaled_enc [ target_col ], reference = lgbtrain , free_raw_data = False , ) # Final TRAIN/TEST ftrain = pd . concat ([ df_train_scaled_enc , df_valid_scaled_enc ]) . reset_index ( drop = True ) flgbtrain = lgbm . Dataset ( ftrain . drop ( columns = [ target_col ] + id_cols ), ftrain [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbtest = lgbm . Dataset ( df_test_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_test_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , reference = flgbtrain , free_raw_data = False , ) config = {} config[\"objective\"] = \"binary\" df_train_scaled_enc = df_train_scaled.copy() df_valid_scaled_enc = df_valid_scaled.copy() df_test_scaled_enc = df_test_scaled.copy() lgb_cat_cols = cat_cols label_encoder = LabelEncoder(lgb_cat_cols) df_train_scaled_enc = label_encoder.fit_transform(df_train_scaled_enc) df_valid_scaled_enc = label_encoder.transform(df_valid_scaled_enc) df_test_scaled_enc = label_encoder.transform(df_test_scaled_enc) lgbtrain = lgbm.Dataset( df_train_scaled_enc.drop(columns=[target_col] + id_cols), df_train_scaled_enc[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbvalid = lgbm.Dataset( df_valid_scaled_enc.drop(columns=[target_col] + id_cols), df_valid_scaled_enc[target_col], reference=lgbtrain, free_raw_data=False, ) # Final TRAIN/TEST ftrain = pd.concat([df_train_scaled_enc, df_valid_scaled_enc]).reset_index(drop=True) flgbtrain = lgbm.Dataset( ftrain.drop(columns=[target_col] + id_cols), ftrain[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbtest = lgbm.Dataset( df_test_scaled_enc.drop(columns=[target_col] + id_cols), df_test_scaled_enc[target_col], categorical_feature=lgb_cat_cols, reference=flgbtrain, free_raw_data=False, ) In [22]: Copied! model = lgbm . train ( config , lgbtrain , valid_sets = [ lgbvalid ], valid_names = [ \"\" ], feval = None , ) model = lgbm.train( config, lgbtrain, valid_sets=[lgbvalid], valid_names=[\"\"], feval=None, ) [LightGBM] [Info] Number of positive: 81, number of negative: 747 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002491 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 467 [LightGBM] [Info] Number of data points in the train set: 828, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097826 -> initscore=-2.221616 [LightGBM] [Info] Start training from score -2.221616 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best 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positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf In [23]: Copied! res = predict_cls_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data , raw_score = True ), task = \"binary\" , ) result = pd . DataFrame ( { \"predicted\" : res , \"ground_truth\" : df_test [ target_col ] . values , } ) print ( \"Classification report: \\n {} \" . format ( classification_report ( result [ \"ground_truth\" ], result [ \"predicted\" ]) ) ) res = predict_cls_lgbm_from_raw( preds_raw=model.predict(lgbtest.data, raw_score=True), task=\"binary\", ) result = pd.DataFrame( { \"predicted\": res, \"ground_truth\": df_test[target_col].values, } ) print( \"Classification report:\\n{}\".format( classification_report(result[\"ground_truth\"], result[\"predicted\"]) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [24]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = predict_proba_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data ), task = \"binary\" , binary2d = True , ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = predict_proba_lgbm_from_raw( preds_raw=model.predict(lgbtest.data), task=\"binary\", binary2d=True, ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0","title":"LightGBM"},{"location":"notebooks/04_model_training_pipeline.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Example usage notebook \u00b6 Objects and functions in this notebook are listed with all paramaters to ilustrate their capabillities. Most of the paramaters have default values in the implementation Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd import mlflow pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) os . environ [ \"AWS_ACCESS_KEY_ID\" ] = \"minioadmin\" os . environ [ \"AWS_SECRET_ACCESS_KEY\" ] = \"minioadmin\" os . environ [ \"MLFLOW_S3_ENDPOINT_URL\" ] = f \"http://10.152.183.156:9000\" import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd import mlflow pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\" In [2]: Copied! import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump , dill_load from inference_model.training.utils import flatten_dict , get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump, dill_load from inference_model.training.utils import flatten_dict, get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast Train a testing model \u00b6 In [3]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [4]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) In [6]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) In [7]: Copied! prepare_data = PreprocessData ( id_cols = id_cols , target_col = target_col , cat_cols = cat_cols , cont_cols = cont_cols , ) # this should be fitted only on training data _ = prepare_data . fit ( df = df_pd ) prepare_data = PreprocessData( id_cols=id_cols, target_col=target_col, cat_cols=cat_cols, cont_cols=cont_cols, ) # this should be fitted only on training data _ = prepare_data.fit(df=df_pd) In [8]: Copied! optimizer = LGBOptunaOptimizer ( objective = \"binary\" , n_class = 2 , ) trainer = Trainer ( cat_cols = prepare_data . cat_cols , target_col = prepare_data . target_col , id_cols = id_cols , objective = \"binary\" , n_class = 2 , optimizer = optimizer , preprocessors = [ prepare_data ], ) metrics_dict = trainer . fit ( df_train = df_train , df_valid = df_valid , df_test = df_test , ) optimizer = LGBOptunaOptimizer( objective=\"binary\", n_class=2, ) trainer = Trainer( cat_cols=prepare_data.cat_cols, target_col=prepare_data.target_col, id_cols=id_cols, objective=\"binary\", n_class=2, optimizer=optimizer, preprocessors=[prepare_data], ) metrics_dict = trainer.fit( df_train=df_train, df_valid=df_valid, df_test=df_test, ) [I 2024-05-22 13:01:18,589] A new study created in memory with name: LightGBMTuner feature_fraction, val_score: inf: 0%| | 0/7 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000002: 14%|#4 | 1/7 [00:03<00:21, 3.62s/it][I 2024-05-22 13:01:22,217] Trial 0 finished with value: 1.6789085046471396e-06 and parameters: {'feature_fraction': 0.4}. Best is trial 0 with value: 1.6789085046471396e-06. feature_fraction, val_score: 0.000002: 14%|#4 | 1/7 [00:03<00:21, 3.62s/it] Early stopping, best iteration is: [588] valid_0's binary_logloss: 1.67891e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000002: 29%|##8 | 2/7 [00:05<00:13, 2.72s/it][I 2024-05-22 13:01:24,308] Trial 1 finished with value: 1.9702431647601935e-06 and parameters: {'feature_fraction': 0.8999999999999999}. Best is trial 0 with value: 1.6789085046471396e-06. feature_fraction, val_score: 0.000002: 29%|##8 | 2/7 [00:05<00:13, 2.72s/it] Early stopping, best iteration is: [380] valid_0's binary_logloss: 1.97024e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 43%|####2 | 3/7 [00:06<00:06, 1.71s/it][I 2024-05-22 13:01:24,812] Trial 2 finished with value: 1.3616097829410516e-06 and parameters: {'feature_fraction': 1.0}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 43%|####2 | 3/7 [00:06<00:06, 1.71s/it] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 57%|#####7 | 4/7 [00:07<00:04, 1.40s/it][I 2024-05-22 13:01:25,731] Trial 3 finished with value: 2.2594885710041037e-06 and parameters: {'feature_fraction': 0.6}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 57%|#####7 | 4/7 [00:07<00:04, 1.40s/it] Early stopping, best iteration is: [794] valid_0's binary_logloss: 2.25949e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 71%|#######1 | 5/7 [00:08<00:02, 1.21s/it][I 2024-05-22 13:01:26,610] Trial 4 finished with value: 2.5490428264265304e-06 and parameters: {'feature_fraction': 0.8}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 71%|#######1 | 5/7 [00:08<00:02, 1.21s/it] Early stopping, best iteration is: [485] valid_0's binary_logloss: 2.54904e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 86%|########5 | 6/7 [00:09<00:01, 1.14s/it][I 2024-05-22 13:01:27,606] Trial 5 finished with value: 2.2594885710041037e-06 and parameters: {'feature_fraction': 0.5}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 86%|########5 | 6/7 [00:09<00:01, 1.14s/it] Early stopping, best iteration is: [794] valid_0's binary_logloss: 2.25949e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 100%|##########| 7/7 [00:10<00:00, 1.10s/it][I 2024-05-22 13:01:28,615] Trial 6 finished with value: 2.5249718256638313e-06 and parameters: {'feature_fraction': 0.7}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 100%|##########| 7/7 [00:10<00:00, 1.43s/it] Early stopping, best iteration is: [715] valid_0's binary_logloss: 2.52497e-06 num_leaves, val_score: 0.000001: 0%| | 0/20 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 5%|5 | 1/20 [00:00<00:09, 1.99it/s][I 2024-05-22 13:01:29,127] Trial 7 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 227}. Best is trial 7 with value: 1.3616097829410516e-06. num_leaves, val_score: 0.000001: 5%|5 | 1/20 [00:00<00:09, 1.99it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 10%|# | 2/20 [00:00<00:08, 2.01it/s][I 2024-05-22 13:01:29,623] Trial 8 finished with value: 1.361609811862523e-06 and parameters: {'num_leaves': 14}. Best is trial 7 with value: 1.3616097829410516e-06. num_leaves, val_score: 0.000001: 10%|# | 2/20 [00:01<00:08, 2.01it/s] Early stopping, best iteration is: [390] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 15%|#5 | 3/20 [00:01<00:08, 2.00it/s][I 2024-05-22 13:01:30,125] Trial 9 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 90}. Best is trial 7 with value: 1.3616097829410516e-06. num_leaves, val_score: 0.000001: 15%|#5 | 3/20 [00:01<00:08, 2.00it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 20%|## | 4/20 [00:02<00:08, 1.88it/s][I 2024-05-22 13:01:30,707] Trial 10 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 255}. Best is trial 10 with value: 1.3616097829410512e-06. num_leaves, val_score: 0.000001: 20%|## | 4/20 [00:02<00:08, 1.88it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 25%|##5 | 5/20 [00:02<00:07, 1.89it/s][I 2024-05-22 13:01:31,227] Trial 11 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 252}. Best is trial 10 with value: 1.3616097829410512e-06. num_leaves, val_score: 0.000001: 25%|##5 | 5/20 [00:02<00:07, 1.89it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 30%|### | 6/20 [00:03<00:08, 1.70it/s][I 2024-05-22 13:01:31,929] Trial 12 finished with value: 1.361609782941051e-06 and parameters: {'num_leaves': 161}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 30%|### | 6/20 [00:03<00:08, 1.70it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 35%|###5 | 7/20 [00:03<00:07, 1.70it/s][I 2024-05-22 13:01:32,519] Trial 13 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 169}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 35%|###5 | 7/20 [00:03<00:07, 1.70it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 40%|#### | 8/20 [00:04<00:06, 1.77it/s][I 2024-05-22 13:01:33,097] Trial 14 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 156}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 40%|#### | 8/20 [00:04<00:06, 1.77it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 45%|####5 | 9/20 [00:05<00:06, 1.67it/s][I 2024-05-22 13:01:33,710] Trial 15 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 196}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 45%|####5 | 9/20 [00:05<00:06, 1.67it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 50%|##### | 10/20 [00:05<00:05, 1.75it/s][I 2024-05-22 13:01:34,220] Trial 16 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 111}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 50%|##### | 10/20 [00:05<00:05, 1.75it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 55%|#####5 | 11/20 [00:06<00:04, 1.82it/s][I 2024-05-22 13:01:34,717] Trial 17 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 57}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 55%|#####5 | 11/20 [00:06<00:04, 1.82it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 60%|###### | 12/20 [00:06<00:04, 1.87it/s][I 2024-05-22 13:01:35,221] Trial 18 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 212}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 60%|###### | 12/20 [00:06<00:04, 1.87it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 65%|######5 | 13/20 [00:07<00:03, 1.90it/s][I 2024-05-22 13:01:35,730] Trial 19 finished with value: 1.3616097829410518e-06 and parameters: {'num_leaves': 255}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 65%|######5 | 13/20 [00:07<00:03, 1.90it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 70%|####### | 14/20 [00:07<00:03, 1.85it/s][I 2024-05-22 13:01:36,303] Trial 20 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 150}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 70%|####### | 14/20 [00:07<00:03, 1.85it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 75%|#######5 | 15/20 [00:08<00:02, 1.86it/s][I 2024-05-22 13:01:36,827] Trial 21 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 186}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 75%|#######5 | 15/20 [00:08<00:02, 1.86it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 80%|######## | 16/20 [00:08<00:02, 1.82it/s][I 2024-05-22 13:01:37,407] Trial 22 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 171}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 80%|######## | 16/20 [00:08<00:02, 1.82it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 85%|########5 | 17/20 [00:09<00:01, 1.86it/s][I 2024-05-22 13:01:37,920] Trial 23 finished with value: 1.3616097829410518e-06 and parameters: {'num_leaves': 127}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 85%|########5 | 17/20 [00:09<00:01, 1.86it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 90%|######### | 18/20 [00:09<00:01, 1.88it/s][I 2024-05-22 13:01:38,433] Trial 24 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 220}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 90%|######### | 18/20 [00:09<00:01, 1.88it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 95%|#########5| 19/20 [00:10<00:00, 1.83it/s][I 2024-05-22 13:01:39,016] Trial 25 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 85}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 95%|#########5| 19/20 [00:10<00:00, 1.83it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 100%|##########| 20/20 [00:10<00:00, 1.77it/s][I 2024-05-22 13:01:39,624] Trial 26 finished with value: 1.361609782941051e-06 and parameters: {'num_leaves': 140}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 100%|##########| 20/20 [00:11<00:00, 1.82it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 bagging, val_score: 0.000001: 0%| | 0/10 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 10%|# | 1/10 [00:00<00:05, 1.65it/s][I 2024-05-22 13:01:40,309] Trial 27 finished with value: 3.449205755838957e-07 and parameters: {'bagging_fraction': 0.4418842108373299, 'bagging_freq': 5}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 10%|# | 1/10 [00:00<00:05, 1.65it/s] Early stopping, best iteration is: [266] valid_0's binary_logloss: 3.44921e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 20%|## | 2/10 [00:00<00:03, 2.63it/s][I 2024-05-22 13:01:40,531] Trial 28 finished with value: 0.003198735879283877 and parameters: {'bagging_fraction': 0.40010886623598485, 'bagging_freq': 5}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 20%|## | 2/10 [00:00<00:03, 2.63it/s] Early stopping, best iteration is: [49] valid_0's binary_logloss: 0.00319874 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 30%|### | 3/10 [00:01<00:03, 2.29it/s][I 2024-05-22 13:01:41,034] Trial 29 finished with value: 2.3253951344156617e-06 and parameters: {'bagging_fraction': 0.5615890028222774, 'bagging_freq': 3}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 30%|### | 3/10 [00:01<00:03, 2.29it/s] Early stopping, best iteration is: [216] valid_0's binary_logloss: 2.3254e-06 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 40%|#### | 4/10 [00:02<00:03, 1.89it/s][I 2024-05-22 13:01:41,710] Trial 30 finished with value: 1.6847441854237672e-06 and parameters: {'bagging_fraction': 0.9729155031966343, 'bagging_freq': 7}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 40%|#### | 4/10 [00:02<00:03, 1.89it/s] Early stopping, best iteration is: [253] valid_0's binary_logloss: 1.68474e-06 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 50%|##### | 5/10 [00:02<00:02, 1.90it/s][I 2024-05-22 13:01:42,229] Trial 31 finished with value: 2.3371325286333586e-06 and parameters: {'bagging_fraction': 0.7517048260537027, 'bagging_freq': 1}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 50%|##### | 5/10 [00:02<00:02, 1.90it/s] Early stopping, best iteration is: [197] valid_0's binary_logloss: 2.33713e-06 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 60%|###### | 6/10 [00:02<00:01, 2.23it/s][I 2024-05-22 13:01:42,525] Trial 32 finished with value: 0.0031987358794118973 and parameters: {'bagging_fraction': 0.40032139731038635, 'bagging_freq': 5}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 60%|###### | 6/10 [00:02<00:01, 2.23it/s] Early stopping, best iteration is: [49] valid_0's binary_logloss: 0.00319874 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 70%|####### | 7/10 [00:03<00:01, 2.17it/s][I 2024-05-22 13:01:43,013] Trial 33 finished with value: 2.9743999611270926e-07 and parameters: {'bagging_fraction': 0.6425832769598463, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 70%|####### | 7/10 [00:03<00:01, 2.17it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.9744e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 80%|######## | 8/10 [00:03<00:00, 2.24it/s][I 2024-05-22 13:01:43,427] Trial 34 finished with value: 3.040896374710074e-07 and parameters: {'bagging_fraction': 0.6280540860504722, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 80%|######## | 8/10 [00:03<00:00, 2.24it/s] Early stopping, best iteration is: [208] valid_0's binary_logloss: 3.0409e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 90%|######### | 9/10 [00:04<00:00, 2.19it/s][I 2024-05-22 13:01:43,909] Trial 35 finished with value: 7.219543528343957e-07 and parameters: {'bagging_fraction': 0.6210765540576059, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 90%|######### | 9/10 [00:04<00:00, 2.19it/s] Early stopping, best iteration is: [187] valid_0's binary_logloss: 7.21954e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 100%|##########| 10/10 [00:04<00:00, 2.25it/s][I 2024-05-22 13:01:44,327] Trial 36 finished with value: 3.404675474216927e-07 and parameters: {'bagging_fraction': 0.6658803881381716, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 100%|##########| 10/10 [00:04<00:00, 2.16it/s] Early stopping, best iteration is: [251] valid_0's binary_logloss: 3.40468e-07 feature_fraction_stage2, val_score: 0.000000: 0%| | 0/3 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.000000: 33%|###3 | 1/3 [00:00<00:00, 2.51it/s][I 2024-05-22 13:01:44,809] Trial 37 finished with value: 2.9743999611270936e-07 and parameters: {'feature_fraction': 0.9520000000000001}. Best is trial 37 with value: 2.9743999611270936e-07. feature_fraction_stage2, val_score: 0.000000: 33%|###3 | 1/3 [00:00<00:00, 2.51it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.9744e-07 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.000000: 67%|######6 | 2/3 [00:00<00:00, 2.80it/s][I 2024-05-22 13:01:45,199] Trial 38 finished with value: 2.9743999611270936e-07 and parameters: {'feature_fraction': 0.9840000000000001}. Best is trial 37 with value: 2.9743999611270936e-07. feature_fraction_stage2, val_score: 0.000000: 67%|######6 | 2/3 [00:00<00:00, 2.80it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.9744e-07 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.000000: 100%|##########| 3/3 [00:01<00:00, 2.20it/s][I 2024-05-22 13:01:45,710] Trial 39 finished with value: 4.498413698090123e-07 and parameters: {'feature_fraction': 0.92}. Best is trial 37 with value: 2.9743999611270936e-07. feature_fraction_stage2, val_score: 0.000000: 100%|##########| 3/3 [00:01<00:00, 2.29it/s] Early stopping, best iteration is: [256] valid_0's binary_logloss: 4.49841e-07 regularization_factors, val_score: 0.000000: 0%| | 0/20 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds Early stopping, best iteration is: [227] valid_0's binary_logloss: 0.00690416 regularization_factors, val_score: 0.000000: 5%|5 | 1/20 [00:00<00:05, 3.40it/s][I 2024-05-22 13:01:46,021] Trial 40 finished with value: 0.006904162868543424 and parameters: {'lambda_l1': 2.2800500671305803, 'lambda_l2': 0.0011641838798314252}. Best is trial 40 with value: 0.006904162868543424. regularization_factors, val_score: 0.000000: 5%|5 | 1/20 [00:00<00:05, 3.40it/s] Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 10%|# | 2/20 [00:00<00:06, 2.87it/s][I 2024-05-22 13:01:46,408] Trial 41 finished with value: 2.974565285658327e-07 and parameters: {'lambda_l1': 3.2596973482462435e-08, 'lambda_l2': 1.061451695187828e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 10%|# | 2/20 [00:00<00:06, 2.87it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97457e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 15%|#5 | 3/20 [00:01<00:06, 2.68it/s][I 2024-05-22 13:01:46,811] Trial 42 finished with value: 2.974705202215368e-07 and parameters: {'lambda_l1': 2.241952369984708e-08, 'lambda_l2': 1.8880584030540923e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 15%|#5 | 3/20 [00:01<00:06, 2.68it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97471e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 20%|## | 4/20 [00:01<00:05, 2.83it/s][I 2024-05-22 13:01:47,131] Trial 43 finished with value: 2.9748702210105415e-07 and parameters: {'lambda_l1': 1.0199507520333984e-08, 'lambda_l2': 2.130651303908614e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 20%|## | 4/20 [00:01<00:05, 2.83it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97487e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 25%|##5 | 5/20 [00:01<00:05, 2.72it/s][I 2024-05-22 13:01:47,526] Trial 44 finished with value: 2.974846523730242e-07 and parameters: {'lambda_l1': 1.6736475077289633e-08, 'lambda_l2': 1.0112045153335179e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 25%|##5 | 5/20 [00:01<00:05, 2.72it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97485e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 30%|### | 6/20 [00:02<00:04, 2.89it/s][I 2024-05-22 13:01:47,829] Trial 45 finished with value: 3.176935636511711e-07 and parameters: {'lambda_l1': 5.882861681810232e-06, 'lambda_l2': 1.1233467846223274e-06}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 30%|### | 6/20 [00:02<00:04, 2.89it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 3.17694e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 35%|###5 | 7/20 [00:03<00:08, 1.61it/s][I 2024-05-22 13:01:49,021] Trial 46 finished with value: 0.00013806811413863104 and parameters: {'lambda_l1': 1.0772166664037463e-05, 'lambda_l2': 3.5907966551315282}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 35%|###5 | 7/20 [00:03<00:08, 1.61it/s] Did not meet early stopping. Best iteration is: [1000] valid_0's binary_logloss: 0.000138068 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 40%|#### | 8/20 [00:03<00:06, 1.91it/s][I 2024-05-22 13:01:49,331] Trial 47 finished with value: 3.3174632310732203e-07 and parameters: {'lambda_l1': 9.228838671620809e-07, 'lambda_l2': 1.2357091396245195e-05}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 40%|#### | 8/20 [00:03<00:06, 1.91it/s] Early stopping, best iteration is: [209] valid_0's binary_logloss: 3.31746e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 45%|####5 | 9/20 [00:03<00:05, 2.10it/s][I 2024-05-22 13:01:49,706] Trial 48 finished with value: 2.8970612681555003e-06 and parameters: {'lambda_l1': 0.004116041688790151, 'lambda_l2': 4.91159030541583e-07}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 45%|####5 | 9/20 [00:03<00:05, 2.10it/s] Early stopping, best iteration is: [206] valid_0's binary_logloss: 2.89706e-06 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 50%|##### | 10/20 [00:04<00:04, 2.33it/s][I 2024-05-22 13:01:50,028] Trial 49 finished with value: 5.975862396554039e-07 and parameters: {'lambda_l1': 2.4738617566052645e-07, 'lambda_l2': 0.003148588239925189}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 50%|##### | 10/20 [00:04<00:04, 2.33it/s] Early stopping, best iteration is: [283] valid_0's binary_logloss: 5.97586e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 55%|#####5 | 11/20 [00:04<00:03, 2.41it/s][I 2024-05-22 13:01:50,411] Trial 50 finished with value: 5.528528493352603e-07 and parameters: {'lambda_l1': 0.0005147056784853866, 'lambda_l2': 1.3373340341538605e-05}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 55%|#####5 | 11/20 [00:04<00:03, 2.41it/s] Early stopping, best iteration is: [204] valid_0's binary_logloss: 5.52853e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 60%|###### | 12/20 [00:05<00:03, 2.45it/s][I 2024-05-22 13:01:50,806] Trial 51 finished with value: 2.97483914403635e-07 and parameters: {'lambda_l1': 1.1161313264875577e-08, 'lambda_l2': 1.3457934495742431e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 60%|###### | 12/20 [00:05<00:03, 2.45it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97484e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 65%|######5 | 13/20 [00:05<00:02, 2.63it/s][I 2024-05-22 13:01:51,122] Trial 52 finished with value: 2.974834213929394e-07 and parameters: {'lambda_l1': 1.1851264993602946e-08, 'lambda_l2': 1.1579633227443254e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 65%|######5 | 13/20 [00:05<00:02, 2.63it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97483e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 70%|####### | 14/20 [00:05<00:02, 2.59it/s][I 2024-05-22 13:01:51,521] Trial 53 finished with value: 3.0224491028537356e-07 and parameters: {'lambda_l1': 1.8444434562217248e-07, 'lambda_l2': 1.904342905861566e-07}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 70%|####### | 14/20 [00:05<00:02, 2.59it/s] Early stopping, best iteration is: [204] valid_0's binary_logloss: 3.02245e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 75%|#######5 | 15/20 [00:06<00:02, 2.38it/s][I 2024-05-22 13:01:52,020] Trial 54 finished with value: 2.97111604544352e-07 and parameters: {'lambda_l1': 1.8697547616086913e-07, 'lambda_l2': 1.4195276461779107e-07}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 75%|#######5 | 15/20 [00:06<00:02, 2.38it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97112e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 80%|######## | 16/20 [00:06<00:01, 2.61it/s][I 2024-05-22 13:01:52,319] Trial 55 finished with value: 2.7975386625160922e-05 and parameters: {'lambda_l1': 9.1209069884061e-06, 'lambda_l2': 2.832296835692325e-07}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 80%|######## | 16/20 [00:06<00:01, 2.61it/s] Early stopping, best iteration is: [150] valid_0's binary_logloss: 2.79754e-05 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 85%|########5 | 17/20 [00:06<00:01, 2.77it/s][I 2024-05-22 13:01:52,623] Trial 56 finished with value: 3.012783257895089e-07 and parameters: {'lambda_l1': 5.249197497865728e-07, 'lambda_l2': 8.723247474648092e-06}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 85%|########5 | 17/20 [00:06<00:01, 2.77it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 3.01278e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 90%|######### | 18/20 [00:07<00:00, 2.31it/s][I 2024-05-22 13:01:53,223] Trial 57 finished with value: 6.216923691382048e-06 and parameters: {'lambda_l1': 1.2654511369988902e-07, 'lambda_l2': 0.026856741790607278}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 90%|######### | 18/20 [00:07<00:00, 2.31it/s] Early stopping, best iteration is: [494] valid_0's binary_logloss: 6.21692e-06 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 95%|#########5| 19/20 [00:07<00:00, 2.57it/s][I 2024-05-22 13:01:53,511] Trial 58 finished with value: 4.6829312861054814e-05 and parameters: {'lambda_l1': 0.02854037816163236, 'lambda_l2': 1.2681271167280724e-07}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 95%|#########5| 19/20 [00:07<00:00, 2.57it/s] Early stopping, best iteration is: [182] valid_0's binary_logloss: 4.68293e-05 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 100%|##########| 20/20 [00:08<00:00, 2.74it/s][I 2024-05-22 13:01:53,821] Trial 59 finished with value: 2.468284408525897e-07 and parameters: {'lambda_l1': 0.0001184573048303984, 'lambda_l2': 4.182514336496212e-06}. Best is trial 59 with value: 2.468284408525897e-07. regularization_factors, val_score: 0.000000: 100%|##########| 20/20 [00:08<00:00, 2.47it/s] Early stopping, best iteration is: [240] valid_0's binary_logloss: 2.46828e-07 min_child_samples, val_score: 0.000000: 0%| | 0/5 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 20%|## | 1/5 [00:00<00:01, 3.44it/s][I 2024-05-22 13:01:54,119] Trial 60 finished with value: 5.156782324432179e-06 and parameters: {'min_child_samples': 10}. Best is trial 60 with value: 5.156782324432179e-06. min_child_samples, val_score: 0.000000: 20%|## | 1/5 [00:00<00:01, 3.44it/s] Early stopping, best iteration is: [199] valid_0's binary_logloss: 5.15678e-06 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 40%|#### | 2/5 [00:00<00:00, 3.32it/s][I 2024-05-22 13:01:54,427] Trial 61 finished with value: 3.282327081150373e-07 and parameters: {'min_child_samples': 100}. Best is trial 61 with value: 3.282327081150373e-07. min_child_samples, val_score: 0.000000: 40%|#### | 2/5 [00:00<00:00, 3.32it/s] Early stopping, best iteration is: [269] valid_0's binary_logloss: 3.28233e-07 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 60%|###### | 3/5 [00:00<00:00, 3.41it/s][I 2024-05-22 13:01:54,712] Trial 62 finished with value: 3.6291058037098226e-06 and parameters: {'min_child_samples': 5}. Best is trial 61 with value: 3.282327081150373e-07. min_child_samples, val_score: 0.000000: 60%|###### | 3/5 [00:00<00:00, 3.41it/s] Early stopping, best iteration is: [176] valid_0's binary_logloss: 3.62911e-06 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 80%|######## | 4/5 [00:01<00:00, 3.78it/s][I 2024-05-22 13:01:54,932] Trial 63 finished with value: 3.8920361508245125e-07 and parameters: {'min_child_samples': 25}. Best is trial 61 with value: 3.282327081150373e-07. min_child_samples, val_score: 0.000000: 80%|######## | 4/5 [00:01<00:00, 3.78it/s] Early stopping, best iteration is: [204] valid_0's binary_logloss: 3.89204e-07 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 100%|##########| 5/5 [00:01<00:00, 3.62it/s][I 2024-05-22 13:01:55,228] Trial 64 finished with value: 2.1603725624802887e-07 and parameters: {'min_child_samples': 50}. Best is trial 64 with value: 2.1603725624802887e-07. min_child_samples, val_score: 0.000000: 100%|##########| 5/5 [00:01<00:00, 3.56it/s] Early stopping, best iteration is: [252] valid_0's binary_logloss: 2.16037e-07 In [12]: Copied! # save/load trainer dill_dump ( \"lgbm_trainer.dill\" , trainer ) trainer = dill_load ( \"lgbm_trainer.dill\" ) # save/load trainer dill_dump(\"lgbm_trainer.dill\", trainer) trainer = dill_load(\"lgbm_trainer.dill\") In [13]: Copied! pprint ( metrics_dict ) pprint(metrics_dict) {'cls_report': {'0': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 94}, '1': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 10}, 'accuracy': 1.0, 'macro avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}, 'weighted avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}}, 'cm': [[94, 0], [0, 10]], 'prec_rec_curve': [[0.09615384615384616, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 1.0]]} In [14]: Copied! pprint ( trainer . compute_metrics ( df_test , with_dynamic_binary_threshold = True )) pprint(trainer.compute_metrics(df_test, with_dynamic_binary_threshold=True)) {'cls_report': {'0': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 94}, '1': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 10}, 'accuracy': 1.0, 'macro avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}, 'weighted avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}}, 'cm': [[94, 0], [0, 10]], 'prec_rec_curve': [[0.09615384615384616, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 1.0]]} Mlflow \u00b6 tracking model registration in Minio permanent experiment deletion # in python mlflow.get_experiment_by_name(\"success6g_lgbm\").experiment_id # in cmd mlflow experiments delete -x 1 In [26]: Copied! mlflow_host = \"10.152.183.54\" mlflow_host_url = \"mlflow.mlflow.svc.cluster.local\" mlflow_port = \"5000\" os . environ [ \"AWS_ACCESS_KEY_ID\" ] = \"minioadmin\" os . environ [ \"AWS_SECRET_ACCESS_KEY\" ] = \"minioadmin\" os . environ [ \"MLFLOW_S3_ENDPOINT_URL\" ] = f \"http://10.152.183.156:9000\" mlflow . set_tracking_uri ( \"http://\" + mlflow_host + \":\" + mlflow_port ) experiment_id = get_or_create_experiment ( \"success6g\" ) mlflow . set_experiment ( experiment_id = experiment_id ) mlflow_host = \"10.152.183.54\" mlflow_host_url = \"mlflow.mlflow.svc.cluster.local\" mlflow_port = \"5000\" os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\" mlflow.set_tracking_uri(\"http://\" + mlflow_host + \":\" + mlflow_port) experiment_id = get_or_create_experiment(\"success6g\") mlflow.set_experiment(experiment_id=experiment_id) Out[26]: <Experiment: artifact_location='s3://mlflow/3', creation_time=1716388845676, experiment_id='3', last_update_time=1716388845676, lifecycle_stage='active', name='success6g', tags={}> In [27]: Copied! metrics_dict_flattened = flatten_dict ( metrics_dict ) # mlflow metrics can be only int, float not list del metrics_dict_flattened [ \"cm\" ] del metrics_dict_flattened [ \"prec_rec_curve\" ] metrics_dict_flattened = flatten_dict(metrics_dict) # mlflow metrics can be only int, float not list del metrics_dict_flattened[\"cm\"] del metrics_dict_flattened[\"prec_rec_curve\"] In [29]: Copied! run_name = \"test\" with mlflow . start_run ( experiment_id = experiment_id , run_name = run_name , nested = True ) as run : mlflow . log_params ( trainer . optimizer . best ) mlflow . log_metrics ( metrics_dict_flattened ) # Log tags mlflow . set_tags ( tags = { \"project\" : \"SUCCESS6G\" , \"optimizer_engine\" : \"optuna\" , \"model_family\" : \"ligtgbm\" , \"feature_set_version\" : 1 , } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow . pyfunc . log_model ( python_model = trainer , artifact_path = artifact_path , registered_model_name = registered_model_name , ) model_uri = mlflow . get_artifact_uri ( artifact_path ) print ( f \"Run ID: \\n { run . info . run_id } \\n Model uri: \\n { model_uri } \" ) run_name = \"test\" with mlflow.start_run( experiment_id=experiment_id, run_name=run_name, nested=True ) as run: mlflow.log_params(trainer.optimizer.best) mlflow.log_metrics(metrics_dict_flattened) # Log tags mlflow.set_tags( tags={ \"project\": \"SUCCESS6G\", \"optimizer_engine\": \"optuna\", \"model_family\": \"ligtgbm\", \"feature_set_version\": 1, } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow.pyfunc.log_model( python_model=trainer, artifact_path=artifact_path, registered_model_name=registered_model_name, ) model_uri = mlflow.get_artifact_uri(artifact_path) print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\") 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: typing.Union[pandas.core.frame.DataFrame, dict], skipping schema inference 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: <class 'pandas.core.frame.DataFrame'>, skipping schema inference Successfully registered model 'success6g_model'. 2024/05/22 14:44:22 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: success6g_model, version 1 Run ID: 24fedb96c5004767800fa32a3220c8dd Model uri: s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model Created version '1' of model 'success6g_model'. Testing of predictions \u00b6 Example data \u00b6 In [30]: Copied! df_pd . iloc [: 2 ] df_pd.iloc[:2] Out[30]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc In [31]: Copied! # for testing using mlflow inference service request_dict = { \"engine_load\" : [ 100.0 , 100.0 ], \"engine_coolant_temp\" : [ 17.0 , 17.0 ], \"engine_speed\" : [ 904.5 , 906.0 ], \"vehicle_speed\" : [ 0.0 , 0.0 ], \"intake_air_temp\" : [ 10.0 , 11.0 ], \"maf\" : [ 12.55 , 12.36 ], \"throttle_position\" : [ 83.14 , 83.14 ], \"fuel_rg_pressure\" : [ 37270.0 , 37800.0 ], \"barometric_pressure\" : [ 101.0 , 101.0 ], \"control_voltage\" : [ 0.06 , 14.56 ], \"class\" : [ 1 , 1 ], \"car_id\" : [ \"123abc\" , \"123abc\" ], } # for testing using mlflow inference service request_dict = { \"engine_load\": [100.0, 100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"class\": [1, 1], \"car_id\": [\"123abc\", \"123abc\"], } Trainer \u00b6 In [32]: Copied! trainer . predict ( df = df_pd . iloc [: 2 ] . drop ( columns = [ \"class\" ]), context = {}) trainer.predict(df=df_pd.iloc[:2].drop(columns=[\"class\"]), context={}) Out[32]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 0 14.269467 1 14.269467 Downloaded Trainer \u00b6 In [33]: Copied! model_uri model_uri Out[33]: 's3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model' In [34]: Copied! loaded_trainer = mlflow . pyfunc . load_model ( model_uri ) loaded_trainer = mlflow.pyfunc.load_model(model_uri) Downloading artifacts: 0%| | 0/9 [00:00<?, ?it/s] In [35]: Copied! loaded_trainer . predict ( df_pd . iloc [: 2 ] . drop ( columns = [ \"class\" ])) loaded_trainer.predict(df_pd.iloc[:2].drop(columns=[\"class\"])) Out[35]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 0 14.269467 1 14.269467 Downloaded/Served Trainer \u00b6 i.e. testing model locally In [36]: Copied! model_uri model_uri Out[36]: 's3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model' In [ ]: Copied! ! mlflow models serve - m \"s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model\" -- env - manager local - p 5000 ! mlflow models serve -m \"s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model\" --env-manager local -p 5000 Downloading artifacts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 2088.80it/s] 2024/05/22 14:45:07 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function' Downloading artifacts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 9/9 [00:00<00:00, 69.72it/s] 2024/05/22 14:45:07 INFO mlflow.pyfunc.backend: === Running command 'exec gunicorn --timeout=60 -b 127.0.0.1:5000 -w 1 ${GUNICORN_CMD_ARGS} -- mlflow.pyfunc.scoring_server.wsgi:app' [2024-05-22 14:45:07 +0000] [17971] [INFO] Starting gunicorn 22.0.0 [2024-05-22 14:45:07 +0000] [17971] [INFO] Listening at: http://127.0.0.1:5000 (17971) [2024-05-22 14:45:07 +0000] [17971] [INFO] Using worker: sync [2024-05-22 14:45:07 +0000] [17972] [INFO] Booting worker with pid: 17972 root@jupyter-5uperpalo:~/assignment# curl -X POST -H \"Content-Type:application/json\" --data '{\"inputs\": {\"engine_load\": [100.0 ,100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"car_id\": [\"123abc\", \"123abc\"]}}' http://127.0.0.1:5000/invocations {\"predictions\": [{\"class\": 14.26946728558632}, {\"class\": 14.26946728558632}]} Model deployment using Kserve \u00b6 https://mlflow.org/docs/latest/deployment/deploy-model-to-kubernetes/tutorial.html?highlight=kserve#step-7-deploying-the-model-to-kserve Using model_uri \u00b6 since I configured minio as artifact store I can use model_uri - I was WRONG! procedure: make sure the kserve is able to reach minio * by [defining s3-secret and attaching it to service account](https://kserve.github.io/website/0.7/modelserving/storage/s3/s3/#create-s3-secret) * created ServiceAccount must be in a same namespace as the InferenceService we are going to create, [e.g. stackoverflow here](https://stackoverflow.com/a/70977798) * `If an application does not specify a serviceAccountName, it uses the \"default\" service account.`[e.g. here](https://kubernetes.io/docs/reference/access-authn-authz/rbac/#role-binding-examples) ``` apiVersion: v1 kind: Secret metadata: name: s3creds namespace: mlflow-kserve-success6g annotations: serving.kserve.io/s3-endpoint: 10.152.183.156:9000 # replace with your s3 endpoint e.g minio-service.kubeflow:9000 serving.kserve.io/s3-usehttps: \"0\" # by default 1, if testing with minio you can set to 0 # serving.kserve.io/s3-region: \"us-east-2\" serving.kserve.io/s3-useanoncredential: \"false\" # omitting this is the same as false, if true will ignore provided credential and use anonymous credentials type: Opaque stringData: # use `stringData` for raw credential string or `data` for base64 encoded string AWS_ACCESS_KEY_ID: \"minioadmin\" AWS_SECRET_ACCESS_KEY: \"minioadmin\" ``` `kubectl apply -f create-s3-secret.yaml` create namespace ``` kubectl create namespace mlflow-kserve-success6g namespace/mlflow-kserve-success6g created ``` specify configuration of the deployment in the file `mlflow-kserve-success6g_model_uri.yaml` ``` apiVersion: \"serving.kserve.io/v1beta1\" kind: \"InferenceService\" metadata: name: \"mlflow-success6g\" namespace: \"mlflow-kserve-success6g\" spec: predictor: model: modelFormat: name: mlflow protocolVersion: v2 storageUri: \"s3://mlflow/1/777cf64c922149a4b77c85987865deb0/artifacts/success6g_model\" ``` deploy ``` kubectl apply -f mlflow-kserve-success6g.yaml inferenceservice.serving.kserve.io/mlflow-success6g created ``` check the status of the deployment `kubectl get inferenceservice mlflow-success6g` specify testing json in `test-input.json` ``` { \"inputs\": [ { \"name\": \"input\", \"shape\": [13], \"datatype\": \"FP32\", \"data\": { \"engine_load\": [100.0 ,100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"car_id\": [\"123abc\", \"123abc\"], } } ] } ``` test json ``` SERVICE_HOSTNAME=$(kubectl get inferenceservice mlflow-success6g -n mlflow-kserve-success6g -o jsonpath='{.status.url}' | cut -d \"/\" -f 3) curl -v \\ -H \"Host: ${SERVICE_HOSTNAME}\" \\ -H \"Content-Type: application/json\" \\ -d @./test-input.json \\ http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/mlflow-success6g/infer ``` but after all the trouble this can still fail if you developed the model in different python version than kserve is using ``` pmulinka@saiacheron:~/kubernetes/kserve$ kubectl logs -p mlflow-success6g-predictor-00001-deployment-74ccf4fb5d-lktz9 -n mlflow-kserve-success6g Defaulted container \"kserve-container\" out of: kserve-container, queue-proxy, storage-initializer (init) Environment tarball not found at '/mnt/models/environment.tar.gz' Environment not found at './envs/environment' 2024-05-16 15:32:55,293 [mlserver.parallel] DEBUG - Starting response processing loop... 2024-05-16 15:32:55,298 [mlserver.rest] INFO - HTTP server running on http://0.0.0.0:8080 INFO: Started server process [1] INFO: Waiting for application startup. 2024-05-16 15:32:55,411 [mlserver.metrics] INFO - Metrics server running on http://0.0.0.0:8082 2024-05-16 15:32:55,412 [mlserver.metrics] INFO - Prometheus scraping endpoint can be accessed on http://0.0.0.0:8082/metrics INFO: Started server process [1] INFO: Waiting for application startup. INFO: Application startup complete. 2024-05-16 15:32:57,613 [mlserver.grpc] INFO - gRPC server running on http://0.0.0.0:9000 INFO: Application startup complete. INFO: Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit) INFO: Uvicorn running on http://0.0.0.0:8082 (Press CTRL+C to quit) 2024/05/16 15:32:58 WARNING mlflow.pyfunc: Detected one or more mismatches between the model's dependencies and the current Python environment: - mlflow (current: 2.3.1, required: mlflow==2.12.2) - cryptography (current: 38.0.4, required: cryptography==40.0.2) - cytoolz (current: uninstalled, required: cytoolz==0.12.0) - defusedxml (current: uninstalled, required: defusedxml==0.7.1) - distributed (current: uninstalled, required: distributed==2023.5.1) - lightgbm (current: 3.3.5, required: lightgbm==4.3.0) - lz4 (current: uninstalled, required: lz4==4.3.2) - numpy (current: 1.23.5, required: numpy==1.24.3) - optuna-integration (current: uninstalled, required: optuna-integration==3.6.0) - pandas (current: 2.0.1, required: pandas==2.2.2) - pyarrow (current: 11.0.0, required: pyarrow==12.0.0) - redis (current: uninstalled, required: redis==5.0.4) - scipy (current: 1.9.1, required: scipy==1.10.1) To fix the mismatches, call `mlflow.pyfunc.get_model_dependencies(model_uri)` to fetch the model's environment and install dependencies using the resulting environment file. 2024/05/16 15:32:58 WARNING mlflow.pyfunc: The version of Python that the model was saved in, `Python 3.10.11`, differs from the version of Python that is currently running, `Python 3.8.16`, and may be incompatible 2024-05-16 15:32:58,967 [mlserver] INFO - Couldn't load model 'mlflow-success6g'. Model will be removed from registry. 2024-05-16 15:32:58,967 [mlserver.parallel] ERROR - An error occurred processing a model update of type 'Load'. Traceback (most recent call last): File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/worker.py\", line 158, in _process_model_update await self._model_registry.load(model_settings) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 293, in load return await self._models[model_settings.name].load(model_settings) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 148, in load await self._load_model(new_model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 165, in _load_model model.ready = await model.load() File \"/opt/conda/lib/python3.8/site-packages/mlserver_mlflow/runtime.py\", line 155, in load self._model = mlflow.pyfunc.load_model(model_uri) File \"/opt/conda/lib/python3.8/site-packages/mlflow/pyfunc/__init__.py\", line 596, in load_model model_impl = importlib.import_module(conf[MAIN])._load_pyfunc(data_path) File \"/opt/conda/lib/python3.8/site-packages/mlflow/pyfunc/model.py\", line 291, in _load_pyfunc python_model = cloudpickle.load(f) ModuleNotFoundError: No module named 'inference_model' 2024-05-16 15:32:58,970 [mlserver] INFO - Couldn't load model 'mlflow-success6g'. Model will be removed from registry. 2024-05-16 15:32:58,975 [mlserver.parallel] ERROR - An error occurred processing a model update of type 'Unload'. Traceback (most recent call last): File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/worker.py\", line 160, in _process_model_update await self._model_registry.unload_version( File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 302, in unload_version await model_registry.unload_version(version) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 201, in unload_version model = await self.get_model(version) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 237, in get_model raise ModelNotFound(self._name, version) mlserver.errors.ModelNotFound: Model mlflow-success6g not found 2024-05-16 15:32:58,976 [mlserver] ERROR - Some of the models failed to load during startup! Traceback (most recent call last): File \"/opt/conda/lib/python3.8/site-packages/mlserver/server.py\", line 125, in start await asyncio.gather( File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 293, in load return await self._models[model_settings.name].load(model_settings) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 148, in load await self._load_model(new_model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 161, in _load_model model = await callback(model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/registry.py\", line 152, in load_model loaded = await pool.load_model(model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/pool.py\", line 74, in load_model await self._dispatcher.dispatch_update(load_message) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 123, in dispatch_update return await asyncio.gather( File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 138, in _dispatch_update return await self._dispatch(worker_update) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 146, in _dispatch return await self._wait_response(internal_id) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 152, in _wait_response inference_response = await async_response mlserver.parallel.errors.WorkerError: builtins.ModuleNotFoundError: No module named 'inference_model' 2024-05-16 15:32:58,978 [mlserver.parallel] INFO - Waiting for shutdown of default inference pool... 2024-05-16 15:32:59,163 [mlserver.parallel] INFO - Shutdown of default inference pool complete 2024-05-16 15:32:59,163 [mlserver.grpc] INFO - Waiting for gRPC server shutdown 2024-05-16 15:32:59,169 [mlserver.grpc] INFO - gRPC server shutdown complete INFO: Shutting down INFO: Shutting down INFO: Waiting for application shutdown. INFO: Application shutdown complete. INFO: Finished server process [1] INFO: Waiting for application shutdown. INFO: Application shutdown complete. INFO: Finished server process [1] ``` and some possible readiness probe issues * apparently I am not alone in this, e.g. [here](https://stackoverflow.com/questions/62569747/ml-model-pod-keeps-restarting-in-seldon-deployment) ``` pmulinka@saiacheron:~/kubernetes/kserve$ kubectl describe pod mlflow-success6g-predictor-00001-deployment-74ccf4fb5d-lktz9 -n mlflow-kserve-success6g Events: Type Reason Age From Message ---- ------ ---- ---- ------- Warning Unhealthy 4m11s (x268 over 34m) kubelet Readiness probe failed: Get \"http://10.1.4.204:8012/\": context deadline exceeded (Client.Timeout exceeded while awaiting headers) ``` Using docker image \u00b6 create docker ``` sudo apt-get install python3.10-venv python3.10 -m venv python310venv source python310venv/bin/activate pip install mlflow pip install boto3 export AWS_ACCESS_KEY_ID=minioadmin export AWS_SECRET_ACCESS_KEY=minioadmin export MLFLOW_S3_ENDPOINT_URL=http://10.152.183.156:9000 mlflow models build-docker -m s3://mlflow/1/777cf64c922149a4b77c85987865deb0/artifacts/success6g_model -n 5uperpalo/mlflow-success6g --enable-mlserver ``` generate token and login to DockerHub, [generate DockerHub token](https://hub.docker.com/settings/security?generateToken=true) and login ``` (python310venv) pmulinka@iesc-gpu3:~/scripts$ docker login -u 5uperpalo Password: WARNING! Your password will be stored unencrypted in /home/pmulinka/.docker/config.json. Configure a credential helper to remove this warning. See https://docs.docker.com/engine/reference/commandline/login/#credentials-store Login Succeeded ``` push the image to dockerhub ``` (python310venv) pmulinka@iesc-gpu3:~/scripts$ docker push 5uperpalo/mlflow-success6g Using default tag: latest The push refers to repository [docker.io/5uperpalo/mlflow-success6g] e5f76b582f6c: Pushed 624821c51019: Pushed 93256a85bb86: Pushed 2f62dcdb5ae7: Pushed 1146ddf03b42: Pushed 639ddc87d13f: Pushed 4f18e59d57c4: Pushed 5761e01a334a: Pushed f31b6bde159a: Pushed c25ef1a18d31: Pushed 40a774d6610c: Pushed a2b215b4258b: Pushed b56a9203d2a0: Pushed 2536dbfb2294: Pushed 67ea405a7d49: Pushed a81c3606ee5c: Pushed 4a1518ebc26e: Mounted from library/ubuntu latest: digest: sha256:57f653a3c8d63519c07dccffc6eb12dba5006db07ebcd869467ce752fb1b0bda size: 3899 ``` specify configuration of the deployment in the file `mlflow-kserve-success6g_model_uri.yaml` ``` apiVersion: \"serving.kserve.io/v1beta1\" kind: \"InferenceService\" metadata: name: \"mlflow-success6g\" namespace: \"mlflow-kserve-success6g\" spec: predictor: containers: - name: \"mlflow-success6g\" image: \"5uperpalo/mlflow-success6g\" ports: - containerPort: 8080 protocol: TCP #env: #- name: PROTOCOL #value: \"v2\" resources: limits: cpu: 2 memory: 8Gi requests: cpu: 2 memory: 8Gi ``` deploy ``` pmulinka@saiacheron:~/kubernetes/kserve$ kubectl apply -f mlflow-kserve-success6g_docker_image.yaml inferenceservice.serving.kserve.io/mlflow-success6g created ``` check status of deployment ``` pmulinka@saiacheron:~$ kubectl get inferenceservice -n mlflow-kserve-success6g NAME URL READY PREV LATEST PREVROLLEDOUTREVISION LATESTREADYREVISION AGE mlflow-success6g http://mlflow-success6g.mlflow-kserve-success6g.svc.cluster.local False 100 mlflow-success6g-predictor-00001 6m4s pmulinka@saiacheron:~$ kubectl get pods -n mlflow-kserve-success6g NAME READY STATUS RESTARTS AGE mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 2/2 Running 1 (2m14s ago) 4m47s pmulinka@saiacheron:~$ kubectl get inferenceservice mlflow-success6g -n mlflow-kserve-success6g -o yaml apiVersion: serving.kserve.io/v1beta1 kind: InferenceService metadata: annotations: kubectl.kubernetes.io/last-applied-configuration: | {\"apiVersion\":\"serving.kserve.io/v1beta1\",\"kind\":\"InferenceService\",\"metadata\":{\"annotations\":{},\"name\":\"mlflow-success6g\",\"namespace\":\"mlflow-kserve-success6g\"},\"spec\":{\"predictor\":{\"containers\":[{\"image\":\"5uperpalo/mlflow-success6g\",\"name\":\"mlflow-success6g\",\"ports\":[{\"containerPort\":8080,\"protocol\":\"TCP\"}],\"resources\":{\"limits\":{\"cpu\":2,\"memory\":\"8Gi\"},\"requests\":{\"cpu\":2,\"memory\":\"8Gi\"}}}]}}} creationTimestamp: \"2024-05-28T14:33:05Z\" finalizers: - inferenceservice.finalizers generation: 1 name: mlflow-success6g namespace: mlflow-kserve-success6g resourceVersion: \"76010518\" uid: b9573ff3-0f67-4650-a0c7-51941a0fffe2 spec: predictor: containers: - image: 5uperpalo/mlflow-success6g name: kserve-container ports: - containerPort: 8080 protocol: TCP resources: limits: cpu: \"2\" memory: 8Gi requests: cpu: \"2\" memory: 8Gi status: address: url: http://mlflow-success6g.mlflow-kserve-success6g.svc.cluster.local components: predictor: address: url: http://mlflow-success6g-predictor.mlflow-kserve-success6g.svc.cluster.local latestCreatedRevision: mlflow-success6g-predictor-00001 latestReadyRevision: mlflow-success6g-predictor-00001 latestRolledoutRevision: mlflow-success6g-predictor-00001 traffic: - latestRevision: true percent: 100 revisionName: mlflow-success6g-predictor-00001 url: http://mlflow-success6g-predictor.mlflow-kserve-success6g.svc.cluster.local conditions: - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: Predictor ingress not created status: \"False\" type: IngressReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: PredictorConfigurationReady not ready severity: Info status: \"False\" type: LatestDeploymentReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" message: 'Revision \"mlflow-success6g-predictor-00001\" failed with message: Container failed with: container exited with no error.' reason: RevisionFailed severity: Info status: \"False\" type: PredictorConfigurationReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" message: Revision \"mlflow-success6g-predictor-00001\" failed to become ready. reason: RevisionMissing status: \"False\" type: PredictorReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" message: Revision \"mlflow-success6g-predictor-00001\" failed to become ready. reason: RevisionMissing severity: Info status: \"False\" type: PredictorRouteReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: Predictor ingress not created status: \"False\" type: Ready - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: PredictorRouteReady not ready severity: Info status: \"False\" type: RoutesReady modelStatus: copies: failedCopies: 0 totalCopies: 1 states: activeModelState: Loaded targetModelState: Pending transitionStatus: InProgress observedGeneration: 1 url: http://mlflow-success6g.mlflow-kserve-success6g.svc.cluster.local pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get events -n mlflow-kserve-success6g LAST SEEN TYPE REASON OBJECT MESSAGE 2m28s Warning BackOff pod/mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 Back-off restarting failed container kserve-container in pod mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7_mlflow-kserve-success6g(1ad2d6da-3a40-4b14-956a-0faee3ca9b3c) 2m28s Warning InternalError revision/mlflow-success6g-predictor-00001 failed to update PA \"mlflow-success6g-predictor-00001\": Operation cannot be fulfilled on podautoscalers.autoscaling.internal.knative.dev \"mlflow-success6g-predictor-00001\": the object has been modified; please apply your changes to the latest version and try again ``` debugging ``` # https://kserve.github.io/website/0.10/developer/debug/ pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get ksvc -n mlflow-kserve-success6g NAME URL LATESTCREATED LATESTREADY READY REASON mlflow-success6g-predictor http://mlflow-success6g-predictor.mlflow-kserve-success6g.svc.cluster.local mlflow-success6g-predictor-00001 mlflow-success6g-predictor-00001 False RevisionMissing # RevisionMissing Error pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get revision $(kubectl get configuration mlflow-success6g-predictor -n mlflow-kserve-success6g --output jsonpath=\"{.status.latestCreatedRevisionName}\") Error from server (NotFound): revisions.serving.knative.dev \"mlflow-success6g-predictor-00001\" not found pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get pod -l serving.kserve.io/inferenceservice=mlflow-success6g -n mlflow-kserve-success6g NAME READY STATUS RESTARTS AGE mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 0/2 CrashLoopBackOff 8 (4m51s ago) 43m pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get pod -l serving.kserve.io/inferenceservice=mlflow-success6g -n mlflow-kserve-success6g NAME READY STATUS RESTARTS AGE mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 2/2 Running 9 (7m7s ago) 45m pmulinka@saiacheron:~/kubernetes/kserve$ kubectl logs -l model=mlflow-success6g -n mlflow-kserve-success6g -c storage-initializer No resources found in mlflow-kserve-success6g namespace. pmulinka@saiacheron:~/kubernetes/kserve$ kubectl delete inferenceservice mlflow-success6g -n mlflow-kserve-success6g inferenceservice.serving.kserve.io \"mlflow-success6g\" deleted ``` Appendix \u00b6 output from making docker ``` (python310venv) pmulinka@iesc-gpu3:~/scripts$ mlflow models build-docker -m s3://mlflow/1/777cf64c922149a4b77c85987865deb0/artifacts/success6g_model -n 5uperpalo/mlflow-success6g --enable-mlserver 2024/05/16 22:20:25 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function' 2024/05/16 22:20:25 INFO mlflow.pyfunc.backend: Building docker image with name 5uperpalo/mlflow-success6g #0 building with \"default\" instance using docker driver #1 [internal] load build definition from Dockerfile #1 transferring dockerfile: 2.53kB done #1 DONE 0.1s #2 [internal] load metadata for docker.io/library/ubuntu:20.04 #2 DONE 1.6s #3 [internal] load .dockerignore #3 transferring context: 2B done #3 DONE 0.0s #4 [ 1/17] FROM docker.io/library/ubuntu:20.04@sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b #4 resolve docker.io/library/ubuntu:20.04@sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b 0.0s done #4 ... #5 [internal] load build context #5 transferring context: 197.37kB done #5 DONE 0.1s #4 [ 1/17] FROM docker.io/library/ubuntu:20.04@sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b #4 sha256:2abc4dfd83182546da40dfae3e391db0810ad4a130fb5a887c6ceb3df8e1d310 2.29kB / 2.29kB done #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 0B / 27.51MB 0.2s #4 sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b 1.13kB / 1.13kB done #4 sha256:cc9cc8169c9517ae035cf293b15f06922cb8c6c864d625a72b7b18667f264b70 424B / 424B done #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 3.15MB / 27.51MB 0.4s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 8.39MB / 27.51MB 0.7s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 11.53MB / 27.51MB 0.9s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 16.78MB / 27.51MB 1.2s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 22.02MB / 27.51MB 1.5s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 25.17MB / 27.51MB 1.7s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 27.26MB / 27.51MB 1.8s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 27.51MB / 27.51MB 1.9s done #4 extracting sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 #4 extracting sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 1.8s done #4 DONE 4.0s #6 [ 2/17] RUN apt-get -y update && DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get install -y --no-install-recommends wget curl nginx ca-certificates bzip2 build-essential cmake git-core #6 0.681 Get:1 http://security.ubuntu.com/ubuntu focal-security InRelease [114 kB] #6 0.682 Get:2 http://archive.ubuntu.com/ubuntu focal InRelease [265 kB] #6 1.324 Get:3 http://archive.ubuntu.com/ubuntu focal-updates InRelease [114 kB] #6 1.432 Get:4 http://security.ubuntu.com/ubuntu focal-security/universe amd64 Packages [1205 kB] #6 1.476 Get:5 http://archive.ubuntu.com/ubuntu focal-backports InRelease [108 kB] #6 1.690 Get:6 http://archive.ubuntu.com/ubuntu focal/restricted amd64 Packages [33.4 kB] #6 1.838 Get:7 http://archive.ubuntu.com/ubuntu focal/multiverse amd64 Packages [177 kB] #6 1.897 Get:8 http://archive.ubuntu.com/ubuntu focal/main amd64 Packages [1275 kB] #6 2.177 Get:9 http://security.ubuntu.com/ubuntu focal-security/restricted amd64 Packages [3608 kB] #6 2.537 Get:10 http://archive.ubuntu.com/ubuntu focal/universe amd64 Packages [11.3 MB] #6 3.807 Get:11 http://security.ubuntu.com/ubuntu focal-security/main amd64 Packages [3669 kB] #6 5.468 Get:12 http://security.ubuntu.com/ubuntu focal-security/multiverse amd64 Packages [29.8 kB] #6 7.649 Get:13 http://archive.ubuntu.com/ubuntu focal-updates/restricted amd64 Packages [3758 kB] #6 9.304 Get:14 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 Packages [1502 kB] #6 9.973 Get:15 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 Packages [4143 kB] #6 12.14 Get:16 http://archive.ubuntu.com/ubuntu focal-updates/multiverse amd64 Packages [32.5 kB] #6 12.15 Get:17 http://archive.ubuntu.com/ubuntu focal-backports/main amd64 Packages [55.2 kB] #6 12.18 Get:18 http://archive.ubuntu.com/ubuntu focal-backports/universe amd64 Packages [28.6 kB] #6 12.44 Fetched 31.5 MB in 12s (2621 kB/s) #6 12.44 Reading package lists... #6 13.60 Reading package lists... #6 14.67 Building dependency tree... #6 14.83 Reading state information... #6 15.04 bzip2 is already the newest version (1.0.8-2). #6 15.04 The following additional packages will be installed: #6 15.04 binutils binutils-common binutils-x86-64-linux-gnu cmake-data cpp cpp-9 #6 15.04 dpkg-dev fontconfig-config fonts-dejavu-core g++ g++-9 gcc gcc-9 gcc-9-base #6 15.04 git-man iproute2 libarchive13 libasan5 libasn1-8-heimdal libatomic1 #6 15.04 libbinutils libbrotli1 libbsd0 libc-dev-bin libc6-dev libcap2 libcap2-bin #6 15.04 libcc1-0 libcrypt-dev libctf-nobfd0 libctf0 libcurl3-gnutls libcurl4 #6 15.04 libdpkg-perl libelf1 liberror-perl libexpat1 libfontconfig1 libfreetype6 #6 15.04 libgcc-9-dev libgd3 libgdbm-compat4 libgdbm6 libgomp1 libgssapi-krb5-2 #6 15.04 libgssapi3-heimdal libhcrypto4-heimdal libheimbase1-heimdal #6 15.04 libheimntlm0-heimdal libhx509-5-heimdal libicu66 libisl22 libitm1 libjbig0 #6 15.04 libjpeg-turbo8 libjpeg8 libjsoncpp1 libk5crypto3 libkeyutils1 #6 15.04 libkrb5-26-heimdal libkrb5-3 libkrb5support0 libldap-2.4-2 libldap-common #6 15.04 liblsan0 libmnl0 libmpc3 libmpfr6 libnghttp2-14 #6 15.04 libnginx-mod-http-image-filter libnginx-mod-http-xslt-filter #6 15.04 libnginx-mod-mail libnginx-mod-stream libperl5.30 libpng16-16 libpsl5 #6 15.04 libquadmath0 librhash0 libroken18-heimdal librtmp1 libsasl2-2 #6 15.04 libsasl2-modules-db libsqlite3-0 libssh-4 libssl1.1 libstdc++-9-dev libtiff5 #6 15.04 libtsan0 libubsan1 libuv1 libwebp6 libwind0-heimdal libx11-6 libx11-data #6 15.04 libxau6 libxcb1 libxdmcp6 libxml2 libxpm4 libxslt1.1 libxtables12 #6 15.04 linux-libc-dev make nginx-common nginx-core openssl patch perl #6 15.04 perl-modules-5.30 tzdata ucf xz-utils #6 15.04 Suggested packages: #6 15.04 binutils-doc cmake-doc ninja-build cpp-doc gcc-9-locales debian-keyring #6 15.04 g++-multilib g++-9-multilib gcc-9-doc gcc-multilib manpages-dev autoconf #6 15.04 automake libtool flex bison gdb gcc-doc gcc-9-multilib gettext-base #6 15.04 git-daemon-run | git-daemon-sysvinit git-doc git-el git-email git-gui gitk #6 15.04 gitweb git-cvs git-mediawiki git-svn iproute2-doc lrzip glibc-doc gnupg #6 15.04 | gnupg2 bzr libgd-tools gdbm-l10n krb5-doc krb5-user libstdc++-9-doc #6 15.04 make-doc fcgiwrap nginx-doc ssl-cert ed diffutils-doc perl-doc #6 15.04 libterm-readline-gnu-perl | libterm-readline-perl-perl libb-debug-perl #6 15.04 liblocale-codes-perl #6 15.04 Recommended packages: #6 15.04 fakeroot gnupg | gnupg2 libalgorithm-merge-perl less ssh-client libatm1 #6 15.04 manpages manpages-dev libpam-cap libfile-fcntllock-perl #6 15.04 liblocale-gettext-perl krb5-locales publicsuffix libsasl2-modules netbase #6 15.30 The following NEW packages will be installed: #6 15.30 binutils binutils-common binutils-x86-64-linux-gnu build-essential #6 15.30 ca-certificates cmake cmake-data cpp cpp-9 curl dpkg-dev fontconfig-config #6 15.30 fonts-dejavu-core g++ g++-9 gcc gcc-9 gcc-9-base git git-man iproute2 #6 15.30 libarchive13 libasan5 libasn1-8-heimdal libatomic1 libbinutils libbrotli1 #6 15.30 libbsd0 libc-dev-bin libc6-dev libcap2 libcap2-bin libcc1-0 libcrypt-dev #6 15.30 libctf-nobfd0 libctf0 libcurl3-gnutls libcurl4 libdpkg-perl libelf1 #6 15.30 liberror-perl libexpat1 libfontconfig1 libfreetype6 libgcc-9-dev libgd3 #6 15.30 libgdbm-compat4 libgdbm6 libgomp1 libgssapi-krb5-2 libgssapi3-heimdal #6 15.30 libhcrypto4-heimdal libheimbase1-heimdal libheimntlm0-heimdal #6 15.30 libhx509-5-heimdal libicu66 libisl22 libitm1 libjbig0 libjpeg-turbo8 #6 15.30 libjpeg8 libjsoncpp1 libk5crypto3 libkeyutils1 libkrb5-26-heimdal libkrb5-3 #6 15.30 libkrb5support0 libldap-2.4-2 libldap-common liblsan0 libmnl0 libmpc3 #6 15.30 libmpfr6 libnghttp2-14 libnginx-mod-http-image-filter #6 15.30 libnginx-mod-http-xslt-filter libnginx-mod-mail libnginx-mod-stream #6 15.30 libperl5.30 libpng16-16 libpsl5 libquadmath0 librhash0 libroken18-heimdal #6 15.30 librtmp1 libsasl2-2 libsasl2-modules-db libsqlite3-0 libssh-4 libssl1.1 #6 15.30 libstdc++-9-dev libtiff5 libtsan0 libubsan1 libuv1 libwebp6 libwind0-heimdal #6 15.30 libx11-6 libx11-data libxau6 libxcb1 libxdmcp6 libxml2 libxpm4 libxslt1.1 #6 15.30 libxtables12 linux-libc-dev make nginx nginx-common nginx-core openssl patch #6 15.30 perl perl-modules-5.30 tzdata ucf wget xz-utils #6 15.41 0 upgraded, 119 newly installed, 0 to remove and 0 not upgraded. #6 15.41 Need to get 82.1 MB of archives. #6 15.41 After this operation, 389 MB of additional disk space will be used. #6 15.41 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 perl-modules-5.30 all 5.30.0-9ubuntu0.5 [2739 kB] #6 16.07 Get:2 http://archive.ubuntu.com/ubuntu focal/main amd64 libgdbm6 amd64 1.18.1-5 [27.4 kB] #6 16.07 Get:3 http://archive.ubuntu.com/ubuntu focal/main amd64 libgdbm-compat4 amd64 1.18.1-5 [6244 B] #6 16.07 Get:4 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libperl5.30 amd64 5.30.0-9ubuntu0.5 [3941 kB] #6 16.76 Get:5 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 perl amd64 5.30.0-9ubuntu0.5 [224 kB] #6 16.79 Get:6 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libssl1.1 amd64 1.1.1f-1ubuntu2.22 [1323 kB] #6 17.04 Get:7 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openssl amd64 1.1.1f-1ubuntu2.22 [622 kB] #6 17.15 Get:8 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 ca-certificates all 20230311ubuntu0.20.04.1 [152 kB] #6 17.18 Get:9 http://archive.ubuntu.com/ubuntu focal/main amd64 libbsd0 amd64 0.10.0-1 [45.4 kB] #6 17.20 Get:10 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcap2 amd64 1:2.32-1ubuntu0.1 [15.8 kB] #6 17.20 Get:11 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libelf1 amd64 0.176-1.1ubuntu0.1 [44.2 kB] #6 17.33 Get:12 http://archive.ubuntu.com/ubuntu focal/main amd64 libmnl0 amd64 1.0.4-2 [12.3 kB] #6 17.38 Get:13 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxtables12 amd64 1.8.4-3ubuntu2.1 [28.7 kB] #6 17.42 Get:14 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcap2-bin amd64 1:2.32-1ubuntu0.1 [26.2 kB] #6 17.46 Get:15 http://archive.ubuntu.com/ubuntu focal/main amd64 iproute2 amd64 5.5.0-1ubuntu1 [858 kB] #6 20.61 Get:16 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libexpat1 amd64 2.2.9-1ubuntu0.6 [74.6 kB] #6 21.00 Get:17 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 tzdata all 2024a-0ubuntu0.20.04 [301 kB] #6 22.38 Get:18 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libicu66 amd64 66.1-2ubuntu2.1 [8515 kB] #6 51.24 Get:19 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsqlite3-0 amd64 3.31.1-4ubuntu0.6 [549 kB] #6 53.10 Get:20 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxml2 amd64 2.9.10+dfsg-5ubuntu0.20.04.7 [640 kB] #6 55.32 Get:21 http://archive.ubuntu.com/ubuntu focal/main amd64 ucf all 3.0038+nmu1 [51.6 kB] #6 55.50 Get:21 http://archive.ubuntu.com/ubuntu focal/main amd64 ucf all 3.0038+nmu1 [51.6 kB] #6 55.60 Get:22 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 xz-utils amd64 5.2.4-1ubuntu1.1 [82.6 kB] #6 55.66 Get:23 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkrb5support0 amd64 1.17-6ubuntu4.4 [31.0 kB] #6 55.70 Get:24 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libk5crypto3 amd64 1.17-6ubuntu4.4 [79.9 kB] #6 55.72 Get:25 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkeyutils1 amd64 1.6-6ubuntu1.1 [10.3 kB] #6 55.73 Get:26 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkrb5-3 amd64 1.17-6ubuntu4.4 [330 kB] #6 55.80 Get:27 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgssapi-krb5-2 amd64 1.17-6ubuntu4.4 [121 kB] #6 55.82 Get:28 http://archive.ubuntu.com/ubuntu focal/main amd64 libpng16-16 amd64 1.6.37-2 [179 kB] #6 55.86 Get:29 http://archive.ubuntu.com/ubuntu focal/main amd64 libpsl5 amd64 0.21.0-1ubuntu1 [51.5 kB] #6 55.86 Get:30 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56.46 Get:39 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libctf-nobfd0 amd64 2.34-6ubuntu1.9 [48.2 kB] #6 56.47 Get:40 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libctf0 amd64 2.34-6ubuntu1.9 [46.6 kB] #6 56.48 Get:41 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 binutils-x86-64-linux-gnu amd64 2.34-6ubuntu1.9 [1614 kB] #6 56.76 Get:42 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 binutils amd64 2.34-6ubuntu1.9 [3380 B] #6 56.85 Get:43 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libc-dev-bin amd64 2.31-0ubuntu9.15 [71.8 kB] #6 57.00 Get:44 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 linux-libc-dev amd64 5.4.0-182.202 [1134 kB] #6 57.28 Get:45 http://archive.ubuntu.com/ubuntu focal/main amd64 libcrypt-dev amd64 1:4.4.10-10ubuntu4 [104 kB] #6 57.30 Get:46 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libc6-dev amd64 2.31-0ubuntu9.15 [2519 kB] #6 57.75 Get:47 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 gcc-9-base amd64 9.4.0-1ubuntu1~20.04.2 [18.9 kB] #6 57.75 Get:48 http://archive.ubuntu.com/ubuntu focal/main amd64 libisl22 amd64 0.22.1-1 [592 kB] #6 57.87 Get:49 http://archive.ubuntu.com/ubuntu focal/main amd64 libmpfr6 amd64 4.0.2-1 [240 kB] #6 57.91 Get:50 http://archive.ubuntu.com/ubuntu focal/main amd64 libmpc3 amd64 1.1.0-1 [40.8 kB] #6 57.92 Get:51 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 cpp-9 amd64 9.4.0-1ubuntu1~20.04.2 [7502 kB] #6 59.27 Get:52 http://archive.ubuntu.com/ubuntu focal/main amd64 cpp amd64 4:9.3.0-1ubuntu2 [27.6 kB] #6 59.27 Get:53 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcc1-0 amd64 10.5.0-1ubuntu1~20.04 [48.8 kB] #6 59.36 Get:54 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgomp1 amd64 10.5.0-1ubuntu1~20.04 [102 kB] #6 59.54 Get:55 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libitm1 amd64 10.5.0-1ubuntu1~20.04 [26.2 kB] #6 59.55 Get:56 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http://archive.ubuntu.com/ubuntu focal/main amd64 gcc amd64 4:9.3.0-1ubuntu2 [5208 B] #6 62.91 Get:65 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libstdc++-9-dev amd64 9.4.0-1ubuntu1~20.04.2 [1722 kB] #6 63.39 Get:66 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 g++-9 amd64 9.4.0-1ubuntu1~20.04.2 [8421 kB] #6 65.04 Get:67 http://archive.ubuntu.com/ubuntu focal/main amd64 g++ amd64 4:9.3.0-1ubuntu2 [1604 B] #6 65.06 Get:68 http://archive.ubuntu.com/ubuntu focal/main amd64 make amd64 4.2.1-1.2 [162 kB] #6 65.07 Get:69 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdpkg-perl all 1.19.7ubuntu3.2 [231 kB] #6 65.16 Get:70 http://archive.ubuntu.com/ubuntu focal/main amd64 patch amd64 2.7.6-6 [105 kB] #6 65.20 Get:71 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 dpkg-dev all 1.19.7ubuntu3.2 [679 kB] #6 65.32 Get:72 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 build-essential amd64 12.8ubuntu1.1 [4664 B] #6 65.32 Get:73 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66.71 Get:81 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libhx509-5-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [107 kB] #6 67.13 Get:82 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkrb5-26-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [207 kB] #6 67.89 Get:83 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libheimntlm0-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [15.1 kB] #6 67.92 Get:84 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgssapi3-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [96.5 kB] #6 68.05 Get:85 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsasl2-modules-db amd64 2.1.27+dfsg-2ubuntu0.1 [14.7 kB] #6 68.06 Get:86 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsasl2-2 amd64 2.1.27+dfsg-2ubuntu0.1 [49.3 kB] #6 68.29 Get:87 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libldap-common all 2.4.49+dfsg-2ubuntu1.10 [16.5 kB] #6 68.38 Get:88 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libldap-2.4-2 amd64 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1.18.0-0ubuntu1.4 [14.8 kB] #6 71.41 Get:114 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxslt1.1 amd64 1.1.34-4ubuntu0.20.04.1 [151 kB] #6 71.43 Get:115 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnginx-mod-http-xslt-filter amd64 1.18.0-0ubuntu1.4 [13.0 kB] #6 71.44 Get:116 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnginx-mod-mail amd64 1.18.0-0ubuntu1.4 [42.9 kB] #6 71.47 Get:117 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnginx-mod-stream amd64 1.18.0-0ubuntu1.4 [67.4 kB] #6 71.49 Get:118 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 nginx-core amd64 1.18.0-0ubuntu1.4 [425 kB] #6 71.58 Get:119 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 nginx all 1.18.0-0ubuntu1.4 [3620 B] #6 71.74 debconf: delaying package configuration, since apt-utils is not installed #6 71.80 Fetched 82.1 MB in 56s (1459 kB/s) #6 71.85 Selecting previously unselected package perl-modules-5.30. #6 71.85 (Reading database 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75.93 Preparing to unpack .../022-libkrb5support0_1.17-6ubuntu4.4_amd64.deb ... #6 75.93 Unpacking libkrb5support0:amd64 (1.17-6ubuntu4.4) ... #6 76.00 Selecting previously unselected package libk5crypto3:amd64. #6 76.01 Preparing to unpack .../023-libk5crypto3_1.17-6ubuntu4.4_amd64.deb ... #6 76.01 Unpacking libk5crypto3:amd64 (1.17-6ubuntu4.4) ... #6 76.10 Selecting previously unselected package libkeyutils1:amd64. #6 76.10 Preparing to unpack .../024-libkeyutils1_1.6-6ubuntu1.1_amd64.deb ... #6 76.11 Unpacking libkeyutils1:amd64 (1.6-6ubuntu1.1) ... #6 76.17 Selecting previously unselected package libkrb5-3:amd64. #6 76.18 Preparing to unpack .../025-libkrb5-3_1.17-6ubuntu4.4_amd64.deb ... #6 76.18 Unpacking libkrb5-3:amd64 (1.17-6ubuntu4.4) ... #6 76.31 Selecting previously unselected package libgssapi-krb5-2:amd64. #6 76.31 Preparing to unpack .../026-libgssapi-krb5-2_1.17-6ubuntu4.4_amd64.deb ... #6 76.32 Unpacking libgssapi-krb5-2:amd64 (1.17-6ubuntu4.4) ... #6 76.41 Selecting previously unselected package libpng16-16:amd64. #6 76.41 Preparing to unpack .../027-libpng16-16_1.6.37-2_amd64.deb ... #6 76.42 Unpacking libpng16-16:amd64 (1.6.37-2) ... #6 76.52 Selecting previously unselected package libpsl5:amd64. #6 76.52 Preparing to unpack .../028-libpsl5_0.21.0-1ubuntu1_amd64.deb ... #6 76.53 Unpacking libpsl5:amd64 (0.21.0-1ubuntu1) ... #6 76.61 Selecting previously unselected package libuv1:amd64. #6 76.61 Preparing to unpack .../029-libuv1_1.34.2-1ubuntu1.5_amd64.deb ... #6 76.62 Unpacking libuv1:amd64 (1.34.2-1ubuntu1.5) ... #6 76.70 Selecting previously unselected package libxau6:amd64. #6 76.71 Preparing to unpack .../030-libxau6_1%3a1.0.9-0ubuntu1_amd64.deb ... #6 76.71 Unpacking libxau6:amd64 (1:1.0.9-0ubuntu1) ... #6 76.78 Selecting previously unselected package libxdmcp6:amd64. #6 76.79 Preparing to unpack .../031-libxdmcp6_1%3a1.1.3-0ubuntu1_amd64.deb ... #6 76.79 Unpacking libxdmcp6:amd64 (1:1.1.3-0ubuntu1) ... #6 76.86 Selecting previously unselected package libxcb1:amd64. #6 76.87 Preparing to unpack .../032-libxcb1_1.14-2_amd64.deb ... #6 76.87 Unpacking libxcb1:amd64 (1.14-2) ... #6 76.95 Selecting previously unselected package libx11-data. #6 76.95 Preparing to unpack .../033-libx11-data_2%3a1.6.9-2ubuntu1.6_all.deb ... #6 76.96 Unpacking libx11-data (2:1.6.9-2ubuntu1.6) ... #6 77.08 Selecting previously unselected package libx11-6:amd64. #6 77.08 Preparing to unpack .../034-libx11-6_2%3a1.6.9-2ubuntu1.6_amd64.deb ... #6 77.09 Unpacking libx11-6:amd64 (2:1.6.9-2ubuntu1.6) ... #6 77.22 Selecting previously unselected package wget. #6 77.22 Preparing to unpack .../035-wget_1.20.3-1ubuntu2_amd64.deb ... #6 77.23 Unpacking wget (1.20.3-1ubuntu2) ... #6 77.33 Selecting previously unselected package binutils-common:amd64. #6 77.33 Preparing to unpack .../036-binutils-common_2.34-6ubuntu1.9_amd64.deb ... #6 77.34 Unpacking binutils-common:amd64 (2.34-6ubuntu1.9) ... #6 77.44 Selecting previously unselected package 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Selecting previously unselected package libc-dev-bin. #6 78.12 Preparing to unpack .../042-libc-dev-bin_2.31-0ubuntu9.15_amd64.deb ... #6 78.13 Unpacking libc-dev-bin (2.31-0ubuntu9.15) ... #6 78.20 Selecting previously unselected package linux-libc-dev:amd64. #6 78.21 Preparing to unpack .../043-linux-libc-dev_5.4.0-182.202_amd64.deb ... #6 78.21 Unpacking linux-libc-dev:amd64 (5.4.0-182.202) ... #6 78.41 Selecting previously unselected package libcrypt-dev:amd64. #6 78.42 Preparing to unpack .../044-libcrypt-dev_1%3a4.4.10-10ubuntu4_amd64.deb ... #6 78.42 Unpacking libcrypt-dev:amd64 (1:4.4.10-10ubuntu4) ... #6 78.50 Selecting previously unselected package libc6-dev:amd64. #6 78.51 Preparing to unpack .../045-libc6-dev_2.31-0ubuntu9.15_amd64.deb ... #6 78.51 Unpacking libc6-dev:amd64 (2.31-0ubuntu9.15) ... #6 78.88 Selecting previously unselected package gcc-9-base:amd64. #6 78.88 Preparing to unpack .../046-gcc-9-base_9.4.0-1ubuntu1~20.04.2_amd64.deb ... #6 78.89 Unpacking gcc-9-base:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 78.97 Selecting previously unselected package libisl22:amd64. #6 78.97 Preparing to unpack .../047-libisl22_0.22.1-1_amd64.deb ... #6 78.98 Unpacking libisl22:amd64 (0.22.1-1) ... #6 79.12 Selecting previously unselected package libmpfr6:amd64. #6 79.12 Preparing to unpack .../048-libmpfr6_4.0.2-1_amd64.deb ... #6 79.13 Unpacking libmpfr6:amd64 (4.0.2-1) ... #6 79.24 Selecting previously unselected package libmpc3:amd64. #6 79.24 Preparing to unpack .../049-libmpc3_1.1.0-1_amd64.deb ... #6 79.25 Unpacking libmpc3:amd64 (1.1.0-1) ... #6 79.32 Selecting previously unselected package cpp-9. #6 79.32 Preparing to unpack .../050-cpp-9_9.4.0-1ubuntu1~20.04.2_amd64.deb ... #6 79.33 Unpacking cpp-9 (9.4.0-1ubuntu1~20.04.2) ... #6 80.06 Selecting previously unselected package cpp. #6 80.06 Preparing to unpack .../051-cpp_4%3a9.3.0-1ubuntu2_amd64.deb ... #6 80.07 Unpacking cpp (4:9.3.0-1ubuntu2) ... #6 80.14 Selecting previously unselected package libcc1-0:amd64. #6 80.14 Preparing to unpack .../052-libcc1-0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.15 Unpacking libcc1-0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.23 Selecting previously unselected package libgomp1:amd64. #6 80.23 Preparing to unpack .../053-libgomp1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.24 Unpacking libgomp1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.34 Selecting previously unselected package libitm1:amd64. #6 80.34 Preparing to unpack .../054-libitm1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.35 Unpacking libitm1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.43 Selecting previously unselected package libatomic1:amd64. #6 80.43 Preparing to unpack .../055-libatomic1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.44 Unpacking libatomic1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.51 Selecting previously unselected package libasan5:amd64. #6 80.51 Preparing to unpack .../056-libasan5_9.4.0-1ubuntu1~20.04.2_amd64.deb ... #6 80.52 Unpacking libasan5:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 80.87 Selecting previously unselected package liblsan0:amd64. #6 80.88 Preparing to unpack .../057-liblsan0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.88 Unpacking liblsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.05 Selecting previously unselected package libtsan0:amd64. #6 81.05 Preparing to unpack .../058-libtsan0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 81.06 Unpacking libtsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.35 Selecting previously unselected package libubsan1:amd64. #6 81.35 Preparing to unpack .../059-libubsan1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 81.36 Unpacking libubsan1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.52 Selecting previously unselected package libquadmath0:amd64. #6 81.52 Preparing to unpack .../060-libquadmath0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 81.53 Unpacking libquadmath0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.62 Selecting previously unselected package libgcc-9-dev:amd64. #6 81.62 Preparing to unpack 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Selecting previously unselected package cmake-data. #6 84.52 Preparing to unpack .../072-cmake-data_3.16.3-1ubuntu1.20.04.1_all.deb ... #6 84.54 Unpacking cmake-data (3.16.3-1ubuntu1.20.04.1) ... #6 84.99 Selecting previously unselected package libarchive13:amd64. #6 84.99 Preparing to unpack .../073-libarchive13_3.4.0-2ubuntu1.2_amd64.deb ... #6 85.00 Unpacking libarchive13:amd64 (3.4.0-2ubuntu1.2) ... #6 85.12 Selecting previously unselected package libbrotli1:amd64. #6 85.12 Preparing to unpack .../074-libbrotli1_1.0.7-6ubuntu0.1_amd64.deb ... #6 85.13 Unpacking libbrotli1:amd64 (1.0.7-6ubuntu0.1) ... #6 85.24 Selecting previously unselected package libroken18-heimdal:amd64. #6 85.25 Preparing to unpack .../075-libroken18-heimdal_7.7.0+dfsg-1ubuntu1.4_amd64.deb ... #6 85.25 Unpacking libroken18-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 85.33 Selecting previously unselected package libasn1-8-heimdal:amd64. #6 85.33 Preparing to unpack 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Unpacking libcurl3-gnutls:amd64 (7.68.0-1ubuntu2.22) ... #6 88.11 Selecting previously unselected package liberror-perl. #6 88.12 Preparing to unpack .../099-liberror-perl_0.17029-1_all.deb ... #6 88.12 Unpacking liberror-perl (0.17029-1) ... #6 88.19 Selecting previously unselected package git-man. #6 88.19 Preparing to unpack .../100-git-man_1%3a2.25.1-1ubuntu3.11_all.deb ... #6 88.20 Unpacking git-man (1:2.25.1-1ubuntu3.11) ... #6 88.37 Selecting previously unselected package git. #6 88.38 Preparing to unpack .../101-git_1%3a2.25.1-1ubuntu3.11_amd64.deb ... #6 88.43 Unpacking git (1:2.25.1-1ubuntu3.11) ... #6 88.98 Selecting previously unselected package libfreetype6:amd64. #6 88.99 Preparing to unpack .../102-libfreetype6_2.10.1-2ubuntu0.3_amd64.deb ... #6 89.00 Unpacking libfreetype6:amd64 (2.10.1-2ubuntu0.3) ... #6 89.12 Selecting previously unselected package libfontconfig1:amd64. #6 89.12 Preparing to unpack .../103-libfontconfig1_2.13.1-2ubuntu3_amd64.deb ... #6 89.13 Unpacking libfontconfig1:amd64 (2.13.1-2ubuntu3) ... #6 89.23 Selecting previously unselected package libjpeg-turbo8:amd64. #6 89.23 Preparing to unpack .../104-libjpeg-turbo8_2.0.3-0ubuntu1.20.04.3_amd64.deb ... #6 89.24 Unpacking libjpeg-turbo8:amd64 (2.0.3-0ubuntu1.20.04.3) ... #6 89.32 Selecting previously unselected package libjpeg8:amd64. #6 89.33 Preparing to unpack .../105-libjpeg8_8c-2ubuntu8_amd64.deb ... #6 89.33 Unpacking libjpeg8:amd64 (8c-2ubuntu8) ... #6 89.40 Selecting previously unselected package libjbig0:amd64. #6 89.40 Preparing to unpack .../106-libjbig0_2.1-3.1ubuntu0.20.04.1_amd64.deb ... #6 89.41 Unpacking libjbig0:amd64 (2.1-3.1ubuntu0.20.04.1) ... #6 89.49 Selecting previously unselected package libwebp6:amd64. #6 89.49 Preparing to unpack .../107-libwebp6_0.6.1-2ubuntu0.20.04.3_amd64.deb ... #6 89.50 Unpacking libwebp6:amd64 (0.6.1-2ubuntu0.20.04.3) ... #6 89.60 Selecting previously unselected package libtiff5:amd64. #6 89.60 Preparing to unpack .../108-libtiff5_4.1.0+git191117-2ubuntu0.20.04.12_amd64.deb ... #6 89.61 Unpacking libtiff5:amd64 (4.1.0+git191117-2ubuntu0.20.04.12) ... #6 89.71 Selecting previously unselected package libxpm4:amd64. #6 89.71 Preparing to unpack .../109-libxpm4_1%3a3.5.12-1ubuntu0.20.04.2_amd64.deb ... #6 89.72 Unpacking libxpm4:amd64 (1:3.5.12-1ubuntu0.20.04.2) ... #6 89.80 Selecting previously unselected package libgd3:amd64. #6 89.80 Preparing to unpack .../110-libgd3_2.2.5-5.2ubuntu2.1_amd64.deb ... #6 89.81 Unpacking libgd3:amd64 (2.2.5-5.2ubuntu2.1) ... #6 89.92 Selecting previously unselected package nginx-common. #6 89.92 Preparing to unpack .../111-nginx-common_1.18.0-0ubuntu1.4_all.deb ... #6 89.99 Unpacking nginx-common (1.18.0-0ubuntu1.4) ... #6 90.08 Selecting previously unselected package libnginx-mod-http-image-filter. #6 90.08 Preparing to unpack .../112-libnginx-mod-http-image-filter_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.09 Unpacking libnginx-mod-http-image-filter (1.18.0-0ubuntu1.4) ... #6 90.17 Selecting previously unselected package libxslt1.1:amd64. #6 90.17 Preparing to unpack .../113-libxslt1.1_1.1.34-4ubuntu0.20.04.1_amd64.deb ... #6 90.18 Unpacking libxslt1.1:amd64 (1.1.34-4ubuntu0.20.04.1) ... #6 90.28 Selecting previously unselected package libnginx-mod-http-xslt-filter. #6 90.28 Preparing to unpack .../114-libnginx-mod-http-xslt-filter_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.29 Unpacking libnginx-mod-http-xslt-filter (1.18.0-0ubuntu1.4) ... #6 90.37 Selecting previously unselected package libnginx-mod-mail. #6 90.37 Preparing to unpack .../115-libnginx-mod-mail_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.38 Unpacking libnginx-mod-mail (1.18.0-0ubuntu1.4) ... #6 90.46 Selecting previously unselected package libnginx-mod-stream. #6 90.46 Preparing to unpack .../116-libnginx-mod-stream_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.47 Unpacking libnginx-mod-stream (1.18.0-0ubuntu1.4) ... #6 90.56 Selecting previously unselected package nginx-core. #6 90.56 Preparing to unpack .../117-nginx-core_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.57 Unpacking nginx-core (1.18.0-0ubuntu1.4) ... #6 90.69 Selecting previously unselected package nginx. #6 90.70 Preparing to unpack .../118-nginx_1.18.0-0ubuntu1.4_all.deb ... #6 90.71 Unpacking nginx (1.18.0-0ubuntu1.4) ... #6 90.79 Setting up libexpat1:amd64 (2.2.9-1ubuntu0.6) ... #6 90.81 Setting up libxau6:amd64 (1:1.0.9-0ubuntu1) ... #6 90.84 Setting up libkeyutils1:amd64 (1.6-6ubuntu1.1) ... #6 90.86 Setting up libpsl5:amd64 (0.21.0-1ubuntu1) ... #6 90.88 Setting up perl-modules-5.30 (5.30.0-9ubuntu0.5) ... #6 90.90 Setting up libssl1.1:amd64 (1.1.1f-1ubuntu2.22) ... #6 91.02 Setting up libbrotli1:amd64 (1.0.7-6ubuntu0.1) ... #6 91.04 Setting up libsqlite3-0:amd64 (3.31.1-4ubuntu0.6) ... #6 91.06 Setting up binutils-common:amd64 (2.34-6ubuntu1.9) ... #6 91.08 Setting up libnghttp2-14:amd64 (1.40.0-1ubuntu0.3) ... #6 91.11 Setting up linux-libc-dev:amd64 (5.4.0-182.202) ... #6 91.13 Setting up libctf-nobfd0:amd64 (2.34-6ubuntu1.9) ... #6 91.15 Setting up nginx-common (1.18.0-0ubuntu1.4) ... #6 91.69 Setting up libgomp1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 91.71 Setting up libldap-common (2.4.49+dfsg-2ubuntu1.10) ... #6 91.74 Setting up libjbig0:amd64 (2.1-3.1ubuntu0.20.04.1) ... #6 91.76 Setting up libcap2:amd64 (1:2.32-1ubuntu0.1) ... #6 91.79 Setting up libkrb5support0:amd64 (1.17-6ubuntu4.4) ... #6 91.81 Setting up libsasl2-modules-db:amd64 (2.1.27+dfsg-2ubuntu0.1) ... #6 91.83 Setting up tzdata (2024a-0ubuntu0.20.04) ... #6 91.96 #6 91.96 Current default time zone: 'Etc/UTC' #6 91.96 Local time is now: Thu May 16 20:22:03 UTC 2024. #6 91.96 Universal Time is now: Thu May 16 20:22:03 UTC 2024. #6 91.96 Run 'dpkg-reconfigure tzdata' if you wish to change it. #6 91.96 #6 91.99 Setting up libcap2-bin (1:2.32-1ubuntu0.1) ... #6 92.01 Setting up libuv1:amd64 (1.34.2-1ubuntu1.5) ... #6 92.03 Setting up libx11-data (2:1.6.9-2ubuntu1.6) ... #6 92.05 Setting up make (4.2.1-1.2) ... #6 92.08 Setting up libmpfr6:amd64 (4.0.2-1) ... #6 92.10 Setting up librtmp1:amd64 (2.4+20151223.gitfa8646d.1-2build1) ... #6 92.12 Setting up xz-utils (5.2.4-1ubuntu1.1) ... #6 92.14 update-alternatives: using /usr/bin/xz to provide /usr/bin/lzma (lzma) in auto mode #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzma.1.gz because associated file /usr/share/man/man1/xz.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/unlzma.1.gz because associated file /usr/share/man/man1/unxz.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzcat.1.gz because associated file /usr/share/man/man1/xzcat.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzmore.1.gz because associated file /usr/share/man/man1/xzmore.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzless.1.gz because associated file /usr/share/man/man1/xzless.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzdiff.1.gz because associated file /usr/share/man/man1/xzdiff.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzcmp.1.gz because associated file /usr/share/man/man1/xzcmp.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzgrep.1.gz because associated file /usr/share/man/man1/xzgrep.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzegrep.1.gz because associated file /usr/share/man/man1/xzegrep.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzfgrep.1.gz because associated file /usr/share/man/man1/xzfgrep.1.gz (of link group lzma) doesn't exist #6 92.16 Setting up libquadmath0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.18 Setting up libpng16-16:amd64 (1.6.37-2) ... #6 92.20 Setting up libmpc3:amd64 (1.1.0-1) ... #6 92.22 Setting up libmnl0:amd64 (1.0.4-2) ... #6 92.24 Setting up libatomic1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.26 Setting up patch (2.7.6-6) ... #6 92.28 Setting up libwebp6:amd64 (0.6.1-2ubuntu0.20.04.3) ... #6 92.30 Setting up fonts-dejavu-core (2.37-1) ... #6 92.40 Setting up ucf (3.0038+nmu1) ... #6 92.53 Setting up libk5crypto3:amd64 (1.17-6ubuntu4.4) ... #6 92.55 Setting up libjpeg-turbo8:amd64 (2.0.3-0ubuntu1.20.04.3) ... #6 92.57 Setting up libxtables12:amd64 (1.8.4-3ubuntu2.1) ... #6 92.59 Setting up libsasl2-2:amd64 (2.1.27+dfsg-2ubuntu0.1) ... #6 92.61 Setting up libroken18-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 92.64 Setting up libubsan1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.66 Setting up librhash0:amd64 (1.3.9-1) ... #6 92.68 Setting up libcrypt-dev:amd64 (1:4.4.10-10ubuntu4) ... #6 92.70 Setting up git-man (1:2.25.1-1ubuntu3.11) ... #6 92.72 Setting up libisl22:amd64 (0.22.1-1) ... #6 92.74 Setting up cmake-data (3.16.3-1ubuntu1.20.04.1) ... #6 92.77 Setting up libkrb5-3:amd64 (1.17-6ubuntu4.4) ... #6 92.79 Setting up libbinutils:amd64 (2.34-6ubuntu1.9) ... #6 92.81 Setting up libc-dev-bin (2.31-0ubuntu9.15) ... #6 92.83 Setting up openssl (1.1.1f-1ubuntu2.22) ... #6 92.86 Setting up libbsd0:amd64 (0.10.0-1) ... #6 92.88 Setting up libelf1:amd64 (0.176-1.1ubuntu0.1) ... #6 92.91 Setting up libcc1-0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.93 Setting up liblsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.95 Setting up libitm1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.97 Setting up libjsoncpp1:amd64 (1.7.4-3.1ubuntu2) ... #6 92.99 Setting up libheimbase1-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 93.01 Setting up libgdbm6:amd64 (1.18.1-5) ... #6 93.03 Setting up gcc-9-base:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 93.05 Setting up libtsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 93.07 Setting up libctf0:amd64 (2.34-6ubuntu1.9) ... #6 93.09 Setting up libjpeg8:amd64 (8c-2ubuntu8) ... #6 93.11 Setting up libnginx-mod-mail (1.18.0-0ubuntu1.4) ... #6 93.16 Setting up libxdmcp6:amd64 (1:1.1.3-0ubuntu1) ... #6 93.18 Setting up libxcb1:amd64 (1.14-2) ... #6 93.20 Setting up wget (1.20.3-1ubuntu2) ... #6 93.22 Setting up fontconfig-config (2.13.1-2ubuntu3) ... #6 93.55 Setting up iproute2 (5.5.0-1ubuntu1) ... #6 93.75 Setting up libicu66:amd64 (66.1-2ubuntu2.1) ... #6 93.77 Setting up libasn1-8-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 93.79 Setting up libnginx-mod-stream (1.18.0-0ubuntu1.4) ... #6 93.84 Setting up libhcrypto4-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 93.86 Setting up ca-certificates (20230311ubuntu0.20.04.1) ... #6 95.13 Updating certificates in /etc/ssl/certs... #6 96.88 137 added, 0 removed; done. #6 96.95 Setting up libasan5:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 96.98 Setting up libwind0-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.00 Setting up libfreetype6:amd64 (2.10.1-2ubuntu0.3) ... #6 97.02 Setting up libgssapi-krb5-2:amd64 (1.17-6ubuntu4.4) ... #6 97.04 Setting up libgdbm-compat4:amd64 (1.18.1-5) ... #6 97.06 Setting up libssh-4:amd64 (0.9.3-2ubuntu2.5) ... #6 97.08 Setting up cpp-9 (9.4.0-1ubuntu1~20.04.2) ... #6 97.10 Setting up libperl5.30:amd64 (5.30.0-9ubuntu0.5) ... #6 97.12 Setting up libc6-dev:amd64 (2.31-0ubuntu9.15) ... #6 97.14 Setting up libx11-6:amd64 (2:1.6.9-2ubuntu1.6) ... #6 97.16 Setting up libtiff5:amd64 (4.1.0+git191117-2ubuntu0.20.04.12) ... #6 97.18 Setting up libfontconfig1:amd64 (2.13.1-2ubuntu3) ... #6 97.20 Setting up libxml2:amd64 (2.9.10+dfsg-5ubuntu0.20.04.7) ... #6 97.22 Setting up binutils-x86-64-linux-gnu (2.34-6ubuntu1.9) ... #6 97.24 Setting up libxpm4:amd64 (1:3.5.12-1ubuntu0.20.04.2) ... #6 97.26 Setting up libarchive13:amd64 (3.4.0-2ubuntu1.2) ... #6 97.28 Setting up libhx509-5-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.30 Setting up binutils (2.34-6ubuntu1.9) ... #6 97.32 Setting up libgcc-9-dev:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 97.34 Setting up perl (5.30.0-9ubuntu0.5) ... #6 97.39 Setting up libgd3:amd64 (2.2.5-5.2ubuntu2.1) ... #6 97.41 Setting up libdpkg-perl (1.19.7ubuntu3.2) ... #6 97.43 Setting up cpp (4:9.3.0-1ubuntu2) ... #6 97.46 Setting up libxslt1.1:amd64 (1.1.34-4ubuntu0.20.04.1) ... #6 97.48 Setting up libkrb5-26-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.50 Setting up gcc-9 (9.4.0-1ubuntu1~20.04.2) ... #6 97.52 Setting up libstdc++-9-dev:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 97.54 Setting up libnginx-mod-http-image-filter (1.18.0-0ubuntu1.4) ... #6 97.59 Setting up libheimntlm0-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.61 Setting up gcc (4:9.3.0-1ubuntu2) ... #6 97.65 Setting up dpkg-dev (1.19.7ubuntu3.2) ... #6 97.69 Setting up liberror-perl (0.17029-1) ... #6 97.71 Setting up libnginx-mod-http-xslt-filter (1.18.0-0ubuntu1.4) ... #6 97.75 Setting up libgssapi3-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.77 Setting up g++-9 (9.4.0-1ubuntu1~20.04.2) ... #6 97.79 Setting up g++ (4:9.3.0-1ubuntu2) ... #6 97.85 update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode #6 97.85 update-alternatives: warning: skip creation of /usr/share/man/man1/c++.1.gz because associated file /usr/share/man/man1/g++.1.gz (of link group c++) doesn't exist #6 97.87 Setting up build-essential (12.8ubuntu1.1) ... #6 97.89 Setting up nginx-core (1.18.0-0ubuntu1.4) ... #6 97.98 invoke-rc.d: could not determine current runlevel #6 97.99 invoke-rc.d: policy-rc.d denied execution of start. #6 98.00 Setting up nginx (1.18.0-0ubuntu1.4) ... #6 98.02 Setting up libldap-2.4-2:amd64 (2.4.49+dfsg-2ubuntu1.10) ... #6 98.04 Setting up libcurl3-gnutls:amd64 (7.68.0-1ubuntu2.22) ... #6 98.06 Setting up git (1:2.25.1-1ubuntu3.11) ... #6 98.14 Setting up libcurl4:amd64 (7.68.0-1ubuntu2.22) ... #6 98.16 Setting up curl (7.68.0-1ubuntu2.22) ... #6 98.18 Setting up cmake (3.16.3-1ubuntu1.20.04.1) ... #6 98.20 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #6 98.25 Processing triggers for ca-certificates (20230311ubuntu0.20.04.1) ... #6 98.26 Updating certificates in /etc/ssl/certs... #6 99.63 0 added, 0 removed; done. #6 99.63 Running hooks in /etc/ca-certificates/update.d... #6 99.63 done. #6 DONE 100.6s #7 [ 3/17] RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev #7 0.464 Reading package lists... #7 1.538 Building dependency tree... #7 1.711 Reading state information... #7 1.915 curl is already the newest version (7.68.0-1ubuntu2.22). #7 1.915 tzdata is already the newest version (2024a-0ubuntu0.20.04). #7 1.915 tzdata set to manually installed. #7 1.915 wget is already the newest version (1.20.3-1ubuntu2). #7 1.915 xz-utils is already the newest version (5.2.4-1ubuntu1.1). #7 1.915 xz-utils set to manually installed. #7 1.915 The following additional packages will be installed: #7 1.915 binfmt-support bzip2-doc file icu-devtools libclang-cpp10 libdrm-amdgpu1 #7 1.915 libdrm-common libdrm-intel1 libdrm-nouveau2 libdrm-radeon1 libdrm2 libedit2 #7 1.915 libevent-2.1-7 libexpat1-dev libfontconfig1-dev libfontenc1 libfreetype-dev #7 1.915 libfreetype6-dev libgcrypt20-dev libgl1 libgl1-mesa-dri libglapi-mesa #7 1.915 libglib2.0-0 libglib2.0-data libglvnd0 libglx-mesa0 libglx0 libgmp-dev #7 1.915 libgmpxx4ldbl libgnutls-dane0 libgnutls-openssl27 libgnutls28-dev #7 1.915 libgnutlsxx28 libgpg-error-dev libice-dev libice6 libicu-dev libidn2-dev #7 1.915 libllvm10 libllvm12 libmagic-mgc libmagic1 libmpdec2 libncurses-dev libnspr4 #7 1.915 libnspr4-dev libnss3 libnss3-dev libp11-kit-dev libpciaccess0 libpfm4 #7 1.915 libpipeline1 libpng-dev libpng-tools libpthread-stubs0-dev libpython3-stdlib #7 1.915 libpython3.8-minimal libpython3.8-stdlib libreadline8 libsensors-config #7 1.915 libsensors5 libsm-dev libsm6 libtasn1-6-dev libtasn1-doc libtcl8.6 libtk8.6 #7 1.915 libunbound8 libutempter0 libvulkan1 libwayland-client0 libx11-dev #7 1.915 libx11-xcb1 libxau-dev libxaw7 libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 #7 1.915 libxcb-present0 libxcb-randr0 libxcb-shape0 libxcb-shm0 libxcb-sync1 #7 1.915 libxcb-xfixes0 libxcb1-dev libxcomposite1 libxdmcp-dev libxext-dev libxext6 #7 1.915 libxfixes3 libxft-dev libxft2 libxi6 libxinerama1 libxkbfile1 libxmlsec1 #7 1.915 libxmlsec1-gcrypt libxmlsec1-gnutls libxmlsec1-nss libxmlsec1-openssl #7 1.915 libxmu6 libxmuu1 libxrandr2 libxrender-dev libxrender1 libxshmfence1 #7 1.915 libxslt1-dev libxss-dev libxss1 libxt-dev libxt6 libxtst6 libxv1 #7 1.915 libxxf86dga1 libxxf86vm1 libyaml-0-2 libz3-4 libz3-dev llvm-10 llvm-10-dev #7 1.915 llvm-10-runtime llvm-10-tools llvm-runtime mesa-vulkan-drivers mime-support #7 1.915 nettle-dev pkg-config python3 python3-minimal python3-pkg-resources #7 1.915 python3-pygments python3-yaml python3.8 python3.8-minimal readline-common #7 1.915 shared-mime-info tcl tcl-dev tcl8.6 tcl8.6-dev tk tk8.6 tk8.6-dev uuid-dev #7 1.915 x11-common x11-utils x11proto-core-dev x11proto-dev x11proto-scrnsaver-dev #7 1.915 x11proto-xext-dev xbitmaps xdg-user-dirs xorg-sgml-doctools xterm xtrans-dev #7 1.917 Suggested packages: #7 1.917 freetype2-doc libgcrypt20-doc gmp-doc libgmp10-doc libmpfr-dev dns-root-data #7 1.917 gnutls-bin gnutls-doc libice-doc icu-doc liblzma-doc ncurses-doc pciutils #7 1.917 readline-doc lm-sensors libsm-doc sqlite3-doc libssl-doc libx11-doc #7 1.917 libxcb-doc libxext-doc libxt-doc llvm-10-doc python3-doc python3-tk #7 1.917 python3-venv python3-setuptools python-pygments-doc ttf-bitstream-vera #7 1.917 python3.8-venv python3.8-doc tcl-doc tcl-tclreadline tcl8.6-doc tk-doc #7 1.917 tk8.6-doc mesa-utils xfonts-cyrillic #7 2.228 The following NEW packages will be installed: #7 2.228 binfmt-support bzip2-doc file icu-devtools libbz2-dev libclang-cpp10 #7 2.228 libdrm-amdgpu1 libdrm-common libdrm-intel1 libdrm-nouveau2 libdrm-radeon1 #7 2.228 libdrm2 libedit2 libevent-2.1-7 libexpat1-dev libffi-dev libfontconfig1-dev #7 2.228 libfontenc1 libfreetype-dev libfreetype6-dev libgcrypt20-dev libgl1 #7 2.228 libgl1-mesa-dri libglapi-mesa libglib2.0-0 libglib2.0-data libglvnd0 #7 2.228 libglx-mesa0 libglx0 libgmp-dev libgmpxx4ldbl libgnutls-dane0 #7 2.228 libgnutls-openssl27 libgnutls28-dev libgnutlsxx28 libgpg-error-dev #7 2.228 libice-dev libice6 libicu-dev libidn2-dev libllvm10 libllvm12 liblzma-dev #7 2.228 libmagic-mgc libmagic1 libmpdec2 libncurses-dev libncursesw5-dev libnspr4 #7 2.228 libnspr4-dev libnss3 libnss3-dev libp11-kit-dev libpciaccess0 libpfm4 #7 2.228 libpipeline1 libpng-dev libpng-tools libpthread-stubs0-dev libpython3-stdlib #7 2.229 libpython3.8-minimal libpython3.8-stdlib libreadline-dev libreadline8 #7 2.229 libsensors-config libsensors5 libsm-dev libsm6 libsqlite3-dev libssl-dev #7 2.229 libtasn1-6-dev libtasn1-doc libtcl8.6 libtk8.6 libunbound8 libutempter0 #7 2.229 libvulkan1 libwayland-client0 libx11-dev libx11-xcb1 libxau-dev libxaw7 #7 2.229 libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 libxcb-present0 libxcb-randr0 #7 2.229 libxcb-shape0 libxcb-shm0 libxcb-sync1 libxcb-xfixes0 libxcb1-dev #7 2.229 libxcomposite1 libxdmcp-dev libxext-dev libxext6 libxfixes3 libxft-dev #7 2.229 libxft2 libxi6 libxinerama1 libxkbfile1 libxml2-dev libxmlsec1 #7 2.229 libxmlsec1-dev libxmlsec1-gcrypt libxmlsec1-gnutls libxmlsec1-nss #7 2.229 libxmlsec1-openssl libxmu6 libxmuu1 libxrandr2 libxrender-dev libxrender1 #7 2.229 libxshmfence1 libxslt1-dev libxss-dev libxss1 libxt-dev libxt6 libxtst6 #7 2.229 libxv1 libxxf86dga1 libxxf86vm1 libyaml-0-2 libz3-4 libz3-dev llvm llvm-10 #7 2.229 llvm-10-dev llvm-10-runtime llvm-10-tools llvm-runtime mesa-vulkan-drivers #7 2.229 mime-support nettle-dev pkg-config python3 python3-minimal #7 2.229 python3-pkg-resources python3-pygments python3-yaml python3.8 #7 2.229 python3.8-minimal readline-common shared-mime-info tcl tcl-dev tcl8.6 #7 2.229 tcl8.6-dev tk tk-dev tk8.6 tk8.6-dev uuid-dev x11-common x11-utils #7 2.229 x11proto-core-dev x11proto-dev x11proto-scrnsaver-dev x11proto-xext-dev #7 2.229 xbitmaps xdg-user-dirs xorg-sgml-doctools xterm xtrans-dev zlib1g-dev #7 2.493 0 upgraded, 167 newly installed, 0 to remove and 0 not upgraded. #7 2.493 Need to get 138 MB of archives. #7 2.493 After this operation, 1062 MB of additional disk space will be used. #7 2.493 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libpython3.8-minimal amd64 3.8.10-0ubuntu1~20.04.9 [718 kB] #7 3.260 Get:2 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3.8-minimal amd64 3.8.10-0ubuntu1~20.04.9 [1890 kB] #7 4.013 Get:3 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-minimal amd64 3.8.2-0ubuntu2 [23.6 kB] #7 4.022 Get:4 http://archive.ubuntu.com/ubuntu focal/main amd64 mime-support all 3.64ubuntu1 [30.6 kB] #7 4.038 Get:5 http://archive.ubuntu.com/ubuntu focal/main amd64 libmpdec2 amd64 2.4.2-3 [81.1 kB] #7 4.067 Get:6 http://archive.ubuntu.com/ubuntu focal/main amd64 readline-common all 8.0-4 [53.5 kB] #7 4.089 Get:7 http://archive.ubuntu.com/ubuntu focal/main amd64 libreadline8 amd64 8.0-4 [131 kB] #7 4.143 Get:8 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libpython3.8-stdlib amd64 3.8.10-0ubuntu1~20.04.9 [1674 kB] #7 4.808 Get:9 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3.8 amd64 3.8.10-0ubuntu1~20.04.9 [387 kB] #7 4.961 Get:10 http://archive.ubuntu.com/ubuntu focal/main amd64 libpython3-stdlib amd64 3.8.2-0ubuntu2 [7068 B] #7 4.963 Get:11 http://archive.ubuntu.com/ubuntu focal/main amd64 python3 amd64 3.8.2-0ubuntu2 [47.6 kB] #7 4.983 Get:12 http://archive.ubuntu.com/ubuntu focal/main amd64 libmagic-mgc amd64 1:5.38-4 [218 kB] #7 5.068 Get:13 http://archive.ubuntu.com/ubuntu focal/main amd64 libmagic1 amd64 1:5.38-4 [75.9 kB] #7 5.100 Get:14 http://archive.ubuntu.com/ubuntu focal/main amd64 file amd64 1:5.38-4 [23.3 kB] #7 5.108 Get:15 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglib2.0-0 amd64 2.64.6-1~ubuntu20.04.7 [1289 kB] #7 5.621 Get:16 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglib2.0-data all 2.64.6-1~ubuntu20.04.7 [5944 B] #7 5.623 Get:17 http://archive.ubuntu.com/ubuntu focal/main amd64 libyaml-0-2 amd64 0.2.2-1 [48.9 kB] #7 5.640 Get:18 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-pkg-resources all 45.2.0-1ubuntu0.1 [130 kB] #7 5.693 Get:19 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-yaml amd64 5.3.1-1ubuntu0.1 [136 kB] #7 5.752 Get:20 http://archive.ubuntu.com/ubuntu focal/main amd64 shared-mime-info amd64 1.15-1 [430 kB] #7 5.921 Get:21 http://archive.ubuntu.com/ubuntu focal/main amd64 xdg-user-dirs amd64 0.17-2ubuntu1 [48.3 kB] #7 5.940 Get:22 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-common all 2.4.107-8ubuntu1~20.04.2 [5396 B] #7 5.941 Get:23 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm2 amd64 2.4.107-8ubuntu1~20.04.2 [34.1 kB] #7 5.952 Get:24 http://archive.ubuntu.com/ubuntu focal/main amd64 libedit2 amd64 3.1-20191231-1 [87.0 kB] #7 5.991 Get:25 http://archive.ubuntu.com/ubuntu focal/main amd64 libpipeline1 amd64 1.5.2-2build1 [27.7 kB] #7 6.000 Get:26 http://archive.ubuntu.com/ubuntu focal/main amd64 libxext6 amd64 2:1.3.4-0ubuntu1 [29.1 kB] #7 6.016 Get:27 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmuu1 amd64 2:1.1.3-0ubuntu1 [9728 B] #7 6.019 Get:28 http://archive.ubuntu.com/ubuntu focal/universe amd64 binfmt-support amd64 2.2.0-2 [58.2 kB] #7 6.041 Get:29 http://archive.ubuntu.com/ubuntu focal/main amd64 bzip2-doc all 1.0.8-2 [501 kB] #7 6.241 Get:30 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 icu-devtools amd64 66.1-2ubuntu2.1 [189 kB] #7 6.320 Get:31 http://archive.ubuntu.com/ubuntu focal/main amd64 libbz2-dev amd64 1.0.8-2 [30.2 kB] #7 6.329 Get:32 http://archive.ubuntu.com/ubuntu focal/main amd64 libllvm10 amd64 1:10.0.0-4ubuntu1 [15.3 MB] #7 12.57 Get:33 http://archive.ubuntu.com/ubuntu focal/universe amd64 libclang-cpp10 amd64 1:10.0.0-4ubuntu1 [9944 kB] #7 16.83 Get:34 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-amdgpu1 amd64 2.4.107-8ubuntu1~20.04.2 [18.6 kB] #7 16.83 Get:35 http://archive.ubuntu.com/ubuntu focal/main amd64 libpciaccess0 amd64 0.16-0ubuntu1 [17.9 kB] #7 16.84 Get:36 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-intel1 amd64 2.4.107-8ubuntu1~20.04.2 [60.3 kB] #7 16.86 Get:37 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-nouveau2 amd64 2.4.107-8ubuntu1~20.04.2 [16.6 kB] #7 16.87 Get:38 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-radeon1 amd64 2.4.107-8ubuntu1~20.04.2 [19.7 kB] #7 16.87 Get:39 http://archive.ubuntu.com/ubuntu focal/main amd64 libevent-2.1-7 amd64 2.1.11-stable-1 [138 kB] #7 16.93 Get:40 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libexpat1-dev amd64 2.2.9-1ubuntu0.6 [116 kB] #7 16.98 Get:41 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 zlib1g-dev amd64 1:1.2.11.dfsg-2ubuntu1.5 [155 kB] #7 17.04 Get:42 http://archive.ubuntu.com/ubuntu focal/main amd64 libpng-dev amd64 1.6.37-2 [175 kB] #7 17.11 Get:43 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libfreetype-dev amd64 2.10.1-2ubuntu0.3 [493 kB] #7 17.30 Get:44 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libfreetype6-dev amd64 2.10.1-2ubuntu0.3 [9808 B] #7 17.31 Get:45 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 uuid-dev amd64 2.34-0.1ubuntu9.6 [33.6 kB] #7 17.32 Get:46 http://archive.ubuntu.com/ubuntu focal/main amd64 pkg-config amd64 0.29.1-0ubuntu4 [45.5 kB] #7 17.34 Get:47 http://archive.ubuntu.com/ubuntu focal/main amd64 libfontconfig1-dev amd64 2.13.1-2ubuntu3 [731 kB] #7 17.63 Get:48 http://archive.ubuntu.com/ubuntu focal/main amd64 libfontenc1 amd64 1:1.1.4-0ubuntu1 [14.0 kB] #7 17.64 Get:49 http://archive.ubuntu.com/ubuntu focal/main amd64 libgpg-error-dev amd64 1.37-1 [109 kB] #7 17.68 Get:50 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgcrypt20-dev amd64 1.8.5-5ubuntu1.1 [471 kB] #7 17.87 Get:51 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglapi-mesa amd64 21.2.6-0ubuntu0.1~20.04.2 [27.4 kB] #7 17.88 Get:52 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libllvm12 amd64 1:12.0.0-3ubuntu1~20.04.5 [18.8 MB] #7 25.65 Get:53 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsensors-config all 1:3.6.0-2ubuntu1.1 [6052 B] #7 25.67 Get:54 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsensors5 amd64 1:3.6.0-2ubuntu1.1 [27.2 kB] #7 25.67 Get:55 http://archive.ubuntu.com/ubuntu focal/main amd64 libvulkan1 amd64 1.2.131.2-1 [93.3 kB] #7 25.68 Get:56 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl1-mesa-dri amd64 21.2.6-0ubuntu0.1~20.04.2 [11.0 MB] #7 30.46 Get:57 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-xcb1 amd64 2:1.6.9-2ubuntu1.6 [9448 B] #7 30.46 Get:58 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-dri2-0 amd64 1.14-2 [6920 B] #7 30.47 Get:59 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-dri3-0 amd64 1.14-2 [6552 B] #7 30.47 Get:60 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-glx0 amd64 1.14-2 [22.1 kB] #7 30.57 Get:61 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-present0 amd64 1.14-2 [5560 B] #7 30.62 Get:62 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-shm0 amd64 1.14-2 [5584 B] #7 30.67 Get:63 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-sync1 amd64 1.14-2 [8884 B] #7 30.73 Get:64 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-xfixes0 amd64 1.14-2 [9296 B] #7 30.79 Get:65 http://archive.ubuntu.com/ubuntu focal/main amd64 libxfixes3 amd64 1:5.0.3-2 [10.9 kB] #7 30.86 Get:66 http://archive.ubuntu.com/ubuntu focal/main amd64 libxshmfence1 amd64 1.3-1 [5028 B] #7 30.90 Get:67 http://archive.ubuntu.com/ubuntu focal/main amd64 libxxf86vm1 amd64 1:1.1.4-1build1 [10.2 kB] #7 30.96 Get:68 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglx-mesa0 amd64 21.2.6-0ubuntu0.1~20.04.2 [137 kB] #7 31.14 Get:69 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgmpxx4ldbl amd64 2:6.2.0+dfsg-4ubuntu0.1 [9144 B] #7 31.19 Get:70 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgmp-dev amd64 2:6.2.0+dfsg-4ubuntu0.1 [320 kB] #7 31.31 Get:71 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutls-openssl27 amd64 3.6.13-2ubuntu1.11 [29.8 kB] #7 31.40 Get:72 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libunbound8 amd64 1.9.4-2ubuntu1.5 [353 kB] #7 31.66 Get:73 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutls-dane0 amd64 3.6.13-2ubuntu1.11 [29.1 kB] #7 31.71 Get:74 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutlsxx28 amd64 3.6.13-2ubuntu1.11 [14.7 kB] #7 31.76 Get:75 http://archive.ubuntu.com/ubuntu focal/main amd64 libidn2-dev amd64 2.2.0-2 [64.6 kB] #7 31.81 Get:76 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libp11-kit-dev amd64 0.23.20-1ubuntu0.1 [65.2 kB] #7 31.86 Get:77 http://archive.ubuntu.com/ubuntu focal/main amd64 libtasn1-6-dev amd64 4.16.0-2 [85.8 kB] #7 31.92 Get:78 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 nettle-dev amd64 3.5.1+really3.5.1-2ubuntu0.2 [987 kB] #7 32.16 Get:79 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutls28-dev amd64 3.6.13-2ubuntu1.11 [873 kB] #7 32.38 Get:80 http://archive.ubuntu.com/ubuntu focal/main amd64 x11-common all 1:7.7+19ubuntu14 [22.3 kB] #7 32.43 Get:81 http://archive.ubuntu.com/ubuntu focal/main amd64 libice6 amd64 2:1.0.10-0ubuntu1 [41.0 kB] #7 32.48 Get:82 http://archive.ubuntu.com/ubuntu focal/main amd64 xorg-sgml-doctools all 1:1.11-1 [12.9 kB] #7 32.57 Get:83 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-dev all 2019.2-1ubuntu1 [594 kB] #7 32.88 Get:84 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-core-dev all 2019.2-1ubuntu1 [2620 B] #7 32.92 Get:85 http://archive.ubuntu.com/ubuntu focal/main amd64 libice-dev amd64 2:1.0.10-0ubuntu1 [47.8 kB] #7 32.97 Get:86 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libicu-dev amd64 66.1-2ubuntu2.1 [9451 kB] #7 34.76 Get:87 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libncurses-dev amd64 6.2-0ubuntu2.1 [340 kB] #7 34.86 Get:88 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libncursesw5-dev amd64 6.2-0ubuntu2.1 [988 B] #7 34.91 Get:89 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnspr4 amd64 2:4.35-0ubuntu0.20.04.1 [108 kB] #7 34.97 Get:90 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnspr4-dev amd64 2:4.35-0ubuntu0.20.04.1 [207 kB] #7 35.05 Get:91 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnss3 amd64 2:3.98-0ubuntu0.20.04.2 [1391 kB] #7 35.35 Get:92 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnss3-dev amd64 2:3.98-0ubuntu0.20.04.2 [246 kB] #7 35.44 Get:93 http://archive.ubuntu.com/ubuntu focal/main amd64 libpng-tools amd64 1.6.37-2 [26.1 kB] #7 35.53 Get:94 http://archive.ubuntu.com/ubuntu focal/main amd64 libpthread-stubs0-dev amd64 0.4-1 [5384 B] #7 35.57 Get:95 http://archive.ubuntu.com/ubuntu focal/main amd64 libreadline-dev amd64 8.0-4 [141 kB] #7 35.75 Get:96 http://archive.ubuntu.com/ubuntu focal/main amd64 libsm6 amd64 2:1.2.3-1 [16.1 kB] #7 35.80 Get:97 http://archive.ubuntu.com/ubuntu focal/main amd64 libsm-dev amd64 2:1.2.3-1 [17.0 kB] #7 35.85 Get:98 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsqlite3-dev amd64 3.31.1-4ubuntu0.6 [696 kB] #7 36.05 Get:99 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libssl-dev amd64 1.1.1f-1ubuntu2.22 [1586 kB] #7 36.41 Get:100 http://archive.ubuntu.com/ubuntu focal/main amd64 libtcl8.6 amd64 8.6.10+dfsg-1 [902 kB] #7 36.61 Get:101 http://archive.ubuntu.com/ubuntu focal/main amd64 libxrender1 amd64 1:0.9.10-1 [18.7 kB] #7 36.66 Get:102 http://archive.ubuntu.com/ubuntu focal/main amd64 libxft2 amd64 2.3.3-0ubuntu1 [39.2 kB] #7 36.71 Get:103 http://archive.ubuntu.com/ubuntu focal/main amd64 libxss1 amd64 1:1.2.3-1 [8140 B] #7 36.75 Get:104 http://archive.ubuntu.com/ubuntu focal/main amd64 libtk8.6 amd64 8.6.10-1 [714 kB] #7 36.96 Get:105 http://archive.ubuntu.com/ubuntu focal/main amd64 libutempter0 amd64 1.1.6-4 [8256 B] #7 37.01 Get:106 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwayland-client0 amd64 1.18.0-1ubuntu0.1 [23.9 kB] #7 37.10 Get:107 http://archive.ubuntu.com/ubuntu focal/main amd64 libxau-dev amd64 1:1.0.9-0ubuntu1 [9552 B] #7 37.15 Get:108 http://archive.ubuntu.com/ubuntu focal/main amd64 libxdmcp-dev amd64 1:1.1.3-0ubuntu1 [25.3 kB] #7 37.21 Get:109 http://archive.ubuntu.com/ubuntu focal/main amd64 xtrans-dev all 1.4.0-1 [68.9 kB] #7 37.30 Get:110 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb1-dev amd64 1.14-2 [80.5 kB] #7 37.36 Get:111 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-dev amd64 2:1.6.9-2ubuntu1.6 [648 kB] #7 37.54 Get:112 http://archive.ubuntu.com/ubuntu focal/main amd64 libxt6 amd64 1:1.1.5-1 [160 kB] #7 37.61 Get:113 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmu6 amd64 2:1.1.3-0ubuntu1 [45.8 kB] #7 37.66 Get:114 http://archive.ubuntu.com/ubuntu focal/main amd64 libxaw7 amd64 2:1.0.13-1 [173 kB] #7 37.73 Get:115 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-randr0 amd64 1.14-2 [16.3 kB] #7 37.82 Get:116 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-shape0 amd64 1.14-2 [5928 B] #7 37.86 Get:117 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcomposite1 amd64 1:0.4.5-1 [6976 B] #7 37.91 Get:118 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-xext-dev all 2019.2-1ubuntu1 [2616 B] #7 37.95 Get:119 http://archive.ubuntu.com/ubuntu focal/main amd64 libxext-dev amd64 2:1.3.4-0ubuntu1 [82.2 kB] #7 38.11 Get:120 http://archive.ubuntu.com/ubuntu focal/main amd64 libxrender-dev amd64 1:0.9.10-1 [24.9 kB] #7 38.17 Get:121 http://archive.ubuntu.com/ubuntu focal/main amd64 libxft-dev amd64 2.3.3-0ubuntu1 [49.1 kB] #7 38.23 Get:122 http://archive.ubuntu.com/ubuntu focal/main amd64 libxi6 amd64 2:1.7.10-0ubuntu1 [29.9 kB] #7 38.28 Get:123 http://archive.ubuntu.com/ubuntu focal/main amd64 libxinerama1 amd64 2:1.1.4-2 [6904 B] #7 38.33 Get:124 http://archive.ubuntu.com/ubuntu focal/main amd64 libxkbfile1 amd64 1:1.1.0-1 [65.3 kB] #7 38.40 Get:125 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxml2-dev amd64 2.9.10+dfsg-5ubuntu0.20.04.7 [735 kB] #7 38.62 Get:126 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1 amd64 1.2.28-2 [127 kB] #7 38.72 Get:127 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-gcrypt amd64 1.2.28-2 [41.5 kB] #7 38.82 Get:128 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-gnutls amd64 1.2.28-2 [33.4 kB] #7 38.88 Get:129 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-nss amd64 1.2.28-2 [61.8 kB] #7 38.96 Get:130 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-openssl amd64 1.2.28-2 [75.9 kB] #7 39.03 Get:131 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxslt1-dev amd64 1.1.34-4ubuntu0.20.04.1 [219 kB] #7 39.14 Get:132 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-dev amd64 1.2.28-2 [387 kB] #7 39.25 Get:133 http://archive.ubuntu.com/ubuntu focal/main amd64 libxrandr2 amd64 2:1.5.2-0ubuntu1 [18.5 kB] #7 39.29 Get:134 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-scrnsaver-dev all 2019.2-1ubuntu1 [2624 B] #7 39.33 Get:135 http://archive.ubuntu.com/ubuntu focal/main amd64 libxss-dev amd64 1:1.2.3-1 [11.9 kB] #7 39.37 Get:136 http://archive.ubuntu.com/ubuntu focal/main amd64 libxt-dev amd64 1:1.1.5-1 [395 kB] #7 39.50 Get:137 http://archive.ubuntu.com/ubuntu focal/main amd64 libxtst6 amd64 2:1.2.3-1 [12.8 kB] #7 39.59 Get:138 http://archive.ubuntu.com/ubuntu focal/main amd64 libxv1 amd64 2:1.0.11-1 [10.7 kB] #7 39.64 Get:139 http://archive.ubuntu.com/ubuntu focal/main amd64 libxxf86dga1 amd64 2:1.1.5-0ubuntu1 [12.0 kB] #7 39.69 Get:140 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10-runtime amd64 1:10.0.0-4ubuntu1 [180 kB] #7 39.88 Get:141 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-runtime amd64 1:10.0-50~exp1 [2916 B] #7 39.93 Get:142 http://archive.ubuntu.com/ubuntu focal/main amd64 libpfm4 amd64 4.10.1+git20-g7700f49-2 [266 kB] #7 40.02 Get:143 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10 amd64 1:10.0.0-4ubuntu1 [5214 kB] #7 41.04 Get:144 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm amd64 1:10.0-50~exp1 [3880 B] #7 41.08 Get:145 http://archive.ubuntu.com/ubuntu focal/main amd64 libffi-dev amd64 3.3-4 [57.0 kB] #7 41.13 Get:146 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-pygments all 2.3.1+dfsg-1ubuntu2.2 [579 kB] #7 41.29 Get:147 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10-tools amd64 1:10.0.0-4ubuntu1 [317 kB] #7 41.38 Get:148 http://archive.ubuntu.com/ubuntu focal/universe amd64 libz3-4 amd64 4.8.7-4build1 [6792 kB] #7 42.66 Get:149 http://archive.ubuntu.com/ubuntu focal/universe amd64 libz3-dev amd64 4.8.7-4build1 [67.5 kB] #7 42.81 Get:150 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10-dev amd64 1:10.0.0-4ubuntu1 [26.0 MB] #7 47.54 Get:151 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 mesa-vulkan-drivers amd64 21.2.6-0ubuntu0.1~20.04.2 [5788 kB] #7 48.60 Get:152 http://archive.ubuntu.com/ubuntu focal/main amd64 tcl8.6 amd64 8.6.10+dfsg-1 [14.8 kB] #7 48.65 Get:153 http://archive.ubuntu.com/ubuntu focal/universe amd64 tcl amd64 8.6.9+1 [5112 B] #7 48.69 Get:154 http://archive.ubuntu.com/ubuntu focal/main amd64 tcl8.6-dev amd64 8.6.10+dfsg-1 [905 kB] #7 48.89 Get:155 http://archive.ubuntu.com/ubuntu focal/universe amd64 tcl-dev amd64 8.6.9+1 [5760 B] #7 48.94 Get:156 http://archive.ubuntu.com/ubuntu focal/main amd64 tk8.6 amd64 8.6.10-1 [12.5 kB] #7 48.98 Get:157 http://archive.ubuntu.com/ubuntu focal/universe amd64 tk amd64 8.6.9+1 [3240 B] #7 49.02 Get:158 http://archive.ubuntu.com/ubuntu focal/main amd64 tk8.6-dev amd64 8.6.10-1 [711 kB] #7 49.20 Get:159 http://archive.ubuntu.com/ubuntu focal/universe amd64 tk-dev amd64 8.6.9+1 [3076 B] #7 49.29 Get:160 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglvnd0 amd64 1.3.2-1~ubuntu0.20.04.2 [48.1 kB] #7 49.42 Get:161 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglx0 amd64 1.3.2-1~ubuntu0.20.04.2 [32.5 kB] #7 49.48 Get:162 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl1 amd64 1.3.2-1~ubuntu0.20.04.2 [85.8 kB] #7 49.57 Get:163 http://archive.ubuntu.com/ubuntu focal/main amd64 x11-utils amd64 7.7+5 [199 kB] #7 49.68 Get:164 http://archive.ubuntu.com/ubuntu focal/main amd64 xbitmaps all 1.1.1-2 [28.1 kB] #7 49.72 Get:165 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 xterm amd64 353-1ubuntu1.20.04.2 [765 kB] #7 49.91 Get:166 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 liblzma-dev amd64 5.2.4-1ubuntu1.1 [147 kB] #7 49.98 Get:167 http://archive.ubuntu.com/ubuntu focal/main amd64 libtasn1-doc all 4.16.0-2 [303 kB] #7 50.23 debconf: delaying package configuration, since apt-utils is not installed #7 50.28 Fetched 138 MB in 48s (2890 kB/s) #7 50.34 Selecting previously unselected package libpython3.8-minimal:amd64. #7 50.34 (Reading database ... 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previously unselected package libgnutls-openssl27:amd64. #7 63.61 Preparing to unpack .../060-libgnutls-openssl27_3.6.13-2ubuntu1.11_amd64.deb ... #7 63.62 Unpacking libgnutls-openssl27:amd64 (3.6.13-2ubuntu1.11) ... #7 63.69 Selecting previously unselected package libunbound8:amd64. #7 63.69 Preparing to unpack .../061-libunbound8_1.9.4-2ubuntu1.5_amd64.deb ... #7 63.70 Unpacking libunbound8:amd64 (1.9.4-2ubuntu1.5) ... #7 63.82 Selecting previously unselected package libgnutls-dane0:amd64. #7 63.83 Preparing to unpack .../062-libgnutls-dane0_3.6.13-2ubuntu1.11_amd64.deb ... #7 63.83 Unpacking libgnutls-dane0:amd64 (3.6.13-2ubuntu1.11) ... #7 63.91 Selecting previously unselected package libgnutlsxx28:amd64. #7 63.91 Preparing to unpack .../063-libgnutlsxx28_3.6.13-2ubuntu1.11_amd64.deb ... #7 63.92 Unpacking libgnutlsxx28:amd64 (3.6.13-2ubuntu1.11) ... #7 63.98 Selecting previously unselected package libidn2-dev:amd64. #7 63.99 Preparing to unpack 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unselected package x11-common. #7 64.64 Preparing to unpack .../069-x11-common_1%3a7.7+19ubuntu14_all.deb ... #7 64.67 dpkg-query: no packages found matching nux-tools #7 64.69 Unpacking x11-common (1:7.7+19ubuntu14) ... #7 64.77 Selecting previously unselected package libice6:amd64. #7 64.78 Preparing to unpack .../070-libice6_2%3a1.0.10-0ubuntu1_amd64.deb ... #7 64.78 Unpacking libice6:amd64 (2:1.0.10-0ubuntu1) ... #7 64.85 Selecting previously unselected package xorg-sgml-doctools. #7 64.86 Preparing to unpack .../071-xorg-sgml-doctools_1%3a1.11-1_all.deb ... #7 64.87 Unpacking xorg-sgml-doctools (1:1.11-1) ... #7 64.93 Selecting previously unselected package x11proto-dev. #7 64.94 Preparing to unpack .../072-x11proto-dev_2019.2-1ubuntu1_all.deb ... #7 64.94 Unpacking x11proto-dev (2019.2-1ubuntu1) ... #7 65.08 Selecting previously unselected package x11proto-core-dev. #7 65.09 Preparing to unpack .../073-x11proto-core-dev_2019.2-1ubuntu1_all.deb ... #7 65.10 Unpacking x11proto-core-dev (2019.2-1ubuntu1) ... #7 65.16 Selecting previously unselected package libice-dev:amd64. #7 65.16 Preparing to unpack .../074-libice-dev_2%3a1.0.10-0ubuntu1_amd64.deb ... #7 65.17 Unpacking libice-dev:amd64 (2:1.0.10-0ubuntu1) ... #7 65.25 Selecting previously unselected package libicu-dev:amd64. #7 65.25 Preparing to unpack .../075-libicu-dev_66.1-2ubuntu2.1_amd64.deb ... #7 65.26 Unpacking libicu-dev:amd64 (66.1-2ubuntu2.1) ... #7 66.22 Selecting previously unselected package libncurses-dev:amd64. #7 66.23 Preparing to unpack .../076-libncurses-dev_6.2-0ubuntu2.1_amd64.deb ... #7 66.24 Unpacking libncurses-dev:amd64 (6.2-0ubuntu2.1) ... #7 66.36 Selecting previously unselected package libncursesw5-dev:amd64. #7 66.36 Preparing to unpack .../077-libncursesw5-dev_6.2-0ubuntu2.1_amd64.deb ... #7 66.37 Unpacking libncursesw5-dev:amd64 (6.2-0ubuntu2.1) ... #7 66.44 Selecting previously unselected package libnspr4:amd64. #7 66.44 Preparing to unpack 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package libpthread-stubs0-dev:amd64. #7 67.07 Preparing to unpack .../083-libpthread-stubs0-dev_0.4-1_amd64.deb ... #7 67.08 Unpacking libpthread-stubs0-dev:amd64 (0.4-1) ... #7 67.17 Selecting previously unselected package libreadline-dev:amd64. #7 67.18 Preparing to unpack .../084-libreadline-dev_8.0-4_amd64.deb ... #7 67.19 Unpacking libreadline-dev:amd64 (8.0-4) ... #7 67.29 Selecting previously unselected package libsm6:amd64. #7 67.29 Preparing to unpack .../085-libsm6_2%3a1.2.3-1_amd64.deb ... #7 67.30 Unpacking libsm6:amd64 (2:1.2.3-1) ... #7 67.37 Selecting previously unselected package libsm-dev:amd64. #7 67.37 Preparing to unpack .../086-libsm-dev_2%3a1.2.3-1_amd64.deb ... #7 67.38 Unpacking libsm-dev:amd64 (2:1.2.3-1) ... #7 67.45 Selecting previously unselected package libsqlite3-dev:amd64. #7 67.45 Preparing to unpack .../087-libsqlite3-dev_3.31.1-4ubuntu0.6_amd64.deb ... #7 67.46 Unpacking libsqlite3-dev:amd64 (3.31.1-4ubuntu0.6) ... #7 67.60 Selecting previously unselected package libssl-dev:amd64. #7 67.60 Preparing to unpack .../088-libssl-dev_1.1.1f-1ubuntu2.22_amd64.deb ... #7 67.61 Unpacking libssl-dev:amd64 (1.1.1f-1ubuntu2.22) ... #7 67.84 Selecting previously unselected package libtcl8.6:amd64. #7 67.85 Preparing to unpack .../089-libtcl8.6_8.6.10+dfsg-1_amd64.deb ... #7 67.85 Unpacking libtcl8.6:amd64 (8.6.10+dfsg-1) ... #7 68.05 Selecting previously unselected package libxrender1:amd64. #7 68.05 Preparing to unpack .../090-libxrender1_1%3a0.9.10-1_amd64.deb ... #7 68.06 Unpacking libxrender1:amd64 (1:0.9.10-1) ... #7 68.13 Selecting previously unselected package libxft2:amd64. #7 68.14 Preparing to unpack .../091-libxft2_2.3.3-0ubuntu1_amd64.deb ... #7 68.14 Unpacking libxft2:amd64 (2.3.3-0ubuntu1) ... #7 68.23 Selecting previously unselected package libxss1:amd64. #7 68.23 Preparing to unpack .../092-libxss1_1%3a1.2.3-1_amd64.deb ... #7 68.24 Unpacking libxss1:amd64 (1:1.2.3-1) ... #7 68.31 Selecting previously unselected package libtk8.6:amd64. #7 68.31 Preparing to unpack .../093-libtk8.6_8.6.10-1_amd64.deb ... #7 68.32 Unpacking libtk8.6:amd64 (8.6.10-1) ... #7 68.47 Selecting previously unselected package libutempter0:amd64. #7 68.47 Preparing to unpack .../094-libutempter0_1.1.6-4_amd64.deb ... #7 68.48 Unpacking libutempter0:amd64 (1.1.6-4) ... #7 68.55 Selecting previously unselected package libwayland-client0:amd64. #7 68.56 Preparing to unpack .../095-libwayland-client0_1.18.0-1ubuntu0.1_amd64.deb ... #7 68.56 Unpacking libwayland-client0:amd64 (1.18.0-1ubuntu0.1) ... #7 68.63 Selecting previously unselected package libxau-dev:amd64. #7 68.63 Preparing to unpack .../096-libxau-dev_1%3a1.0.9-0ubuntu1_amd64.deb ... #7 68.64 Unpacking libxau-dev:amd64 (1:1.0.9-0ubuntu1) ... #7 68.70 Selecting previously unselected package libxdmcp-dev:amd64. #7 68.71 Preparing to unpack .../097-libxdmcp-dev_1%3a1.1.3-0ubuntu1_amd64.deb ... #7 68.71 Unpacking libxdmcp-dev:amd64 (1:1.1.3-0ubuntu1) ... #7 68.78 Selecting previously unselected package xtrans-dev. #7 68.78 Preparing to unpack .../098-xtrans-dev_1.4.0-1_all.deb ... #7 68.79 Unpacking xtrans-dev (1.4.0-1) ... #7 68.87 Selecting previously unselected package libxcb1-dev:amd64. #7 68.87 Preparing to unpack .../099-libxcb1-dev_1.14-2_amd64.deb ... #7 68.88 Unpacking libxcb1-dev:amd64 (1.14-2) ... #7 68.96 Selecting previously unselected package libx11-dev:amd64. #7 68.96 Preparing to unpack .../100-libx11-dev_2%3a1.6.9-2ubuntu1.6_amd64.deb ... #7 68.97 Unpacking libx11-dev:amd64 (2:1.6.9-2ubuntu1.6) ... #7 69.12 Selecting previously unselected package libxt6:amd64. #7 69.12 Preparing to unpack .../101-libxt6_1%3a1.1.5-1_amd64.deb ... #7 69.12 Unpacking libxt6:amd64 (1:1.1.5-1) ... #7 69.22 Selecting previously unselected package libxmu6:amd64. #7 69.22 Preparing to unpack .../102-libxmu6_2%3a1.1.3-0ubuntu1_amd64.deb ... #7 69.23 Unpacking libxmu6:amd64 (2:1.1.3-0ubuntu1) ... #7 69.31 Selecting previously unselected package libxaw7:amd64. #7 69.32 Preparing to unpack .../103-libxaw7_2%3a1.0.13-1_amd64.deb ... #7 69.32 Unpacking libxaw7:amd64 (2:1.0.13-1) ... #7 69.43 Selecting previously unselected package libxcb-randr0:amd64. #7 69.43 Preparing to unpack .../104-libxcb-randr0_1.14-2_amd64.deb ... #7 69.44 Unpacking libxcb-randr0:amd64 (1.14-2) ... #7 69.51 Selecting previously unselected package libxcb-shape0:amd64. #7 69.52 Preparing to unpack .../105-libxcb-shape0_1.14-2_amd64.deb ... #7 69.52 Unpacking libxcb-shape0:amd64 (1.14-2) ... #7 69.59 Selecting previously unselected package libxcomposite1:amd64. #7 69.60 Preparing to unpack .../106-libxcomposite1_1%3a0.4.5-1_amd64.deb ... #7 69.61 Unpacking libxcomposite1:amd64 (1:0.4.5-1) ... #7 69.67 Selecting previously unselected package x11proto-xext-dev. #7 69.67 Preparing to unpack .../107-x11proto-xext-dev_2019.2-1ubuntu1_all.deb ... #7 69.68 Unpacking x11proto-xext-dev (2019.2-1ubuntu1) ... #7 69.74 Selecting previously unselected package libxext-dev:amd64. #7 69.75 Preparing to unpack .../108-libxext-dev_2%3a1.3.4-0ubuntu1_amd64.deb ... #7 69.75 Unpacking libxext-dev:amd64 (2:1.3.4-0ubuntu1) ... #7 69.84 Selecting previously unselected package libxrender-dev:amd64. #7 69.84 Preparing to unpack .../109-libxrender-dev_1%3a0.9.10-1_amd64.deb ... #7 69.84 Unpacking libxrender-dev:amd64 (1:0.9.10-1) ... #7 69.90 Selecting previously unselected package libxft-dev:amd64. #7 69.90 Preparing to unpack .../110-libxft-dev_2.3.3-0ubuntu1_amd64.deb ... #7 69.91 Unpacking libxft-dev:amd64 (2.3.3-0ubuntu1) ... #7 69.98 Selecting previously unselected package libxi6:amd64. #7 69.99 Preparing to unpack .../111-libxi6_2%3a1.7.10-0ubuntu1_amd64.deb ... #7 69.99 Unpacking libxi6:amd64 (2:1.7.10-0ubuntu1) ... #7 70.07 Selecting previously unselected package libxinerama1:amd64. #7 70.07 Preparing to unpack .../112-libxinerama1_2%3a1.1.4-2_amd64.deb ... #7 70.08 Unpacking libxinerama1:amd64 (2:1.1.4-2) ... #7 70.15 Selecting previously unselected package libxkbfile1:amd64. #7 70.15 Preparing to unpack .../113-libxkbfile1_1%3a1.1.0-1_amd64.deb ... #7 70.16 Unpacking libxkbfile1:amd64 (1:1.1.0-1) ... #7 70.23 Selecting previously unselected package libxml2-dev:amd64. #7 70.24 Preparing to unpack .../114-libxml2-dev_2.9.10+dfsg-5ubuntu0.20.04.7_amd64.deb ... #7 70.25 Unpacking libxml2-dev:amd64 (2.9.10+dfsg-5ubuntu0.20.04.7) ... #7 70.40 Selecting previously unselected package libxmlsec1:amd64. #7 70.41 Preparing to unpack .../115-libxmlsec1_1.2.28-2_amd64.deb ... #7 70.42 Unpacking libxmlsec1:amd64 (1.2.28-2) ... #7 70.52 Selecting previously unselected package libxmlsec1-gcrypt:amd64. #7 70.52 Preparing to unpack .../116-libxmlsec1-gcrypt_1.2.28-2_amd64.deb ... #7 70.53 Unpacking libxmlsec1-gcrypt:amd64 (1.2.28-2) ... #7 70.61 Selecting previously unselected package libxmlsec1-gnutls:amd64. #7 70.62 Preparing to unpack .../117-libxmlsec1-gnutls_1.2.28-2_amd64.deb ... #7 70.62 Unpacking libxmlsec1-gnutls:amd64 (1.2.28-2) ... #7 70.69 Selecting previously unselected package libxmlsec1-nss:amd64. #7 70.69 Preparing to unpack .../118-libxmlsec1-nss_1.2.28-2_amd64.deb ... #7 70.70 Unpacking libxmlsec1-nss:amd64 (1.2.28-2) ... #7 70.79 Selecting previously unselected package libxmlsec1-openssl:amd64. #7 70.80 Preparing to unpack .../119-libxmlsec1-openssl_1.2.28-2_amd64.deb ... #7 70.80 Unpacking libxmlsec1-openssl:amd64 (1.2.28-2) ... #7 70.89 Selecting previously unselected package libxslt1-dev:amd64. #7 70.89 Preparing to unpack .../120-libxslt1-dev_1.1.34-4ubuntu0.20.04.1_amd64.deb ... #7 70.90 Unpacking libxslt1-dev:amd64 (1.1.34-4ubuntu0.20.04.1) ... #7 71.00 Selecting previously unselected package libxmlsec1-dev. #7 71.01 Preparing to unpack .../121-libxmlsec1-dev_1.2.28-2_amd64.deb ... #7 71.01 Unpacking libxmlsec1-dev (1.2.28-2) ... #7 71.15 Selecting previously unselected package libxrandr2:amd64. #7 71.15 Preparing to unpack .../122-libxrandr2_2%3a1.5.2-0ubuntu1_amd64.deb ... #7 71.15 Unpacking libxrandr2:amd64 (2:1.5.2-0ubuntu1) ... #7 71.21 Selecting previously unselected package x11proto-scrnsaver-dev. #7 71.22 Preparing to unpack .../123-x11proto-scrnsaver-dev_2019.2-1ubuntu1_all.deb ... #7 71.22 Unpacking x11proto-scrnsaver-dev (2019.2-1ubuntu1) ... #7 71.28 Selecting previously unselected package libxss-dev:amd64. #7 71.28 Preparing to unpack .../124-libxss-dev_1%3a1.2.3-1_amd64.deb ... #7 71.29 Unpacking libxss-dev:amd64 (1:1.2.3-1) ... #7 71.36 Selecting previously unselected package libxt-dev:amd64. #7 71.36 Preparing to unpack .../125-libxt-dev_1%3a1.1.5-1_amd64.deb ... #7 71.37 Unpacking libxt-dev:amd64 (1:1.1.5-1) ... #7 71.50 Selecting previously unselected package libxtst6:amd64. #7 71.51 Preparing to unpack .../126-libxtst6_2%3a1.2.3-1_amd64.deb ... #7 71.52 Unpacking libxtst6:amd64 (2:1.2.3-1) ... #7 71.58 Selecting previously unselected package libxv1:amd64. #7 71.59 Preparing to unpack .../127-libxv1_2%3a1.0.11-1_amd64.deb ... #7 71.59 Unpacking libxv1:amd64 (2:1.0.11-1) ... #7 71.66 Selecting previously unselected package libxxf86dga1:amd64. #7 71.66 Preparing to unpack .../128-libxxf86dga1_2%3a1.1.5-0ubuntu1_amd64.deb ... #7 71.66 Unpacking libxxf86dga1:amd64 (2:1.1.5-0ubuntu1) ... #7 71.73 Selecting previously unselected package llvm-10-runtime. #7 71.74 Preparing to unpack .../129-llvm-10-runtime_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 71.74 Unpacking llvm-10-runtime (1:10.0.0-4ubuntu1) ... #7 71.84 Selecting previously unselected package llvm-runtime. #7 71.84 Preparing to unpack .../130-llvm-runtime_1%3a10.0-50~exp1_amd64.deb ... #7 71.85 Unpacking llvm-runtime (1:10.0-50~exp1) ... #7 71.93 Selecting previously unselected package libpfm4:amd64. #7 71.93 Preparing to unpack .../131-libpfm4_4.10.1+git20-g7700f49-2_amd64.deb ... #7 71.94 Unpacking libpfm4:amd64 (4.10.1+git20-g7700f49-2) ... #7 72.05 Selecting previously unselected package llvm-10. #7 72.05 Preparing to unpack .../132-llvm-10_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 72.06 Unpacking llvm-10 (1:10.0.0-4ubuntu1) ... #7 72.62 Selecting previously unselected package llvm. #7 72.62 Preparing to unpack .../133-llvm_1%3a10.0-50~exp1_amd64.deb ... #7 72.63 Unpacking llvm (1:10.0-50~exp1) ... #7 72.71 Selecting previously unselected package libffi-dev:amd64. #7 72.71 Preparing to unpack .../134-libffi-dev_3.3-4_amd64.deb ... #7 72.72 Unpacking libffi-dev:amd64 (3.3-4) ... #7 72.81 Selecting previously unselected package python3-pygments. #7 72.81 Preparing to unpack .../135-python3-pygments_2.3.1+dfsg-1ubuntu2.2_all.deb ... #7 72.82 Unpacking python3-pygments (2.3.1+dfsg-1ubuntu2.2) ... #7 72.95 Selecting previously unselected package llvm-10-tools. #7 72.95 Preparing to unpack .../136-llvm-10-tools_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 72.96 Unpacking llvm-10-tools (1:10.0.0-4ubuntu1) ... #7 73.12 Selecting previously unselected package libz3-4:amd64. #7 73.12 Preparing to unpack .../137-libz3-4_4.8.7-4build1_amd64.deb ... #7 73.13 Unpacking libz3-4:amd64 (4.8.7-4build1) ... #7 73.78 Selecting previously unselected package libz3-dev:amd64. #7 73.78 Preparing to unpack .../138-libz3-dev_4.8.7-4build1_amd64.deb ... #7 73.79 Unpacking libz3-dev:amd64 (4.8.7-4build1) ... #7 73.88 Selecting previously unselected package llvm-10-dev. #7 73.89 Preparing to unpack .../139-llvm-10-dev_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 73.90 Unpacking llvm-10-dev (1:10.0.0-4ubuntu1) ... #7 76.60 Selecting previously unselected package mesa-vulkan-drivers:amd64. #7 76.60 Preparing to unpack .../140-mesa-vulkan-drivers_21.2.6-0ubuntu0.1~20.04.2_amd64.deb ... #7 76.61 Unpacking mesa-vulkan-drivers:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 77.24 Selecting previously unselected package tcl8.6. #7 77.24 Preparing to unpack .../141-tcl8.6_8.6.10+dfsg-1_amd64.deb ... #7 77.25 Unpacking tcl8.6 (8.6.10+dfsg-1) ... #7 77.32 Selecting previously unselected package tcl. #7 77.32 Preparing to unpack .../142-tcl_8.6.9+1_amd64.deb ... #7 77.34 Unpacking tcl (8.6.9+1) ... #7 77.40 Selecting previously unselected package tcl8.6-dev:amd64. #7 77.41 Preparing to unpack .../143-tcl8.6-dev_8.6.10+dfsg-1_amd64.deb ... #7 77.42 Unpacking tcl8.6-dev:amd64 (8.6.10+dfsg-1) ... #7 77.59 Selecting previously unselected package tcl-dev:amd64. #7 77.59 Preparing to unpack .../144-tcl-dev_8.6.9+1_amd64.deb ... #7 77.60 Unpacking tcl-dev:amd64 (8.6.9+1) ... #7 77.68 Selecting previously unselected package tk8.6. #7 77.69 Preparing to unpack .../145-tk8.6_8.6.10-1_amd64.deb ... #7 77.69 Unpacking tk8.6 (8.6.10-1) ... #7 77.76 Selecting previously unselected package tk. #7 77.77 Preparing to unpack .../146-tk_8.6.9+1_amd64.deb ... #7 77.78 Unpacking tk (8.6.9+1) ... #7 77.85 Selecting previously unselected package tk8.6-dev:amd64. #7 77.85 Preparing to unpack .../147-tk8.6-dev_8.6.10-1_amd64.deb ... #7 77.86 Unpacking tk8.6-dev:amd64 (8.6.10-1) ... #7 78.01 Selecting previously unselected package tk-dev:amd64. #7 78.01 Preparing to unpack .../148-tk-dev_8.6.9+1_amd64.deb ... #7 78.02 Unpacking tk-dev:amd64 (8.6.9+1) ... #7 78.10 Selecting previously unselected package libglvnd0:amd64. #7 78.10 Preparing to unpack .../149-libglvnd0_1.3.2-1~ubuntu0.20.04.2_amd64.deb ... #7 78.11 Unpacking libglvnd0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 78.21 Selecting previously unselected package libglx0:amd64. #7 78.21 Preparing to unpack .../150-libglx0_1.3.2-1~ubuntu0.20.04.2_amd64.deb ... #7 78.22 Unpacking libglx0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 78.31 Selecting previously unselected package libgl1:amd64. #7 78.31 Preparing to unpack .../151-libgl1_1.3.2-1~ubuntu0.20.04.2_amd64.deb ... #7 78.32 Unpacking libgl1:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 78.41 Selecting previously unselected package x11-utils. #7 78.42 Preparing to unpack .../152-x11-utils_7.7+5_amd64.deb ... #7 78.42 Unpacking x11-utils (7.7+5) ... #7 78.52 Selecting previously unselected package xbitmaps. #7 78.53 Preparing to unpack .../153-xbitmaps_1.1.1-2_all.deb ... #7 78.54 Unpacking xbitmaps (1.1.1-2) ... #7 78.63 Selecting previously unselected package xterm. #7 78.63 Preparing to unpack .../154-xterm_353-1ubuntu1.20.04.2_amd64.deb ... #7 78.64 Unpacking xterm (353-1ubuntu1.20.04.2) ... #7 78.78 Selecting previously unselected package liblzma-dev:amd64. #7 78.78 Preparing to unpack .../155-liblzma-dev_5.2.4-1ubuntu1.1_amd64.deb ... #7 78.79 Unpacking liblzma-dev:amd64 (5.2.4-1ubuntu1.1) ... #7 78.88 Selecting previously unselected package libtasn1-doc. #7 78.89 Preparing to unpack .../156-libtasn1-doc_4.16.0-2_all.deb ... #7 78.89 Unpacking libtasn1-doc (4.16.0-2) ... #7 79.03 Setting up libpipeline1:amd64 (1.5.2-2build1) ... #7 79.05 Setting up libxcb-dri3-0:amd64 (1.14-2) ... #7 79.08 Setting up bzip2-doc (1.0.8-2) ... #7 79.10 Setting up libx11-xcb1:amd64 (2:1.6.9-2ubuntu1.6) ... #7 79.12 Setting up libpciaccess0:amd64 (0.16-0ubuntu1) ... #7 79.14 Setting up libncurses-dev:amd64 (6.2-0ubuntu2.1) ... #7 79.16 Setting up libgnutls-openssl27:amd64 (3.6.13-2ubuntu1.11) ... #7 79.18 Setting up libxcb-xfixes0:amd64 (1.14-2) ... #7 79.20 Setting up mime-support (3.64ubuntu1) ... #7 79.27 Setting up libncursesw5-dev:amd64 (6.2-0ubuntu2.1) ... #7 79.29 Setting up libxrender1:amd64 (1:0.9.10-1) ... #7 79.31 Setting up xdg-user-dirs (0.17-2ubuntu1) ... #7 79.37 Setting up libpng-tools (1.6.37-2) ... #7 79.39 Setting up libmagic-mgc (1:5.38-4) ... #7 79.41 Setting up libyaml-0-2:amd64 (0.2.2-1) ... #7 79.44 Setting up libglib2.0-0:amd64 (2.64.6-1~ubuntu20.04.7) ... #7 79.46 No schema files found: doing nothing. #7 79.47 Setting up libglvnd0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 79.50 Setting up libxcb-glx0:amd64 (1.14-2) ... #7 79.52 Setting up libtasn1-doc (4.16.0-2) ... #7 79.54 Setting up libedit2:amd64 (3.1-20191231-1) ... #7 79.56 Setting up libxcb-shape0:amd64 (1.14-2) ... #7 79.59 Setting up x11-common (1:7.7+19ubuntu14) ... #7 79.84 update-rc.d: warning: start and stop actions are no longer supported; falling back to defaults #7 79.85 invoke-rc.d: could not determine current runlevel #7 79.86 invoke-rc.d: policy-rc.d denied execution of start. #7 79.87 Setting up libsensors-config (1:3.6.0-2ubuntu1.1) ... #7 79.91 Setting up libmagic1:amd64 (1:5.38-4) ... #7 79.95 Setting up libxext6:amd64 (2:1.3.4-0ubuntu1) ... #7 79.97 Setting up libxcb-shm0:amd64 (1.14-2) ... #7 79.99 Setting up file (1:5.38-4) ... #7 80.01 Setting up libffi-dev:amd64 (3.3-4) ... #7 80.04 Setting up libpthread-stubs0-dev:amd64 (0.4-1) ... #7 80.06 Setting up libxxf86vm1:amd64 (1:1.1.4-1build1) ... #7 80.08 Setting up libllvm10:amd64 (1:10.0.0-4ubuntu1) ... #7 80.10 Setting up libxcb-present0:amd64 (1.14-2) ... #7 80.12 Setting up xtrans-dev (1.4.0-1) ... #7 80.15 Setting up libfontenc1:amd64 (1:1.1.4-0ubuntu1) ... #7 80.17 Setting up libz3-4:amd64 (4.8.7-4build1) ... #7 80.19 Setting up libglib2.0-data (2.64.6-1~ubuntu20.04.7) ... #7 80.21 Setting up libgmpxx4ldbl:amd64 (2:6.2.0+dfsg-4ubuntu0.1) ... #7 80.24 Setting up libpfm4:amd64 (4.10.1+git20-g7700f49-2) ... #7 80.26 Setting up libexpat1-dev:amd64 (2.2.9-1ubuntu0.6) ... #7 80.28 Setting up libsqlite3-dev:amd64 (3.31.1-4ubuntu0.6) ... #7 80.30 Setting up libgpg-error-dev (1.37-1) ... #7 80.32 Setting up libnspr4:amd64 (2:4.35-0ubuntu0.20.04.1) ... #7 80.35 Setting up libxfixes3:amd64 (1:5.0.3-2) ... #7 80.37 Setting up libxcb-sync1:amd64 (1.14-2) ... #7 80.39 Setting up uuid-dev:amd64 (2.34-0.1ubuntu9.6) ... #7 80.41 Setting up libllvm12:amd64 (1:12.0.0-3ubuntu1~20.04.5) ... #7 80.44 Setting up shared-mime-info (1.15-1) ... #7 83.97 Setting up libxinerama1:amd64 (2:1.1.4-2) ... #7 83.99 Setting up libxv1:amd64 (2:1.0.11-1) ... #7 84.01 Setting up libssl-dev:amd64 (1.1.1f-1ubuntu2.22) ... #7 84.03 Setting up libevent-2.1-7:amd64 (2.1.11-stable-1) ... #7 84.06 Setting up libxrandr2:amd64 (2:1.5.2-0ubuntu1) ... #7 84.08 Setting up libtcl8.6:amd64 (8.6.10+dfsg-1) ... #7 84.10 Setting up binfmt-support (2.2.0-2) ... #7 84.34 invoke-rc.d: could not determine current runlevel #7 84.35 invoke-rc.d: policy-rc.d denied execution of start. #7 84.35 Setting up icu-devtools (66.1-2ubuntu2.1) ... #7 84.37 Setting up pkg-config (0.29.1-0ubuntu4) ... #7 84.45 Setting up libgnutlsxx28:amd64 (3.6.13-2ubuntu1.11) ... #7 84.47 Setting up libsensors5:amd64 (1:3.6.0-2ubuntu1.1) ... #7 84.49 Setting up libidn2-dev:amd64 (2.2.0-2) ... #7 84.51 Setting up libglapi-mesa:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 84.54 Setting up libvulkan1:amd64 (1.2.131.2-1) ... #7 84.56 Setting up libutempter0:amd64 (1.1.6-4) ... #7 84.58 Setting up liblzma-dev:amd64 (5.2.4-1ubuntu1.1) ... #7 84.60 Setting up libxcb-dri2-0:amd64 (1.14-2) ... #7 84.63 Setting up zlib1g-dev:amd64 (1:1.2.11.dfsg-2ubuntu1.5) ... #7 84.65 Setting up libxshmfence1:amd64 (1.3-1) ... #7 84.67 Setting up libxcb-randr0:amd64 (1.14-2) ... #7 84.70 Setting up xorg-sgml-doctools (1:1.11-1) ... #7 84.72 Setting up libxss1:amd64 (1:1.2.3-1) ... #7 84.74 Setting up libxkbfile1:amd64 (1:1.1.0-1) ... #7 84.76 Setting up libmpdec2:amd64 (2.4.2-3) ... #7 84.78 Setting up libxmlsec1:amd64 (1.2.28-2) ... #7 84.81 Setting up libtasn1-6-dev:amd64 (4.16.0-2) ... #7 84.83 Setting up libdrm-common (2.4.107-8ubuntu1~20.04.2) ... #7 84.85 Setting up libxcomposite1:amd64 (1:0.4.5-1) ... #7 84.90 Setting up readline-common (8.0-4) ... #7 84.93 Setting up libicu-dev:amd64 (66.1-2ubuntu2.1) ... #7 84.95 Setting up libxmuu1:amd64 (2:1.1.3-0ubuntu1) ... #7 84.98 Setting up xbitmaps (1.1.1-2) ... #7 85.00 Setting up libp11-kit-dev:amd64 (0.23.20-1ubuntu0.1) ... #7 85.02 Setting up libbz2-dev:amd64 (1.0.8-2) ... #7 85.04 Setting up libwayland-client0:amd64 (1.18.0-1ubuntu0.1) ... #7 85.06 Setting up x11proto-dev (2019.2-1ubuntu1) ... #7 85.08 Setting up libnspr4-dev (2:4.35-0ubuntu0.20.04.1) ... #7 85.11 Setting up libz3-dev:amd64 (4.8.7-4build1) ... #7 85.13 Setting up libice6:amd64 (2:1.0.10-0ubuntu1) ... #7 85.15 Setting up tcl8.6 (8.6.10+dfsg-1) ... #7 85.18 Setting up libxft2:amd64 (2.3.3-0ubuntu1) ... #7 85.20 Setting up libgmp-dev:amd64 (2:6.2.0+dfsg-4ubuntu0.1) ... #7 85.22 Setting up libxau-dev:amd64 (1:1.0.9-0ubuntu1) ... #7 85.24 Setting up libxi6:amd64 (2:1.7.10-0ubuntu1) ... #7 85.27 Setting up nettle-dev:amd64 (3.5.1+really3.5.1-2ubuntu0.2) ... #7 85.29 Setting up libice-dev:amd64 (2:1.0.10-0ubuntu1) ... #7 85.31 Setting up libxmlsec1-openssl:amd64 (1.2.28-2) ... #7 85.34 Setting up libxtst6:amd64 (2:1.2.3-1) ... #7 85.36 Setting up tcl8.6-dev:amd64 (8.6.10+dfsg-1) ... #7 85.38 Setting up libtk8.6:amd64 (8.6.10-1) ... #7 85.40 Setting up libreadline8:amd64 (8.0-4) ... #7 85.42 Setting up libpng-dev:amd64 (1.6.37-2) ... #7 85.45 Setting up libxxf86dga1:amd64 (2:1.1.5-0ubuntu1) ... #7 85.47 Setting up libnss3:amd64 (2:3.98-0ubuntu0.20.04.2) ... #7 85.49 Setting up libreadline-dev:amd64 (8.0-4) ... #7 85.51 Setting up libclang-cpp10 (1:10.0.0-4ubuntu1) ... #7 85.54 Setting up llvm-10-runtime (1:10.0.0-4ubuntu1) ... #7 85.56 Setting up libfreetype-dev:amd64 (2.10.1-2ubuntu0.3) ... #7 85.59 Setting up libxdmcp-dev:amd64 (1:1.1.3-0ubuntu1) ... #7 85.61 Setting up libxml2-dev:amd64 (2.9.10+dfsg-5ubuntu0.20.04.7) ... #7 85.65 Setting up libunbound8:amd64 (1.9.4-2ubuntu1.5) ... #7 85.67 Setting up libgcrypt20-dev (1.8.5-5ubuntu1.1) ... #7 85.69 Setting up x11proto-core-dev (2019.2-1ubuntu1) ... #7 85.71 Setting up llvm-runtime (1:10.0-50~exp1) ... #7 85.74 Setting up libxmlsec1-gcrypt:amd64 (1.2.28-2) ... #7 85.76 Setting up libxmlsec1-nss:amd64 (1.2.28-2) ... #7 85.78 Setting up x11proto-xext-dev (2019.2-1ubuntu1) ... #7 85.80 Setting up libdrm2:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 85.82 Setting up libnss3-dev:amd64 (2:3.98-0ubuntu0.20.04.2) ... #7 85.85 Setting up tcl (8.6.9+1) ... #7 85.87 Setting up libpython3.8-stdlib:amd64 (3.8.10-0ubuntu1~20.04.9) ... #7 85.89 Setting up python3.8 (3.8.10-0ubuntu1~20.04.9) ... #7 86.58 Setting up libxmlsec1-gnutls:amd64 (1.2.28-2) ... #7 86.61 Setting up x11proto-scrnsaver-dev (2019.2-1ubuntu1) ... #7 86.63 Setting up libsm6:amd64 (2:1.2.3-1) ... #7 86.65 Setting up libpython3-stdlib:amd64 (3.8.2-0ubuntu2) ... #7 86.67 Setting up libdrm-amdgpu1:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.69 Setting up tk8.6 (8.6.10-1) ... #7 86.72 Setting up mesa-vulkan-drivers:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 86.75 Setting up libgnutls-dane0:amd64 (3.6.13-2ubuntu1.11) ... #7 86.77 Setting up libdrm-nouveau2:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.79 Setting up libxcb1-dev:amd64 (1.14-2) ... #7 86.81 Setting up libxslt1-dev:amd64 (1.1.34-4ubuntu0.20.04.1) ... #7 86.84 Setting up libsm-dev:amd64 (2:1.2.3-1) ... #7 86.86 Setting up libdrm-radeon1:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.88 Setting up llvm-10 (1:10.0.0-4ubuntu1) ... #7 86.90 Setting up libdrm-intel1:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.93 Setting up libgl1-mesa-dri:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 86.96 Setting up libx11-dev:amd64 (2:1.6.9-2ubuntu1.6) ... #7 86.99 Setting up python3 (3.8.2-0ubuntu2) ... #7 87.18 Setting up libfreetype6-dev:amd64 (2.10.1-2ubuntu0.3) ... #7 87.21 Setting up python3-pygments (2.3.1+dfsg-1ubuntu2.2) ... #7 87.83 Setting up llvm (1:10.0-50~exp1) ... #7 87.85 Setting up libxt6:amd64 (1:1.1.5-1) ... #7 87.87 Setting up tcl-dev:amd64 (8.6.9+1) ... #7 87.90 Setting up libxext-dev:amd64 (2:1.3.4-0ubuntu1) ... #7 87.92 Setting up tk (8.6.9+1) ... #7 87.94 Setting up libxrender-dev:amd64 (1:0.9.10-1) ... #7 87.96 Setting up python3-pkg-resources (45.2.0-1ubuntu0.1) ... #7 88.28 Setting up libgnutls28-dev:amd64 (3.6.13-2ubuntu1.11) ... #7 88.31 Setting up libxmu6:amd64 (2:1.1.3-0ubuntu1) ... #7 88.33 Setting up libfontconfig1-dev:amd64 (2.13.1-2ubuntu3) ... #7 88.35 Setting up libglx-mesa0:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 88.37 Setting up libglx0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 88.40 Setting up python3-yaml (5.3.1-1ubuntu0.1) ... #7 88.69 Setting up libxaw7:amd64 (2:1.0.13-1) ... #7 88.73 Setting up libxss-dev:amd64 (1:1.2.3-1) ... #7 88.75 Setting up libgl1:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 88.77 Setting up libxt-dev:amd64 (1:1.1.5-1) ... #7 88.79 Setting up libxmlsec1-dev (1.2.28-2) ... #7 88.82 Setting up x11-utils (7.7+5) ... #7 88.93 Setting up xterm (353-1ubuntu1.20.04.2) ... #7 89.00 update-alternatives: using /usr/bin/xterm to provide /usr/bin/x-terminal-emulator (x-terminal-emulator) in auto mode #7 89.00 update-alternatives: warning: skip creation of /usr/share/man/man1/x-terminal-emulator.1.gz because associated file /usr/share/man/man1/xterm.1.gz (of link group x-terminal-emulator) doesn't exist #7 89.02 update-alternatives: using /usr/bin/lxterm to provide /usr/bin/x-terminal-emulator (x-terminal-emulator) in auto mode #7 89.02 update-alternatives: warning: skip creation of /usr/share/man/man1/x-terminal-emulator.1.gz because associated file /usr/share/man/man1/lxterm.1.gz (of link group x-terminal-emulator) doesn't exist #7 89.04 Setting up libxft-dev:amd64 (2.3.3-0ubuntu1) ... #7 89.06 Setting up llvm-10-tools (1:10.0.0-4ubuntu1) ... #7 89.08 Setting up llvm-10-dev (1:10.0.0-4ubuntu1) ... #7 89.10 Setting up tk8.6-dev:amd64 (8.6.10-1) ... #7 89.12 Setting up tk-dev:amd64 (8.6.9+1) ... #7 89.14 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #7 DONE 90.5s #8 [ 4/17] RUN git clone --depth 1 --branch $(git ls-remote --tags --sort=v:refname https://github.com/pyenv/pyenv.git | grep -o -E 'v[1-9]+(\\.[1-9]+)+$' | tail -1) https://github.com/pyenv/pyenv.git /root/.pyenv #8 0.857 Cloning into '/root/.pyenv'... #8 1.882 Note: switching to 'aabaa892438d05d96775042a129b1580fcd60105'. #8 1.882 #8 1.882 You are in 'detached HEAD' state. You can look around, make experimental #8 1.882 changes and commit them, and you can discard any commits you make in this #8 1.882 state without impacting any branches by switching back to a branch. #8 1.882 #8 1.882 If you want to create a new branch to retain commits you create, you may #8 1.882 do so (now or later) by using -c with the switch command. Example: #8 1.882 #8 1.882 git switch -c #8 1.882 #8 1.882 Or undo this operation with: #8 1.882 #8 1.882 git switch - #8 1.882 #8 1.882 Turn off this advice by setting config variable advice.detachedHead to false #8 1.882 #8 DONE 2.0s #9 [ 5/17] RUN apt install -y python3.8 python3.8-distutils && ln -s -f $(which python3.8) /usr/bin/python && wget https://bootstrap.pypa.io/get-pip.py -O /tmp/get-pip.py && python /tmp/get-pip.py #9 0.502 #9 0.502 WARNING: apt does not have a stable CLI interface. Use with caution in scripts. #9 0.502 #9 0.563 Reading package lists... #9 1.644 Building dependency tree... #9 1.808 Reading state information... #9 2.060 python3.8 is already the newest version (3.8.10-0ubuntu1~20.04.9). #9 2.060 python3.8 set to manually installed. #9 2.060 The following NEW packages will be installed: #9 2.060 python3-distutils python3-lib2to3 #9 2.331 0 upgraded, 2 newly installed, 0 to remove and 0 not upgraded. #9 2.331 Need to get 218 kB of archives. #9 2.331 After this operation, 2115 kB of additional disk space will be used. #9 2.331 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-lib2to3 all 3.8.10-0ubuntu1~20.04 [76.3 kB] #9 2.755 Get:2 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-distutils all 3.8.10-0ubuntu1~20.04 [141 kB] #9 3.105 debconf: delaying package configuration, since apt-utils is not installed #9 3.173 Fetched 218 kB in 1s (254 kB/s) #9 3.227 Selecting previously unselected package python3-lib2to3. #9 3.227 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 24296 files and directories currently installed.) #9 3.256 Preparing to unpack .../python3-lib2to3_3.8.10-0ubuntu1~20.04_all.deb ... #9 3.263 Unpacking python3-lib2to3 (3.8.10-0ubuntu1~20.04) ... #9 3.357 Selecting previously unselected package python3-distutils. #9 3.358 Preparing to unpack .../python3-distutils_3.8.10-0ubuntu1~20.04_all.deb ... #9 3.365 Unpacking python3-distutils (3.8.10-0ubuntu1~20.04) ... #9 3.486 Setting up python3-lib2to3 (3.8.10-0ubuntu1~20.04) ... #9 3.636 Setting up python3-distutils (3.8.10-0ubuntu1~20.04) ... #9 3.862 --2024-05-16 20:23:48-- https://bootstrap.pypa.io/get-pip.py #9 3.866 Resolving bootstrap.pypa.io (bootstrap.pypa.io)... 151.101.64.175, 151.101.128.175, 151.101.0.175, ... #9 3.930 Connecting to bootstrap.pypa.io (bootstrap.pypa.io)|151.101.64.175|:443... connected. #9 4.180 HTTP request sent, awaiting response... 200 OK #9 4.192 Length: 2635835 (2.5M) [text/x-python] #9 4.192 Saving to: '/tmp/get-pip.py' #9 4.206 #9 4.206 0K .......... .......... .......... .......... .......... 1% 968K 3s #9 4.244 50K .......... .......... .......... .......... .......... 3% 4.78M 2s #9 4.254 100K .......... .......... .......... .......... .......... 5% 7.08M 1s #9 4.261 150K .......... .......... .......... .......... .......... 7% 7.48M 1s #9 4.267 200K .......... .......... .......... .......... .......... 9% 6.58M 1s #9 4.275 250K .......... .......... .......... .......... .......... 11% 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Collecting setuptools #9 6.909 Downloading setuptools-69.5.1-py3-none-any.whl.metadata (6.2 kB) #9 6.992 Collecting wheel #9 7.007 Downloading wheel-0.43.0-py3-none-any.whl.metadata (2.2 kB) #9 7.077 Downloading pip-24.0-py3-none-any.whl (2.1 MB) #9 7.224 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 2.1/2.1 MB 15.0 MB/s eta 0:00:00 #9 7.243 Downloading setuptools-69.5.1-py3-none-any.whl (894 kB) #9 7.297 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 894.6/894.6 kB 18.5 MB/s eta 0:00:00 #9 7.315 Downloading wheel-0.43.0-py3-none-any.whl (65 kB) #9 7.331 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 65.8/65.8 kB 4.8 MB/s eta 0:00:00 #9 7.407 Installing collected packages: wheel, setuptools, pip #9 9.087 Successfully installed pip-24.0 setuptools-69.5.1 wheel-0.43.0 #9 9.088 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #9 DONE 9.6s #10 [ 6/17] RUN pip install virtualenv #10 1.047 Collecting virtualenv #10 1.095 Downloading virtualenv-20.26.2-py3-none-any.whl.metadata (4.4 kB) #10 1.182 Collecting distlib < 1,>=0.3.7 (from virtualenv) #10 1.197 Downloading distlib-0.3.8-py2.py3-none-any.whl.metadata (5.1 kB) #10 1.276 Collecting filelock < 4,>=3.12.2 (from virtualenv) #10 1.291 Downloading filelock-3.14.0-py3-none-any.whl.metadata (2.8 kB) #10 1.368 Collecting platformdirs < 5,>=3.9.1 (from virtualenv) #10 1.383 Downloading platformdirs-4.2.2-py3-none-any.whl.metadata (11 kB) #10 1.439 Downloading virtualenv-20.26.2-py3-none-any.whl (3.9 MB) #10 1.656 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.9/3.9 MB 18.3 MB/s eta 0:00:00 #10 1.673 Downloading distlib-0.3.8-py2.py3-none-any.whl (468 kB) #10 1.706 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 468.9/468.9 kB 17.0 MB/s eta 0:00:00 #10 1.724 Downloading filelock-3.14.0-py3-none-any.whl (12 kB) #10 1.752 Downloading platformdirs-4.2.2-py3-none-any.whl (18 kB) #10 1.871 Installing collected packages: distlib, platformdirs, filelock, virtualenv #10 2.123 Successfully installed distlib-0.3.8 filelock-3.14.0 platformdirs-4.2.2 virtualenv-20.26.2 #10 2.123 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #10 DONE 2.3s #11 [ 7/17] RUN apt-get install -y --no-install-recommends openjdk-11-jdk maven #11 0.605 Reading package lists... #11 1.699 Building dependency tree... #11 1.870 Reading state information... #11 2.070 The following additional packages will be installed: #11 2.070 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.070 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.070 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.070 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.070 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.070 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.070 libguice-java libharfbuzz0b libhawtjni-runtime-java libjansi-java #11 2.070 libjansi-native-java libjsr305-java liblcms2-2 libmaven-parent-java #11 2.070 libmaven-resolver-java libmaven-shared-utils-java libmaven3-core-java #11 2.070 libpcsclite1 libplexus-cipher-java libplexus-classworlds-java #11 2.070 libplexus-component-annotations-java libplexus-interpolation-java #11 2.070 libplexus-sec-dispatcher-java libplexus-utils2-java libsisu-inject-java #11 2.070 libsisu-plexus-java libslf4j-java libwagon-file-java #11 2.071 libwagon-http-shaded-java libwagon-provider-api-java openjdk-11-jdk-headless #11 2.071 openjdk-11-jre openjdk-11-jre-headless #11 2.072 Suggested packages: #11 2.072 default-jre libaopalliance-java-doc libasound2-plugins alsa-utils #11 2.072 libatinject-jsr330-api-java-doc libservlet3.1-java libcommons-io-java-doc #11 2.072 libcommons-lang3-java-doc cups-common libasm-java libcglib-java #11 2.072 libjsr305-java-doc liblcms2-utils libmaven-shared-utils-java-doc #11 2.072 liblogback-java pcscd libplexus-cipher-java-doc #11 2.072 libplexus-classworlds-java-doc libplexus-sec-dispatcher-java-doc #11 2.072 libplexus-utils2-java-doc junit4 testng libcommons-logging-java #11 2.072 liblog4j1.2-java openjdk-11-demo openjdk-11-source visualvm libnss-mdns #11 2.072 fonts-dejavu-extra fonts-ipafont-gothic fonts-ipafont-mincho #11 2.072 fonts-wqy-microhei | fonts-wqy-zenhei fonts-indic #11 2.072 Recommended packages: #11 2.072 alsa-ucm-conf alsa-topology-conf dbus libatk-wrapper-java-jni #11 2.072 fonts-dejavu-extra #11 2.267 The following NEW packages will be installed: #11 2.268 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.268 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.268 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.268 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.268 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.268 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.268 libguice-java libharfbuzz0b libhawtjni-runtime-java libjansi-java #11 2.268 libjansi-native-java libjsr305-java liblcms2-2 libmaven-parent-java #11 2.268 libmaven-resolver-java libmaven-shared-utils-java libmaven3-core-java #11 2.268 libpcsclite1 libplexus-cipher-java libplexus-classworlds-java #11 2.268 libplexus-component-annotations-java libplexus-interpolation-java #11 2.268 libplexus-sec-dispatcher-java libplexus-utils2-java libsisu-inject-java #11 2.268 libsisu-plexus-java libslf4j-java libwagon-file-java #11 2.269 libwagon-http-shaded-java libwagon-provider-api-java maven openjdk-11-jdk #11 2.269 openjdk-11-jdk-headless openjdk-11-jre openjdk-11-jre-headless #11 2.386 0 upgraded, 51 newly installed, 0 to remove and 0 not upgraded. #11 2.386 Need to get 125 MB of archives. #11 2.386 After this operation, 277 MB of additional disk space will be used. #11 2.386 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdbus-1-3 amd64 1.12.16-2ubuntu2.3 [179 kB] #11 2.602 Get:2 http://archive.ubuntu.com/ubuntu focal/main amd64 java-common all 0.72 [6816 B] #11 2.603 Get:3 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libavahi-common-data amd64 0.7-4ubuntu7.3 [21.4 kB] #11 2.607 Get:4 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libavahi-common3 amd64 0.7-4ubuntu7.3 [21.9 kB] #11 2.610 Get:5 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libavahi-client3 amd64 0.7-4ubuntu7.3 [25.5 kB] #11 2.615 Get:6 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcups2 amd64 2.3.1-9ubuntu1.6 [233 kB] #11 2.667 Get:7 http://archive.ubuntu.com/ubuntu focal/main amd64 liblcms2-2 amd64 2.9-4 [140 kB] #11 2.690 Get:8 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libasound2-data all 1.2.2-2.1ubuntu2.5 [20.1 kB] #11 2.694 Get:9 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libasound2 amd64 1.2.2-2.1ubuntu2.5 [335 kB] #11 2.753 Get:10 http://archive.ubuntu.com/ubuntu focal/main amd64 libgraphite2-3 amd64 1.3.13-11build1 [73.5 kB] #11 2.765 Get:11 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libharfbuzz0b amd64 2.6.4-1ubuntu4.2 [391 kB] #11 2.928 Get:12 http://archive.ubuntu.com/ubuntu focal/main amd64 libpcsclite1 amd64 1.8.26-3 [22.0 kB] #11 3.024 Get:13 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jre-headless amd64 11.0.22+7-0ubuntu2~20.04.1 [38.3 MB] #11 10.09 Get:14 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 ca-certificates-java all 20190405ubuntu1.1 [12.4 kB] #11 10.10 Get:15 http://archive.ubuntu.com/ubuntu focal/universe amd64 libapache-pom-java all 18-1 [4720 B] #11 10.10 Get:16 http://archive.ubuntu.com/ubuntu focal/universe amd64 libatinject-jsr330-api-java all 1.0+ds1-5 [5348 B] #11 10.10 Get:17 http://archive.ubuntu.com/ubuntu focal/universe amd64 libgeronimo-interceptor-3.0-spec-java all 1.0.1-4fakesync [8616 B] #11 10.10 Get:18 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcdi-api-java all 1.2-2 [54.5 kB] #11 10.10 Get:19 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcommons-cli-java all 1.4-1 [53.8 kB] #11 10.10 Get:20 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcommons-parent-java all 43-1 [10.8 kB] #11 10.10 Get:21 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libcommons-io-java all 2.6-2ubuntu0.20.04.1 [198 kB] #11 10.11 Get:22 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcommons-lang3-java all 3.8-2 [458 kB] #11 10.28 Get:23 http://archive.ubuntu.com/ubuntu focal/universe amd64 libgeronimo-annotation-1.3-spec-java all 1.0-1 [10.7 kB] #11 10.33 Get:24 http://archive.ubuntu.com/ubuntu focal/main amd64 libgif7 amd64 5.1.9-1 [32.2 kB] #11 10.38 Get:25 http://archive.ubuntu.com/ubuntu focal/universe amd64 libjsr305-java all 0.1~+svn49-11 [27.0 kB] #11 10.41 Get:26 http://archive.ubuntu.com/ubuntu focal/universe amd64 libguava-java all 19.0-1 [2028 kB] #11 10.83 Get:27 http://archive.ubuntu.com/ubuntu focal/universe amd64 libaopalliance-java all 20070526-6 [9084 B] #11 10.83 Get:28 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libguice-java all 4.2.1-1ubuntu0.2 [1410 kB] #11 11.08 Get:29 http://archive.ubuntu.com/ubuntu focal/universe amd64 libhawtjni-runtime-java all 1.17-1 [28.8 kB] #11 11.08 Get:30 http://archive.ubuntu.com/ubuntu focal/universe amd64 libjansi-native-java all 1.8-1 [23.8 kB] #11 11.08 Get:31 http://archive.ubuntu.com/ubuntu focal/universe amd64 libjansi-java all 1.18-1 [56.8 kB] #11 11.09 Get:32 http://archive.ubuntu.com/ubuntu focal/universe amd64 libmaven-parent-java all 31-2 [5140 B] #11 11.09 Get:33 http://archive.ubuntu.com/ubuntu focal/universe amd64 libplexus-utils2-java all 3.3.0-1 [250 kB] #11 11.23 Get:34 http://archive.ubuntu.com/ubuntu focal/universe amd64 libwagon-provider-api-java all 3.3.4-1 [48.5 kB] #11 11.36 Get:35 http://archive.ubuntu.com/ubuntu focal/universe amd64 libmaven-resolver-java all 1.4.1-1 [555 kB] #11 11.52 Get:36 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libmaven-shared-utils-java all 3.3.0-1ubuntu0.20.04.1 [149 kB] #11 11.55 Get:37 http://archive.ubuntu.com/ubuntu focal/universe amd64 libplexus-cipher-java all 1.7-3 [15.1 kB] #11 11.55 Get:38 http://archive.ubuntu.com/ubuntu focal/universe amd64 libplexus-classworlds-java all 2.6.0-1 [49.4 kB] #11 11.56 Get:39 http://archive.ubuntu.com/ubuntu focal/universe amd64 libplexus-component-annotations-java all 2.1.0-1 [6564 B] #11 11.56 Get:40 http://archive.ubuntu.com/ubuntu focal/universe amd64 libplexus-interpolation-java all 1.26-1 [76.8 kB] #11 11.58 Get:41 http://archive.ubuntu.com/ubuntu focal/universe amd64 libplexus-sec-dispatcher-java all 1.4-4 [28.1 kB] #11 11.58 Get:42 http://archive.ubuntu.com/ubuntu focal/universe amd64 libslf4j-java all 1.7.25-3 [141 kB] #11 11.61 Get:43 http://archive.ubuntu.com/ubuntu focal/universe amd64 libsisu-inject-java all 0.3.3-1 [347 kB] #11 11.67 Get:44 http://archive.ubuntu.com/ubuntu focal/universe amd64 libsisu-plexus-java all 0.3.3-3 [182 kB] #11 11.79 Get:45 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libmaven3-core-java all 3.6.3-1ubuntu0.1 [1535 kB] #11 12.26 Get:46 http://archive.ubuntu.com/ubuntu focal/universe amd64 libwagon-file-java all 3.3.4-1 [7928 B] #11 12.26 Get:47 http://archive.ubuntu.com/ubuntu focal/universe amd64 libwagon-http-shaded-java all 3.3.4-1 [1855 kB] #11 12.59 Get:48 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 maven all 3.6.3-1ubuntu0.1 [17.8 kB] #11 12.60 Get:49 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jre amd64 11.0.22+7-0ubuntu2~20.04.1 [194 kB] #11 12.63 Get:50 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jdk-headless amd64 11.0.22+7-0ubuntu2~20.04.1 [73.7 MB] #11 25.68 Get:51 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jdk amd64 11.0.22+7-0ubuntu2~20.04.1 [1338 kB] #11 26.04 debconf: delaying package configuration, since apt-utils is not installed #11 26.11 Fetched 125 MB in 24s (5286 kB/s) #11 26.18 Selecting previously unselected package libdbus-1-3:amd64. #11 26.18 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 24567 files and directories currently installed.) #11 26.21 Preparing to unpack .../00-libdbus-1-3_1.12.16-2ubuntu2.3_amd64.deb ... #11 26.22 Unpacking libdbus-1-3:amd64 (1.12.16-2ubuntu2.3) ... #11 26.33 Selecting previously unselected package java-common. #11 26.33 Preparing to unpack .../01-java-common_0.72_all.deb ... #11 26.34 Unpacking java-common (0.72) ... #11 26.40 Selecting previously unselected package libavahi-common-data:amd64. #11 26.41 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previously unselected package libasound2-data. #11 26.91 Preparing to unpack .../07-libasound2-data_1.2.2-2.1ubuntu2.5_all.deb ... #11 26.91 Unpacking libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 27.01 Selecting previously unselected package libasound2:amd64. #11 27.01 Preparing to unpack .../08-libasound2_1.2.2-2.1ubuntu2.5_amd64.deb ... #11 27.02 Unpacking libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 27.14 Selecting previously unselected package libgraphite2-3:amd64. #11 27.14 Preparing to unpack .../09-libgraphite2-3_1.3.13-11build1_amd64.deb ... #11 27.15 Unpacking libgraphite2-3:amd64 (1.3.13-11build1) ... #11 27.24 Selecting previously unselected package libharfbuzz0b:amd64. #11 27.24 Preparing to unpack .../10-libharfbuzz0b_2.6.4-1ubuntu4.2_amd64.deb ... #11 27.25 Unpacking libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 27.37 Selecting previously unselected package libpcsclite1:amd64. #11 27.38 Preparing to unpack .../11-libpcsclite1_1.8.26-3_amd64.deb ... #11 27.38 Unpacking 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libgeronimo-interceptor-3.0-spec-java. #11 31.24 Preparing to unpack .../16-libgeronimo-interceptor-3.0-spec-java_1.0.1-4fakesync_all.deb ... #11 31.25 Unpacking libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 31.31 Selecting previously unselected package libcdi-api-java. #11 31.31 Preparing to unpack .../17-libcdi-api-java_1.2-2_all.deb ... #11 31.32 Unpacking libcdi-api-java (1.2-2) ... #11 31.39 Selecting previously unselected package libcommons-cli-java. #11 31.39 Preparing to unpack .../18-libcommons-cli-java_1.4-1_all.deb ... #11 31.40 Unpacking libcommons-cli-java (1.4-1) ... #11 31.47 Selecting previously unselected package libcommons-parent-java. #11 31.48 Preparing to unpack .../19-libcommons-parent-java_43-1_all.deb ... #11 31.49 Unpacking libcommons-parent-java (43-1) ... #11 31.55 Selecting previously unselected package libcommons-io-java. #11 31.56 Preparing to unpack .../20-libcommons-io-java_2.6-2ubuntu0.20.04.1_all.deb ... #11 31.56 Unpacking libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 31.66 Selecting previously unselected package libcommons-lang3-java. #11 31.66 Preparing to unpack .../21-libcommons-lang3-java_3.8-2_all.deb ... #11 31.67 Unpacking libcommons-lang3-java (3.8-2) ... #11 31.78 Selecting previously unselected package libgeronimo-annotation-1.3-spec-java. #11 31.78 Preparing to unpack .../22-libgeronimo-annotation-1.3-spec-java_1.0-1_all.deb ... #11 31.79 Unpacking libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 31.86 Selecting previously unselected package libgif7:amd64. #11 31.87 Preparing to unpack .../23-libgif7_5.1.9-1_amd64.deb ... #11 31.88 Unpacking libgif7:amd64 (5.1.9-1) ... #11 31.95 Selecting previously unselected package libjsr305-java. #11 31.95 Preparing to unpack .../24-libjsr305-java_0.1~+svn49-11_all.deb ... #11 31.96 Unpacking libjsr305-java (0.1~+svn49-11) ... #11 32.03 Selecting previously unselected package libguava-java. #11 32.03 Preparing to unpack .../25-libguava-java_19.0-1_all.deb ... #11 32.04 Unpacking libguava-java (19.0-1) ... #11 32.29 Selecting previously unselected package libaopalliance-java. #11 32.29 Preparing to unpack .../26-libaopalliance-java_20070526-6_all.deb ... #11 32.30 Unpacking libaopalliance-java (20070526-6) ... #11 32.36 Selecting previously unselected package libguice-java. #11 32.37 Preparing to unpack .../27-libguice-java_4.2.1-1ubuntu0.2_all.deb ... #11 32.37 Unpacking libguice-java (4.2.1-1ubuntu0.2) ... #11 32.59 Selecting previously unselected package libhawtjni-runtime-java. #11 32.60 Preparing to unpack .../28-libhawtjni-runtime-java_1.17-1_all.deb ... #11 32.60 Unpacking libhawtjni-runtime-java (1.17-1) ... #11 32.67 Selecting previously unselected package libjansi-native-java. #11 32.68 Preparing to unpack .../29-libjansi-native-java_1.8-1_all.deb ... #11 32.68 Unpacking libjansi-native-java (1.8-1) ... #11 32.75 Selecting previously unselected package libjansi-java. #11 32.75 Preparing to unpack .../30-libjansi-java_1.18-1_all.deb ... #11 32.76 Unpacking libjansi-java (1.18-1) ... #11 32.83 Selecting previously unselected package libmaven-parent-java. #11 32.84 Preparing to unpack .../31-libmaven-parent-java_31-2_all.deb ... #11 32.85 Unpacking libmaven-parent-java (31-2) ... #11 32.91 Selecting previously unselected package libplexus-utils2-java. #11 32.91 Preparing to unpack .../32-libplexus-utils2-java_3.3.0-1_all.deb ... #11 32.92 Unpacking libplexus-utils2-java (3.3.0-1) ... #11 33.02 Selecting previously unselected package libwagon-provider-api-java. #11 33.02 Preparing to unpack .../33-libwagon-provider-api-java_3.3.4-1_all.deb ... #11 33.03 Unpacking libwagon-provider-api-java (3.3.4-1) ... #11 33.10 Selecting previously unselected package libmaven-resolver-java. #11 33.11 Preparing to unpack .../34-libmaven-resolver-java_1.4.1-1_all.deb ... #11 33.12 Unpacking libmaven-resolver-java (1.4.1-1) ... #11 33.26 Selecting previously unselected package libmaven-shared-utils-java. #11 33.27 Preparing to unpack .../35-libmaven-shared-utils-java_3.3.0-1ubuntu0.20.04.1_all.deb ... #11 33.27 Unpacking libmaven-shared-utils-java (3.3.0-1ubuntu0.20.04.1) ... #11 33.36 Selecting previously unselected package libplexus-cipher-java. #11 33.37 Preparing to unpack .../36-libplexus-cipher-java_1.7-3_all.deb ... #11 33.37 Unpacking libplexus-cipher-java (1.7-3) ... #11 33.44 Selecting previously unselected package libplexus-classworlds-java. #11 33.45 Preparing to unpack .../37-libplexus-classworlds-java_2.6.0-1_all.deb ... #11 33.45 Unpacking libplexus-classworlds-java (2.6.0-1) ... #11 33.53 Selecting previously unselected package libplexus-component-annotations-java. #11 33.53 Preparing to unpack .../38-libplexus-component-annotations-java_2.1.0-1_all.deb ... #11 33.54 Unpacking libplexus-component-annotations-java (2.1.0-1) ... #11 33.60 Selecting previously unselected package libplexus-interpolation-java. #11 33.60 Preparing to unpack .../39-libplexus-interpolation-java_1.26-1_all.deb ... #11 33.61 Unpacking libplexus-interpolation-java (1.26-1) ... #11 33.69 Selecting previously unselected package libplexus-sec-dispatcher-java. #11 33.70 Preparing to unpack .../40-libplexus-sec-dispatcher-java_1.4-4_all.deb ... #11 33.70 Unpacking libplexus-sec-dispatcher-java (1.4-4) ... #11 33.77 Selecting previously unselected package libslf4j-java. #11 33.77 Preparing to unpack .../41-libslf4j-java_1.7.25-3_all.deb ... #11 33.78 Unpacking libslf4j-java (1.7.25-3) ... #11 33.89 Selecting previously unselected package libsisu-inject-java. #11 33.89 Preparing to unpack .../42-libsisu-inject-java_0.3.3-1_all.deb ... #11 33.90 Unpacking libsisu-inject-java (0.3.3-1) ... #11 34.01 Selecting previously unselected package libsisu-plexus-java. #11 34.02 Preparing to unpack .../43-libsisu-plexus-java_0.3.3-3_all.deb ... #11 34.02 Unpacking libsisu-plexus-java (0.3.3-3) ... #11 34.12 Selecting previously unselected package libmaven3-core-java. #11 34.12 Preparing to unpack .../44-libmaven3-core-java_3.6.3-1ubuntu0.1_all.deb ... #11 34.13 Unpacking libmaven3-core-java (3.6.3-1ubuntu0.1) ... #11 34.35 Selecting previously unselected package libwagon-file-java. #11 34.36 Preparing to unpack .../45-libwagon-file-java_3.3.4-1_all.deb ... #11 34.36 Unpacking libwagon-file-java (3.3.4-1) ... #11 34.42 Selecting previously unselected package libwagon-http-shaded-java. #11 34.43 Preparing to unpack .../46-libwagon-http-shaded-java_3.3.4-1_all.deb ... #11 34.43 Unpacking libwagon-http-shaded-java (3.3.4-1) ... #11 34.68 Selecting previously unselected package maven. #11 34.68 Preparing to unpack .../47-maven_3.6.3-1ubuntu0.1_all.deb ... #11 34.69 Unpacking maven (3.6.3-1ubuntu0.1) ... #11 34.76 Selecting previously unselected package openjdk-11-jre:amd64. #11 34.76 Preparing to unpack .../48-openjdk-11-jre_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 34.77 Unpacking openjdk-11-jre:amd64 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libcommons-cli-java (1.4-1) ... #11 40.34 Setting up libplexus-component-annotations-java (2.1.0-1) ... #11 40.38 Setting up libplexus-cipher-java (1.7-3) ... #11 40.40 Setting up libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 40.42 Setting up libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 40.44 Setting up libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 40.46 Setting up libapache-pom-java (18-1) ... #11 40.48 Setting up libavahi-common-data:amd64 (0.7-4ubuntu7.3) ... #11 40.50 Setting up libatinject-jsr330-api-java (1.0+ds1-5) ... #11 40.53 Setting up libdbus-1-3:amd64 (1.12.16-2ubuntu2.3) ... #11 40.55 Setting up libplexus-interpolation-java (1.26-1) ... #11 40.57 Setting up libpcsclite1:amd64 (1.8.26-3) ... #11 40.59 Setting up libgif7:amd64 (5.1.9-1) ... #11 40.61 Setting up libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 40.63 Setting up libplexus-sec-dispatcher-java (1.4-4) ... #11 40.65 Setting up libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 40.67 Setting up libwagon-http-shaded-java (3.3.4-1) ... #11 40.69 Setting up libcdi-api-java (1.2-2) ... #11 40.71 Setting up libhawtjni-runtime-java (1.17-1) ... #11 40.73 Setting up libwagon-provider-api-java (3.3.4-1) ... #11 40.75 Setting up libmaven-parent-java (31-2) ... #11 40.77 Setting up libcommons-parent-java (43-1) ... #11 40.79 Setting up libavahi-common3:amd64 (0.7-4ubuntu7.3) ... #11 40.81 Setting up libmaven-resolver-java (1.4.1-1) ... #11 40.84 Setting up libguava-java (19.0-1) ... #11 40.86 Setting up libcommons-lang3-java (3.8-2) ... #11 40.88 Setting up libjansi-native-java (1.8-1) ... #11 40.90 Setting up libwagon-file-java (3.3.4-1) ... #11 40.92 Setting up libavahi-client3:amd64 (0.7-4ubuntu7.3) ... #11 40.94 Setting up libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 40.96 Setting up libguice-java (4.2.1-1ubuntu0.2) ... #11 40.98 Setting up libjansi-java (1.18-1) ... #11 41.00 Setting up libcups2:amd64 (2.3.1-9ubuntu1.6) ... #11 41.02 Setting up libmaven-shared-utils-java 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/usr/lib/jvm/java-11-openjdk-amd64/bin/pack200 to provide /usr/bin/pack200 (pack200) in auto mode #11 41.36 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/unpack200 to provide /usr/bin/unpack200 (unpack200) in auto mode #11 41.37 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/lib/jexec to provide /usr/bin/jexec (jexec) in auto mode #11 41.92 Setting up openjdk-11-jre:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.95 Setting up openjdk-11-jdk-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.98 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jar to provide /usr/bin/jar (jar) in auto mode #11 41.99 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jarsigner to provide /usr/bin/jarsigner (jarsigner) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javac to provide /usr/bin/javac (javac) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javadoc to provide /usr/bin/javadoc (javadoc) in auto mode #11 42.01 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javap to provide /usr/bin/javap (javap) in auto mode #11 42.02 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jcmd to provide /usr/bin/jcmd (jcmd) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdb to provide /usr/bin/jdb (jdb) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeprscan to provide /usr/bin/jdeprscan (jdeprscan) in auto mode #11 42.04 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeps to provide /usr/bin/jdeps (jdeps) in auto mode #11 42.05 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jfr to provide /usr/bin/jfr (jfr) in auto mode #11 42.06 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jimage to provide /usr/bin/jimage (jimage) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jinfo to provide /usr/bin/jinfo (jinfo) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jlink to provide /usr/bin/jlink (jlink) in auto mode #11 42.08 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmap to provide /usr/bin/jmap (jmap) in auto mode #11 42.09 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmod to provide /usr/bin/jmod (jmod) in auto mode #11 42.10 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jps to provide /usr/bin/jps (jps) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jrunscript to provide /usr/bin/jrunscript (jrunscript) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jshell to provide /usr/bin/jshell (jshell) in auto mode #11 42.12 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstack to provide /usr/bin/jstack (jstack) in auto mode #11 42.13 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstat to provide /usr/bin/jstat (jstat) in auto mode #11 42.14 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstatd to provide /usr/bin/jstatd (jstatd) in auto mode #11 42.15 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmic to provide /usr/bin/rmic (rmic) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/serialver to provide /usr/bin/serialver (serialver) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jaotc to provide /usr/bin/jaotc (jaotc) in auto mode #11 42.17 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jhsdb to provide /usr/bin/jhsdb (jhsdb) in auto mode #11 42.19 Setting up maven (3.6.3-1ubuntu0.1) ... #11 42.23 update-alternatives: using /usr/share/maven/bin/mvn to provide /usr/bin/mvn (mvn) in auto mode #11 42.25 Setting up openjdk-11-jdk:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 42.28 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jconsole to provide /usr/bin/jconsole (jconsole) in auto mode #11 42.29 Setting up ca-certificates-java (20190405ubuntu1.1) ... #11 42.35 head: cannot open '/etc/ssl/certs/java/cacerts' for reading: No such file or directory #11 42.65 Adding debian:COMODO_RSA_Certification_Authority.pem #11 42.66 Adding debian:Hongkong_Post_Root_CA_1.pem #11 42.67 Adding debian:Entrust_Root_Certification_Authority_-_EC1.pem #11 42.67 Adding debian:Certainly_Root_R1.pem #11 42.68 Adding debian:Amazon_Root_CA_2.pem #11 42.68 Adding debian:Certainly_Root_E1.pem #11 42.68 Adding debian:AffirmTrust_Premium_ECC.pem #11 42.68 Adding debian:ANF_Secure_Server_Root_CA.pem #11 42.68 Adding debian:GlobalSign_Root_R46.pem #11 42.69 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068.pem #11 42.69 Adding debian:Certum_Trusted_Root_CA.pem #11 42.69 Adding debian:Go_Daddy_Root_Certificate_Authority_-_G2.pem #11 42.69 Adding debian:SecureSign_RootCA11.pem #11 42.69 Adding debian:Amazon_Root_CA_4.pem #11 42.70 Adding debian:GDCA_TrustAUTH_R5_ROOT.pem #11 42.70 Adding debian:T-TeleSec_GlobalRoot_Class_2.pem #11 42.70 Adding debian:Trustwave_Global_ECC_P256_Certification_Authority.pem #11 42.70 Adding debian:GTS_Root_R1.pem #11 42.71 Adding debian:Izenpe.com.pem #11 42.71 Adding debian:CFCA_EV_ROOT.pem #11 42.71 Adding debian:DigiCert_Global_Root_G2.pem #11 42.71 Adding debian:XRamp_Global_CA_Root.pem #11 42.72 Adding debian:SSL.com_EV_Root_Certification_Authority_RSA_R2.pem #11 42.72 Adding debian:Security_Communication_RootCA3.pem #11 42.72 Adding debian:Starfield_Services_Root_Certificate_Authority_-_G2.pem #11 42.72 Adding debian:Amazon_Root_CA_1.pem #11 42.72 Adding debian:DigiCert_Assured_ID_Root_CA.pem #11 42.72 Adding debian:SSL.com_Root_Certification_Authority_RSA.pem #11 42.73 Adding debian:Telia_Root_CA_v2.pem #11 42.73 Adding debian:SwissSign_Gold_CA_-_G2.pem #11 42.73 Adding debian:GLOBALTRUST_2020.pem #11 42.73 Adding debian:emSign_ECC_Root_CA_-_G3.pem #11 42.73 Adding debian:Microsoft_RSA_Root_Certificate_Authority_2017.pem #11 42.74 Adding debian:TunTrust_Root_CA.pem #11 42.74 Adding debian:Security_Communication_ECC_RootCA1.pem #11 42.74 Adding debian:DigiCert_TLS_ECC_P384_Root_G5.pem #11 42.74 Adding debian:vTrus_Root_CA.pem #11 42.75 Adding debian:Certum_EC-384_CA.pem #11 42.76 Adding debian:CA_Disig_Root_R2.pem #11 42.76 Adding debian:emSign_Root_CA_-_C1.pem #11 42.77 Adding debian:AffirmTrust_Commercial.pem #11 42.77 Adding debian:Entrust_Root_Certification_Authority_-_G4.pem #11 42.78 Adding debian:Starfield_Class_2_CA.pem #11 42.78 Adding debian:Baltimore_CyberTrust_Root.pem #11 42.79 Adding debian:GTS_Root_R4.pem #11 42.79 Adding debian:Starfield_Root_Certificate_Authority_-_G2.pem #11 42.80 Adding debian:QuoVadis_Root_CA_1_G3.pem #11 42.81 Adding debian:DigiCert_Trusted_Root_G4.pem #11 42.81 Adding debian:DigiCert_High_Assurance_EV_Root_CA.pem #11 42.82 Adding debian:AC_RAIZ_FNMT-RCM.pem #11 42.82 Adding debian:Comodo_AAA_Services_root.pem #11 42.82 Adding debian:emSign_ECC_Root_CA_-_C3.pem #11 42.83 Adding debian:Entrust_Root_Certification_Authority.pem #11 42.84 Adding debian:D-TRUST_EV_Root_CA_1_2020.pem #11 42.85 Adding debian:Certigna_Root_CA.pem #11 42.85 Adding debian:TeliaSonera_Root_CA_v1.pem #11 42.86 Adding debian:Certum_Trusted_Network_CA.pem #11 42.86 Adding debian:HiPKI_Root_CA_-_G1.pem #11 42.87 Adding debian:SZAFIR_ROOT_CA2.pem #11 42.87 Adding debian:USERTrust_RSA_Certification_Authority.pem #11 42.88 Adding debian:OISTE_WISeKey_Global_Root_GC_CA.pem #11 42.88 Adding debian:Buypass_Class_2_Root_CA.pem #11 42.89 Adding debian:AC_RAIZ_FNMT-RCM_SERVIDORES_SEGUROS.pem #11 42.89 Adding debian:e-Szigno_Root_CA_2017.pem #11 42.90 Adding debian:Microsoft_ECC_Root_Certificate_Authority_2017.pem #11 42.91 Adding debian:Security_Communication_Root_CA.pem #11 42.91 Adding debian:GlobalSign_Root_CA.pem #11 42.92 Adding debian:E-Tugra_Certification_Authority.pem #11 42.93 Adding debian:Trustwave_Global_Certification_Authority.pem #11 42.93 Adding debian:QuoVadis_Root_CA_2_G3.pem #11 42.93 Adding debian:SSL.com_EV_Root_Certification_Authority_ECC.pem #11 42.94 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068_2.pem #11 42.95 Adding debian:E-Tugra_Global_Root_CA_ECC_v3.pem #11 42.95 Adding debian:Entrust.net_Premium_2048_Secure_Server_CA.pem #11 42.96 Adding debian:AffirmTrust_Premium.pem #11 42.96 Adding debian:Amazon_Root_CA_3.pem #11 42.97 Adding debian:AffirmTrust_Networking.pem #11 42.98 Adding debian:IdenTrust_Public_Sector_Root_CA_1.pem #11 42.98 Adding debian:GlobalSign_ECC_Root_CA_-_R4.pem #11 42.99 Adding debian:QuoVadis_Root_CA_3_G3.pem #11 42.99 Adding debian:certSIGN_ROOT_CA.pem #11 43.00 Adding debian:IdenTrust_Commercial_Root_CA_1.pem #11 43.00 Adding debian:SecureTrust_CA.pem #11 43.01 Adding debian:DigiCert_Global_Root_G3.pem #11 43.01 Adding debian:GlobalSign_Root_E46.pem #11 43.02 Adding debian:SwissSign_Silver_CA_-_G2.pem #11 43.02 Adding debian:vTrus_ECC_Root_CA.pem #11 43.03 Adding debian:GlobalSign_ECC_Root_CA_-_R5.pem #11 43.04 Adding debian:OISTE_WISeKey_Global_Root_GB_CA.pem #11 43.04 Adding debian:DigiCert_Assured_ID_Root_G3.pem #11 43.05 Adding debian:emSign_Root_CA_-_G1.pem #11 43.05 Adding debian:DigiCert_TLS_RSA4096_Root_G5.pem #11 43.06 Adding debian:COMODO_Certification_Authority.pem #11 43.06 Adding debian:DigiCert_Global_Root_CA.pem #11 43.07 Adding debian:QuoVadis_Root_CA_2.pem #11 43.08 Adding debian:TWCA_Global_Root_CA.pem #11 43.09 Adding debian:ACCVRAIZ1.pem #11 43.09 Adding debian:GTS_Root_R2.pem #11 43.10 Adding debian:HARICA_TLS_RSA_Root_CA_2021.pem #11 43.10 Adding debian:Actalis_Authentication_Root_CA.pem #11 43.11 Adding debian:Certum_Trusted_Network_CA_2.pem #11 43.11 Adding debian:Atos_TrustedRoot_2011.pem #11 43.11 Adding debian:T-TeleSec_GlobalRoot_Class_3.pem #11 43.13 Adding debian:NetLock_Arany_=Class_Gold=_F\u0151tan\u00fas\u00edtv\u00e1ny.pem #11 43.13 Adding debian:Certigna.pem #11 43.14 Adding debian:Microsec_e-Szigno_Root_CA_2009.pem #11 43.14 Adding debian:UCA_Extended_Validation_Root.pem #11 43.14 Adding debian:DigiCert_Assured_ID_Root_G2.pem #11 43.15 Adding debian:QuoVadis_Root_CA_3.pem #11 43.15 Adding debian:NAVER_Global_Root_Certification_Authority.pem #11 43.16 Adding debian:GlobalSign_Root_CA_-_R6.pem #11 43.17 Adding debian:TUBITAK_Kamu_SM_SSL_Kok_Sertifikasi_-_Surum_1.pem #11 43.17 Adding debian:Trustwave_Global_ECC_P384_Certification_Authority.pem #11 43.18 Adding debian:Go_Daddy_Class_2_CA.pem #11 43.18 Adding debian:TWCA_Root_Certification_Authority.pem #11 43.19 Adding debian:certSIGN_Root_CA_G2.pem #11 43.19 Adding debian:COMODO_ECC_Certification_Authority.pem #11 43.20 Adding debian:ISRG_Root_X2.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_EV_2009.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_2009.pem #11 43.22 Adding debian:UCA_Global_G2_Root.pem #11 43.22 Adding debian:D-TRUST_BR_Root_CA_1_2020.pem #11 43.23 Adding debian:Secure_Global_CA.pem #11 43.24 Adding debian:Hongkong_Post_Root_CA_3.pem #11 43.24 Adding debian:Buypass_Class_3_Root_CA.pem #11 43.25 Adding debian:Entrust_Root_Certification_Authority_-_G2.pem #11 43.25 Adding debian:SSL.com_Root_Certification_Authority_ECC.pem #11 43.26 Adding debian:Hellenic_Academic_and_Research_Institutions_RootCA_2015.pem #11 43.27 Adding debian:E-Tugra_Global_Root_CA_RSA_v3.pem #11 43.27 Adding debian:USERTrust_ECC_Certification_Authority.pem #11 43.28 Adding debian:ePKI_Root_Certification_Authority.pem #11 43.28 Adding debian:ISRG_Root_X1.pem #11 43.29 Adding debian:HARICA_TLS_ECC_Root_CA_2021.pem #11 43.29 Adding debian:GTS_Root_R3.pem #11 43.30 Adding debian:Hellenic_Academic_and_Research_Institutions_ECC_RootCA_2015.pem #11 43.30 Adding debian:Security_Communication_RootCA2.pem #11 43.31 Adding debian:GlobalSign_Root_CA_-_R3.pem #11 43.35 done. #11 43.36 Processing triggers for mime-support (3.64ubuntu1) ... #11 43.40 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #11 43.45 Processing triggers for ca-certificates (20230311ubuntu0.20.04.1) ... #11 43.47 Updating certificates in /etc/ssl/certs... #11 44.82 0 added, 0 removed; done. #11 44.82 Running hooks in /etc/ca-certificates/update.d... #11 44.83 #11 45.16 done. #11 45.16 done. #11 DONE 45.9s #12 [ 8/17] WORKDIR /opt/mlflow #12 DONE 0.1s #13 [ 9/17] RUN pip install mlflow==2.12.2 #13 1.009 Collecting mlflow==2.12.2 #13 1.075 Downloading mlflow-2.12.2-py3-none-any.whl.metadata (29 kB) #13 1.207 Collecting Flask < 4 (from mlflow==2.12.2) #13 1.222 Downloading flask-3.0.3-py3-none-any.whl.metadata (3.2 kB) #13 1.309 Collecting alembic!=1.10.0, < 2 (from mlflow==2.12.2) #13 1.324 Downloading alembic-1.13.1-py3-none-any.whl.metadata (7.4 kB) #13 1.395 Collecting click < 9,>=7.0 (from mlflow==2.12.2) #13 1.409 Downloading click-8.1.7-py3-none-any.whl.metadata (3.0 kB) #13 1.467 Collecting cloudpickle < 4 (from mlflow==2.12.2) #13 1.483 Downloading cloudpickle-3.0.0-py3-none-any.whl.metadata (7.0 kB) #13 1.564 Collecting docker < 8,>=4.0.0 (from mlflow==2.12.2) #13 1.579 Downloading docker-7.0.0-py3-none-any.whl.metadata (3.5 kB) #13 1.635 Collecting entrypoints < 1 (from mlflow==2.12.2) #13 1.651 Downloading entrypoints-0.4-py3-none-any.whl.metadata (2.6 kB) #13 1.742 Collecting gitpython < 4,>=3.1.9 (from mlflow==2.12.2) #13 1.757 Downloading GitPython-3.1.43-py3-none-any.whl.metadata (13 kB) #13 1.822 Collecting graphene < 4 (from mlflow==2.12.2) #13 1.837 Downloading graphene-3.3-py2.py3-none-any.whl.metadata (7.7 kB) #13 1.946 Collecting importlib-metadata!=4.7.0, < 8,>=3.7.0 (from mlflow==2.12.2) #13 1.960 Downloading importlib_metadata-7.1.0-py3-none-any.whl.metadata (4.7 kB) #13 2.027 Collecting markdown < 4,>=3.3 (from mlflow==2.12.2) #13 2.079 Downloading Markdown-3.6-py3-none-any.whl.metadata (7.0 kB) #13 2.299 Collecting matplotlib < 4 (from mlflow==2.12.2) #13 2.314 Downloading matplotlib-3.7.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (5.7 kB) #13 2.593 Collecting numpy < 2 (from mlflow==2.12.2) #13 2.612 Downloading numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB) #13 2.685 Collecting packaging < 25 (from mlflow==2.12.2) #13 2.698 Downloading packaging-24.0-py3-none-any.whl.metadata (3.2 kB) #13 2.902 Collecting pandas < 3 (from mlflow==2.12.2) #13 2.916 Downloading pandas-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB) #13 3.172 Collecting protobuf < 5,>=3.12.0 (from mlflow==2.12.2) #13 3.187 Downloading protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes) #13 3.323 Collecting pyarrow < 16,>=4.0.0 (from mlflow==2.12.2) #13 3.338 Downloading pyarrow-15.0.2-cp38-cp38-manylinux_2_28_x86_64.whl.metadata (3.0 kB) #13 3.456 Collecting pytz < 2025 (from mlflow==2.12.2) #13 3.471 Downloading pytz-2024.1-py2.py3-none-any.whl.metadata (22 kB) #13 3.482 Requirement already satisfied: pyyaml < 7,>=5.1 in /usr/lib/python3/dist-packages (from mlflow==2.12.2) (5.3.1) #13 3.672 Collecting querystring-parser < 2 (from mlflow==2.12.2) #13 3.689 Downloading querystring_parser-1.2.4-py2.py3-none-any.whl.metadata (559 bytes) #13 3.780 Collecting requests < 3,>=2.17.3 (from mlflow==2.12.2) #13 3.794 Downloading requests-2.31.0-py3-none-any.whl.metadata (4.6 kB) #13 3.969 Collecting scikit-learn < 2 (from mlflow==2.12.2) #13 3.985 Downloading scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB) #13 4.159 Collecting scipy < 2 (from mlflow==2.12.2) #13 4.175 Downloading scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (58 kB) #13 4.199 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 58.9/58.9 kB 2.7 MB/s eta 0:00:00 #13 4.633 Collecting sqlalchemy < 3,>=1.4.0 (from mlflow==2.12.2) #13 4.648 Downloading SQLAlchemy-2.0.30-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.6 kB) #13 4.709 Collecting sqlparse < 1,>=0.4.0 (from mlflow==2.12.2) #13 4.724 Downloading sqlparse-0.5.0-py3-none-any.whl.metadata (3.9 kB) #13 4.795 Collecting Jinja2 < 4,>=2.11 (from mlflow==2.12.2) #13 4.809 Downloading jinja2-3.1.4-py3-none-any.whl.metadata (2.6 kB) #13 4.929 Collecting gunicorn < 23 (from mlflow==2.12.2) #13 4.944 Downloading gunicorn-22.0.0-py3-none-any.whl.metadata (4.4 kB) #13 5.043 Collecting Mako (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.058 Downloading Mako-1.3.5-py3-none-any.whl.metadata (2.9 kB) #13 5.122 Collecting typing-extensions>=4 (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.136 Downloading typing_extensions-4.11.0-py3-none-any.whl.metadata (3.0 kB) #13 5.225 Collecting importlib-resources (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.241 Downloading importlib_resources-6.4.0-py3-none-any.whl.metadata (3.9 kB) #13 5.348 Collecting urllib3>=1.26.0 (from docker < 8,>=4.0.0->mlflow==2.12.2) #13 5.362 Downloading urllib3-2.2.1-py3-none-any.whl.metadata (6.4 kB) #13 5.445 Collecting Werkzeug>=3.0.0 (from Flask < 4->mlflow==2.12.2) #13 5.460 Downloading werkzeug-3.0.3-py3-none-any.whl.metadata (3.7 kB) #13 5.519 Collecting itsdangerous>=2.1.2 (from Flask < 4->mlflow==2.12.2) #13 5.537 Downloading itsdangerous-2.2.0-py3-none-any.whl.metadata (1.9 kB) #13 5.606 Collecting blinker>=1.6.2 (from Flask < 4->mlflow==2.12.2) #13 5.623 Downloading blinker-1.8.2-py3-none-any.whl.metadata (1.6 kB) #13 5.716 Collecting gitdb < 5,>=4.0.1 (from gitpython < 4,>=3.1.9->mlflow==2.12.2) #13 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fonttools, entrypoints, cycler, cloudpickle, click, charset-normalizer, certifi, blinker, Werkzeug, sqlalchemy, scipy, requests, querystring-parser, python-dateutil, pyarrow, Mako, Jinja2, importlib-resources, importlib-metadata, gunicorn, graphql-relay, gitdb, contourpy, scikit-learn, pandas, matplotlib, markdown, graphene, gitpython, Flask, docker, alembic, mlflow #13 34.72 Successfully installed Flask-3.0.3 Jinja2-3.1.4 Mako-1.3.5 MarkupSafe-2.1.5 Werkzeug-3.0.3 alembic-1.13.1 aniso8601-9.0.1 blinker-1.8.2 certifi-2024.2.2 charset-normalizer-3.3.2 click-8.1.7 cloudpickle-3.0.0 contourpy-1.1.1 cycler-0.12.1 docker-7.0.0 entrypoints-0.4 fonttools-4.51.0 gitdb-4.0.11 gitpython-3.1.43 graphene-3.3 graphql-core-3.2.3 graphql-relay-3.2.0 greenlet-3.0.3 gunicorn-22.0.0 idna-3.7 importlib-metadata-7.1.0 importlib-resources-6.4.0 itsdangerous-2.2.0 joblib-1.4.2 kiwisolver-1.4.5 markdown-3.6 matplotlib-3.7.5 mlflow-2.12.2 numpy-1.24.4 packaging-24.0 pandas-2.0.3 pillow-10.3.0 protobuf-4.25.3 pyarrow-15.0.2 pyparsing-3.1.2 python-dateutil-2.9.0.post0 pytz-2024.1 querystring-parser-1.2.4 requests-2.31.0 scikit-learn-1.3.2 scipy-1.10.1 six-1.16.0 smmap-5.0.1 sqlalchemy-2.0.30 sqlparse-0.5.0 threadpoolctl-3.5.0 typing-extensions-4.11.0 tzdata-2024.1 urllib3-2.2.1 zipp-3.18.2 #13 34.72 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. 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12.86 [INFO] Finished at: 2024-05-16T20:25:32Z #14 12.86 [INFO] ------------------------------------------------------------------------ #14 DONE 13.0s #15 [11/17] RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.12.2:jar -DoutputDirectory=/opt/java/jars #15 1.696 [INFO] Scanning for projects... #15 1.897 [INFO] #15 1.897 [INFO] ------------------ < org.apache.maven:standalone-pom >------------------- #15 1.898 [INFO] Building Maven Stub Project (No POM) 1 #15 1.898 [INFO] --------------------------------[ pom ]--------------------------------- #15 1.905 [INFO] #15 1.905 [INFO] --- maven-dependency-plugin:2.8:copy (default-cli) @ standalone-pom --- #15 2.594 [INFO] Configured Artifact: org.mlflow:mlflow-scoring:2.12.2:jar #15 2.804 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/mlflow/mlflow-scoring/2.12.2/mlflow-scoring-2.12.2.jar #15 3.207 [INFO] Downloaded from central: 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maven-plugin-registry-2.2.1.jar to /opt/java/jars/maven-plugin-registry-2.2.1.jar #17 134.5 [INFO] Copying backport-util-concurrent-3.1.jar to /opt/java/jars/backport-util-concurrent-3.1.jar #17 134.5 [INFO] Copying wagon-http-shared-1.0-beta-6.jar to /opt/java/jars/wagon-http-shared-1.0-beta-6.jar #17 134.5 [INFO] Copying commons-httpclient-3.1.jar to /opt/java/jars/commons-httpclient-3.1.jar #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] BUILD SUCCESS #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] Total time: 02:12 min #17 134.5 [INFO] Finished at: 2024-05-16T20:27:50Z #17 134.5 [INFO] ------------------------------------------------------------------------ #17 DONE 134.8s #18 [14/17] COPY model_dir/success6g_model /opt/ml/model #18 DONE 0.1s #19 [15/17] RUN python -c \"from mlflow.models import container as C; C._install_pyfunc_deps('/opt/ml/model', install_mlflow=False, enable_mlserver=True, env_manager='virtualenv');\" #19 1.285 2024/05/16 20:27:52 INFO mlflow.models.container: creating and activating custom environment #19 1.289 2024/05/16 20:27:52 INFO mlflow.utils.virtualenv: Installing python 3.10.11 if it does not exist #19 1.673 Downloading Python-3.10.11.tar.xz... #19 1.673 -> https://www.python.org/ftp/python/3.10.11/Python-3.10.11.tar.xz #19 4.792 Installing Python-3.10.11... #19 108.0 Installed Python-3.10.11 to /root/.pyenv/versions/3.10.11 #19 108.4 2024/05/16 20:29:39 INFO mlflow.utils.virtualenv: Creating a new environment in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b with /root/.pyenv/versions/3.10.11/bin/python #19 109.3 created virtual environment CPython3.10.11.final.0-64 in 719ms #19 109.3 creator CPython3Posix(dest=/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b, clear=False, no_vcs_ignore=False, global=False) #19 109.3 seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/.local/share/virtualenv) #19 109.3 added seed packages: pip==24.0, setuptools==69.5.1, wheel==0.43.0 #19 109.3 activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator #19 109.4 2024/05/16 20:29:40 INFO mlflow.utils.virtualenv: Installing dependencies #19 168.4 WARNING: There was an error checking the latest version of pip. #19 169.3 Collecting mlserver!=1.3.1, < 1.4.0,>=1.2.0 #19 169.4 Downloading mlserver-1.3.5-py3-none-any.whl (113 kB) #19 169.4 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 113.2/113.2 kB 3.4 MB/s eta 0:00:00 #19 169.6 Collecting mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0 #19 169.6 Downloading mlserver_mlflow-1.3.5-py3-none-any.whl (10 kB) #19 169.7 Requirement already satisfied: click in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) (8.1.3) #19 169.8 Collecting fastapi!=0.89.0, < =0.89.1,>=0.88.0 (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.8 Downloading fastapi-0.89.1-py3-none-any.whl (55 kB) #19 169.8 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 55.8/55.8 kB 7.4 MB/s eta 0:00:00 #19 169.9 Collecting python-dotenv (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.9 Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB) #19 171.0 Collecting grpcio (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.0 Downloading grpcio-1.63.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB) #19 171.3 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1.4.0,>=1.2.0) (4.25.3) #19 171.5 Collecting uvicorn (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.5 Downloading uvicorn-0.29.0-py3-none-any.whl (60 kB) #19 171.6 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 60.8/60.8 kB 8.7 MB/s eta 0:00:00 #19 171.7 Collecting starlette-exporter (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.7 Downloading starlette_exporter-0.21.0-py3-none-any.whl (14 kB) #19 171.9 Collecting py-grpc-prometheus (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.9 Downloading py_grpc_prometheus-0.8.0-py3-none-any.whl (12 kB) #19 172.1 Collecting aiokafka (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 172.1 Downloading aiokafka-0.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB) #19 172.2 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orjson-3.10.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (142 kB) #19 173.6 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 142.5/142.5 kB 19.4 MB/s eta 0:00:00 #19 173.7 Collecting uvloop (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 173.7 Downloading uvloop-0.19.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.4 MB) #19 173.8 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.4/3.4 MB 19.8 MB/s eta 0:00:00 #19 173.9 Requirement already satisfied: mlflow in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (2.12.2) #19 174.2 Collecting pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1, < 2.0.0,>=1.6.2 (from fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 174.2 Downloading pydantic-1.10.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB) #19 174.4 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.1/3.1 MB 19.8 MB/s eta 0:00:00 #19 174.5 Collecting starlette==0.22.0 (from fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 174.5 Downloading starlette-0.22.0-py3-none-any.whl (64 kB) #19 174.5 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0:00:00 #19 176.1 Collecting geventhttpclient < =2.0.2,>=1.4.4 (from tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.2 Downloading geventhttpclient-2.0.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (100 kB) #19 176.2 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.3/100.3 kB 10.5 MB/s eta 0:00:00 #19 176.3 Requirement already satisfied: async-timeout in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from aiokafka->mlserver!=1.3.1, < 1.4.0,>=1.2.0) (4.0.3) #19 176.3 Requirement already satisfied: packaging in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from aiokafka->mlserver!=1.3.1, < 1.4.0,>=1.2.0) (23.1) #19 176.4 Requirement 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mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (2.0.30) #19 176.4 Requirement already satisfied: sqlparse < 1,>=0.4.0 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (0.5.0) #19 176.4 Requirement already satisfied: Jinja2 < 4,>=2.11 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.1.2) #19 176.4 Requirement already satisfied: gunicorn < 23 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (22.0.0) #19 176.5 Requirement already satisfied: python-dateutil>=2.8.2 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from pandas->mlserver!=1.3.1, < 1.4.0,>=1.2.0) (2.9.0.post0) #19 176.5 Requirement already satisfied: 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requirements. This could take a while. #19 176.6 Collecting starlette-exporter (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.7 Downloading starlette_exporter-0.20.0-py3-none-any.whl (14 kB) #19 176.7 Downloading starlette_exporter-0.19.0-py3-none-any.whl (14 kB) #19 176.7 Downloading starlette_exporter-0.18.0-py3-none-any.whl (14 kB) #19 176.8 Downloading starlette_exporter-0.17.1-py3-none-any.whl (14 kB) #19 176.8 Collecting h11>=0.8 (from uvicorn->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.8 Downloading h11-0.14.0-py3-none-any.whl (58 kB) #19 176.9 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 58.3/58.3 kB 8.0 MB/s eta 0:00:00 #19 176.9 Requirement already satisfied: typing-extensions>=4.0 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from uvicorn->mlserver!=1.3.1, < 1.4.0,>=1.2.0) (4.11.0) #19 176.9 Collecting aiosignal>=1.1.2 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.9 Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB) #19 177.0 Collecting attrs>=17.3.0 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 177.0 Downloading attrs-23.2.0-py3-none-any.whl (60 kB) #19 177.0 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 60.8/60.8 kB 8.7 MB/s eta 0:00:00 #19 177.2 Collecting frozenlist>=1.1.1 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 177.2 Downloading frozenlist-1.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (239 kB) #19 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yarl-1.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (301 kB) #19 178.0 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 301.6/301.6 kB 16.3 MB/s eta 0:00:00 #19 178.0 Requirement already satisfied: Mako in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from alembic!=1.10.0, < 2->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (1.3.5) #19 178.1 Requirement already satisfied: Werkzeug>=3.0.0 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from Flask < 4->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.0.3) #19 178.1 Requirement already satisfied: itsdangerous>=2.1.2 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from Flask < 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/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from importlib-metadata!=4.7.0, < 8,>=3.7.0->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.18.2) #19 179.2 Requirement already satisfied: MarkupSafe>=2.0 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from Jinja2 < 4,>=2.11->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (2.1.5) #19 179.2 Requirement already satisfied: contourpy>=1.0.1 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from matplotlib < 4->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (1.2.1) #19 179.2 Requirement already satisfied: cycler>=0.10 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from matplotlib < 4->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (0.12.1) #19 179.2 Requirement already satisfied: fonttools>=4.22.0 in 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/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from sqlalchemy < 3,>=1.4.0->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.0.3) #19 179.6 Collecting sniffio>=1.1 (from anyio < 5,>=3.4.0->starlette==0.22.0->fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 179.6 Downloading sniffio-1.3.1-py3-none-any.whl (10 kB) #19 179.7 Collecting exceptiongroup>=1.0.2 (from anyio < 5,>=3.4.0->starlette==0.22.0->fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 179.7 Downloading exceptiongroup-1.2.1-py3-none-any.whl (16 kB) #19 179.9 Collecting zope.event (from gevent>=0.13->geventhttpclient < =2.0.2,>=1.4.4->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 180.0 Downloading zope.event-5.0-py3-none-any.whl (6.8 kB) #19 180.2 Collecting zope.interface (from gevent>=0.13->geventhttpclient < =2.0.2,>=1.4.4->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 180.2 Downloading zope.interface-6.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (247 kB) #19 180.2 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 247.5/247.5 kB 31.3 MB/s eta 0:00:00 #19 180.2 Requirement already satisfied: smmap < 6,>=3.0.1 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from gitdb < 5,>=4.0.1->gitpython < 4,>=3.1.9->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (5.0.1) #19 181.2 Installing collected packages: brotli, zope.interface, zope.event, uvloop, sniffio, python-rapidjson, python-dotenv, pydantic, prometheus-client, orjson, multidict, importlib-resources, h11, grpcio, frozenlist, exceptiongroup, attrs, aiokafka, aiofiles, yarl, uvicorn, tritonclient, py-grpc-prometheus, gevent, anyio, aiosignal, starlette, geventhttpclient, aiohttp, starlette-exporter, fastapi, mlserver, mlserver-mlflow #19 184.4 Successfully installed aiofiles-23.2.1 aiohttp-3.9.5 aiokafka-0.10.0 aiosignal-1.3.1 anyio-4.3.0 attrs-23.2.0 brotli-1.1.0 exceptiongroup-1.2.1 fastapi-0.89.1 frozenlist-1.4.1 gevent-24.2.1 geventhttpclient-2.0.2 grpcio-1.63.0 h11-0.14.0 importlib-resources-6.4.0 mlserver-1.3.5 mlserver-mlflow-1.3.5 multidict-6.0.5 orjson-3.10.3 prometheus-client-0.20.0 py-grpc-prometheus-0.8.0 pydantic-1.10.15 python-dotenv-1.0.1 python-rapidjson-1.16 sniffio-1.3.1 starlette-0.22.0 starlette-exporter-0.17.1 tritonclient-2.45.0 uvicorn-0.29.0 uvloop-0.19.0 yarl-1.9.4 zope.event-5.0 zope.interface-6.4 #19 184.4 WARNING: There was an error checking the latest version of pip. #19 DONE 186.7s #20 [16/17] RUN chmod o+rwX /opt/mlflow/ #20 DONE 0.4s #21 [17/17] RUN rm -rf /var/lib/apt/lists/* #21 DONE 0.6s #22 exporting to image #22 exporting layers #22 exporting layers 12.0s done #22 writing image sha256:29875270957bd037ee2b833cf9201a001d353e4fef34b1d8c4923b278ef8ac02 done #22 naming to docker.io/5uperpalo/mlflow-success6g done #22 DONE 12.1s ```","title":"04_model_training_pipeline"},{"location":"notebooks/04_model_training_pipeline.html#example-usage-notebook","text":"Objects and functions in this notebook are listed with all paramaters to ilustrate their capabillities. Most of the paramaters have default values in the implementation","title":"Example usage notebook"},{"location":"notebooks/04_model_training_pipeline.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd import mlflow pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) os . environ [ \"AWS_ACCESS_KEY_ID\" ] = \"minioadmin\" os . environ [ \"AWS_SECRET_ACCESS_KEY\" ] = \"minioadmin\" os . environ [ \"MLFLOW_S3_ENDPOINT_URL\" ] = f \"http://10.152.183.156:9000\" import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd import mlflow pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\" In [2]: Copied! import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump , dill_load from inference_model.training.utils import flatten_dict , get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump, dill_load from inference_model.training.utils import flatten_dict, get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast","title":"Imports"},{"location":"notebooks/04_model_training_pipeline.html#train-a-testing-model","text":"In [3]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [4]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) In [6]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) In [7]: Copied! prepare_data = PreprocessData ( id_cols = id_cols , target_col = target_col , cat_cols = cat_cols , cont_cols = cont_cols , ) # this should be fitted only on training data _ = prepare_data . fit ( df = df_pd ) prepare_data = PreprocessData( id_cols=id_cols, target_col=target_col, cat_cols=cat_cols, cont_cols=cont_cols, ) # this should be fitted only on training data _ = prepare_data.fit(df=df_pd) In [8]: Copied! optimizer = LGBOptunaOptimizer ( objective = \"binary\" , n_class = 2 , ) trainer = Trainer ( cat_cols = prepare_data . cat_cols , target_col = prepare_data . target_col , id_cols = id_cols , objective = \"binary\" , n_class = 2 , optimizer = optimizer , preprocessors = [ prepare_data ], ) metrics_dict = trainer . fit ( df_train = df_train , df_valid = df_valid , df_test = df_test , ) optimizer = LGBOptunaOptimizer( objective=\"binary\", n_class=2, ) trainer = Trainer( cat_cols=prepare_data.cat_cols, target_col=prepare_data.target_col, id_cols=id_cols, objective=\"binary\", n_class=2, optimizer=optimizer, preprocessors=[prepare_data], ) metrics_dict = trainer.fit( df_train=df_train, df_valid=df_valid, df_test=df_test, ) [I 2024-05-22 13:01:18,589] A new study created in memory with name: LightGBMTuner feature_fraction, val_score: inf: 0%| | 0/7 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000002: 14%|#4 | 1/7 [00:03<00:21, 3.62s/it][I 2024-05-22 13:01:22,217] Trial 0 finished with value: 1.6789085046471396e-06 and parameters: {'feature_fraction': 0.4}. Best is trial 0 with value: 1.6789085046471396e-06. feature_fraction, val_score: 0.000002: 14%|#4 | 1/7 [00:03<00:21, 3.62s/it] Early stopping, best iteration is: [588] valid_0's binary_logloss: 1.67891e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000002: 29%|##8 | 2/7 [00:05<00:13, 2.72s/it][I 2024-05-22 13:01:24,308] Trial 1 finished with value: 1.9702431647601935e-06 and parameters: {'feature_fraction': 0.8999999999999999}. Best is trial 0 with value: 1.6789085046471396e-06. feature_fraction, val_score: 0.000002: 29%|##8 | 2/7 [00:05<00:13, 2.72s/it] Early stopping, best iteration is: [380] valid_0's binary_logloss: 1.97024e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 43%|####2 | 3/7 [00:06<00:06, 1.71s/it][I 2024-05-22 13:01:24,812] Trial 2 finished with value: 1.3616097829410516e-06 and parameters: {'feature_fraction': 1.0}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 43%|####2 | 3/7 [00:06<00:06, 1.71s/it] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 57%|#####7 | 4/7 [00:07<00:04, 1.40s/it][I 2024-05-22 13:01:25,731] Trial 3 finished with value: 2.2594885710041037e-06 and parameters: {'feature_fraction': 0.6}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 57%|#####7 | 4/7 [00:07<00:04, 1.40s/it] Early stopping, best iteration is: [794] valid_0's binary_logloss: 2.25949e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 71%|#######1 | 5/7 [00:08<00:02, 1.21s/it][I 2024-05-22 13:01:26,610] Trial 4 finished with value: 2.5490428264265304e-06 and parameters: {'feature_fraction': 0.8}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 71%|#######1 | 5/7 [00:08<00:02, 1.21s/it] Early stopping, best iteration is: [485] valid_0's binary_logloss: 2.54904e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 86%|########5 | 6/7 [00:09<00:01, 1.14s/it][I 2024-05-22 13:01:27,606] Trial 5 finished with value: 2.2594885710041037e-06 and parameters: {'feature_fraction': 0.5}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 86%|########5 | 6/7 [00:09<00:01, 1.14s/it] Early stopping, best iteration is: [794] valid_0's binary_logloss: 2.25949e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 100%|##########| 7/7 [00:10<00:00, 1.10s/it][I 2024-05-22 13:01:28,615] Trial 6 finished with value: 2.5249718256638313e-06 and parameters: {'feature_fraction': 0.7}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 100%|##########| 7/7 [00:10<00:00, 1.43s/it] Early stopping, best iteration is: [715] valid_0's binary_logloss: 2.52497e-06 num_leaves, val_score: 0.000001: 0%| | 0/20 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 5%|5 | 1/20 [00:00<00:09, 1.99it/s][I 2024-05-22 13:01:29,127] Trial 7 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 227}. Best is trial 7 with value: 1.3616097829410516e-06. num_leaves, val_score: 0.000001: 5%|5 | 1/20 [00:00<00:09, 1.99it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 10%|# | 2/20 [00:00<00:08, 2.01it/s][I 2024-05-22 13:01:29,623] Trial 8 finished with value: 1.361609811862523e-06 and parameters: {'num_leaves': 14}. Best is trial 7 with value: 1.3616097829410516e-06. num_leaves, val_score: 0.000001: 10%|# | 2/20 [00:01<00:08, 2.01it/s] Early stopping, best iteration is: [390] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 15%|#5 | 3/20 [00:01<00:08, 2.00it/s][I 2024-05-22 13:01:30,125] Trial 9 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 90}. Best is trial 7 with value: 1.3616097829410516e-06. num_leaves, val_score: 0.000001: 15%|#5 | 3/20 [00:01<00:08, 2.00it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 20%|## | 4/20 [00:02<00:08, 1.88it/s][I 2024-05-22 13:01:30,707] Trial 10 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 255}. Best is trial 10 with value: 1.3616097829410512e-06. num_leaves, val_score: 0.000001: 20%|## | 4/20 [00:02<00:08, 1.88it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 25%|##5 | 5/20 [00:02<00:07, 1.89it/s][I 2024-05-22 13:01:31,227] Trial 11 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 252}. Best is trial 10 with value: 1.3616097829410512e-06. num_leaves, val_score: 0.000001: 25%|##5 | 5/20 [00:02<00:07, 1.89it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 30%|### | 6/20 [00:03<00:08, 1.70it/s][I 2024-05-22 13:01:31,929] Trial 12 finished with value: 1.361609782941051e-06 and parameters: {'num_leaves': 161}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 30%|### | 6/20 [00:03<00:08, 1.70it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 35%|###5 | 7/20 [00:03<00:07, 1.70it/s][I 2024-05-22 13:01:32,519] Trial 13 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 169}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 35%|###5 | 7/20 [00:03<00:07, 1.70it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 40%|#### | 8/20 [00:04<00:06, 1.77it/s][I 2024-05-22 13:01:33,097] Trial 14 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 156}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 40%|#### | 8/20 [00:04<00:06, 1.77it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 45%|####5 | 9/20 [00:05<00:06, 1.67it/s][I 2024-05-22 13:01:33,710] Trial 15 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 196}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 45%|####5 | 9/20 [00:05<00:06, 1.67it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 50%|##### | 10/20 [00:05<00:05, 1.75it/s][I 2024-05-22 13:01:34,220] Trial 16 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 111}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 50%|##### | 10/20 [00:05<00:05, 1.75it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 55%|#####5 | 11/20 [00:06<00:04, 1.82it/s][I 2024-05-22 13:01:34,717] Trial 17 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 57}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 55%|#####5 | 11/20 [00:06<00:04, 1.82it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 60%|###### | 12/20 [00:06<00:04, 1.87it/s][I 2024-05-22 13:01:35,221] Trial 18 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 212}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 60%|###### | 12/20 [00:06<00:04, 1.87it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 65%|######5 | 13/20 [00:07<00:03, 1.90it/s][I 2024-05-22 13:01:35,730] Trial 19 finished with value: 1.3616097829410518e-06 and parameters: {'num_leaves': 255}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 65%|######5 | 13/20 [00:07<00:03, 1.90it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 70%|####### | 14/20 [00:07<00:03, 1.85it/s][I 2024-05-22 13:01:36,303] Trial 20 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 150}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 70%|####### | 14/20 [00:07<00:03, 1.85it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 75%|#######5 | 15/20 [00:08<00:02, 1.86it/s][I 2024-05-22 13:01:36,827] Trial 21 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 186}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 75%|#######5 | 15/20 [00:08<00:02, 1.86it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 80%|######## | 16/20 [00:08<00:02, 1.82it/s][I 2024-05-22 13:01:37,407] Trial 22 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 171}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 80%|######## | 16/20 [00:08<00:02, 1.82it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 85%|########5 | 17/20 [00:09<00:01, 1.86it/s][I 2024-05-22 13:01:37,920] Trial 23 finished with value: 1.3616097829410518e-06 and parameters: {'num_leaves': 127}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 85%|########5 | 17/20 [00:09<00:01, 1.86it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 90%|######### | 18/20 [00:09<00:01, 1.88it/s][I 2024-05-22 13:01:38,433] Trial 24 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 220}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 90%|######### | 18/20 [00:09<00:01, 1.88it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 95%|#########5| 19/20 [00:10<00:00, 1.83it/s][I 2024-05-22 13:01:39,016] Trial 25 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 85}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 95%|#########5| 19/20 [00:10<00:00, 1.83it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 100%|##########| 20/20 [00:10<00:00, 1.77it/s][I 2024-05-22 13:01:39,624] Trial 26 finished with value: 1.361609782941051e-06 and parameters: {'num_leaves': 140}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 100%|##########| 20/20 [00:11<00:00, 1.82it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 bagging, val_score: 0.000001: 0%| | 0/10 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 10%|# | 1/10 [00:00<00:05, 1.65it/s][I 2024-05-22 13:01:40,309] Trial 27 finished with value: 3.449205755838957e-07 and parameters: {'bagging_fraction': 0.4418842108373299, 'bagging_freq': 5}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 10%|# | 1/10 [00:00<00:05, 1.65it/s] Early stopping, best iteration is: [266] valid_0's binary_logloss: 3.44921e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 20%|## | 2/10 [00:00<00:03, 2.63it/s][I 2024-05-22 13:01:40,531] Trial 28 finished with value: 0.003198735879283877 and parameters: {'bagging_fraction': 0.40010886623598485, 'bagging_freq': 5}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 20%|## | 2/10 [00:00<00:03, 2.63it/s] Early stopping, best iteration is: [49] valid_0's binary_logloss: 0.00319874 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 30%|### | 3/10 [00:01<00:03, 2.29it/s][I 2024-05-22 13:01:41,034] Trial 29 finished with value: 2.3253951344156617e-06 and parameters: {'bagging_fraction': 0.5615890028222774, 'bagging_freq': 3}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 30%|### | 3/10 [00:01<00:03, 2.29it/s] Early stopping, best iteration is: [216] valid_0's binary_logloss: 2.3254e-06 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 40%|#### | 4/10 [00:02<00:03, 1.89it/s][I 2024-05-22 13:01:41,710] Trial 30 finished with value: 1.6847441854237672e-06 and parameters: {'bagging_fraction': 0.9729155031966343, 'bagging_freq': 7}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 40%|#### | 4/10 [00:02<00:03, 1.89it/s] Early stopping, best iteration is: [253] valid_0's binary_logloss: 1.68474e-06 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 50%|##### | 5/10 [00:02<00:02, 1.90it/s][I 2024-05-22 13:01:42,229] Trial 31 finished with value: 2.3371325286333586e-06 and parameters: {'bagging_fraction': 0.7517048260537027, 'bagging_freq': 1}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 50%|##### | 5/10 [00:02<00:02, 1.90it/s] Early stopping, best iteration is: [197] valid_0's binary_logloss: 2.33713e-06 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 60%|###### | 6/10 [00:02<00:01, 2.23it/s][I 2024-05-22 13:01:42,525] Trial 32 finished with value: 0.0031987358794118973 and parameters: {'bagging_fraction': 0.40032139731038635, 'bagging_freq': 5}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 60%|###### | 6/10 [00:02<00:01, 2.23it/s] Early stopping, best iteration is: [49] valid_0's binary_logloss: 0.00319874 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 70%|####### | 7/10 [00:03<00:01, 2.17it/s][I 2024-05-22 13:01:43,013] Trial 33 finished with value: 2.9743999611270926e-07 and parameters: {'bagging_fraction': 0.6425832769598463, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 70%|####### | 7/10 [00:03<00:01, 2.17it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.9744e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 80%|######## | 8/10 [00:03<00:00, 2.24it/s][I 2024-05-22 13:01:43,427] Trial 34 finished with value: 3.040896374710074e-07 and parameters: {'bagging_fraction': 0.6280540860504722, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 80%|######## | 8/10 [00:03<00:00, 2.24it/s] Early stopping, best iteration is: [208] valid_0's binary_logloss: 3.0409e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 90%|######### | 9/10 [00:04<00:00, 2.19it/s][I 2024-05-22 13:01:43,909] Trial 35 finished with value: 7.219543528343957e-07 and parameters: {'bagging_fraction': 0.6210765540576059, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 90%|######### | 9/10 [00:04<00:00, 2.19it/s] Early stopping, best iteration is: [187] valid_0's binary_logloss: 7.21954e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 100%|##########| 10/10 [00:04<00:00, 2.25it/s][I 2024-05-22 13:01:44,327] Trial 36 finished with value: 3.404675474216927e-07 and parameters: {'bagging_fraction': 0.6658803881381716, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 100%|##########| 10/10 [00:04<00:00, 2.16it/s] Early stopping, best iteration is: [251] valid_0's binary_logloss: 3.40468e-07 feature_fraction_stage2, val_score: 0.000000: 0%| | 0/3 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.000000: 33%|###3 | 1/3 [00:00<00:00, 2.51it/s][I 2024-05-22 13:01:44,809] Trial 37 finished with value: 2.9743999611270936e-07 and parameters: {'feature_fraction': 0.9520000000000001}. Best is trial 37 with value: 2.9743999611270936e-07. feature_fraction_stage2, val_score: 0.000000: 33%|###3 | 1/3 [00:00<00:00, 2.51it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.9744e-07 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.000000: 67%|######6 | 2/3 [00:00<00:00, 2.80it/s][I 2024-05-22 13:01:45,199] Trial 38 finished with value: 2.9743999611270936e-07 and parameters: {'feature_fraction': 0.9840000000000001}. Best is trial 37 with value: 2.9743999611270936e-07. feature_fraction_stage2, val_score: 0.000000: 67%|######6 | 2/3 [00:00<00:00, 2.80it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.9744e-07 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.000000: 100%|##########| 3/3 [00:01<00:00, 2.20it/s][I 2024-05-22 13:01:45,710] Trial 39 finished with value: 4.498413698090123e-07 and parameters: {'feature_fraction': 0.92}. Best is trial 37 with value: 2.9743999611270936e-07. feature_fraction_stage2, val_score: 0.000000: 100%|##########| 3/3 [00:01<00:00, 2.29it/s] Early stopping, best iteration is: [256] valid_0's binary_logloss: 4.49841e-07 regularization_factors, val_score: 0.000000: 0%| | 0/20 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds Early stopping, best iteration is: [227] valid_0's binary_logloss: 0.00690416 regularization_factors, val_score: 0.000000: 5%|5 | 1/20 [00:00<00:05, 3.40it/s][I 2024-05-22 13:01:46,021] Trial 40 finished with value: 0.006904162868543424 and parameters: {'lambda_l1': 2.2800500671305803, 'lambda_l2': 0.0011641838798314252}. Best is trial 40 with value: 0.006904162868543424. regularization_factors, val_score: 0.000000: 5%|5 | 1/20 [00:00<00:05, 3.40it/s] Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 10%|# | 2/20 [00:00<00:06, 2.87it/s][I 2024-05-22 13:01:46,408] Trial 41 finished with value: 2.974565285658327e-07 and parameters: {'lambda_l1': 3.2596973482462435e-08, 'lambda_l2': 1.061451695187828e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 10%|# | 2/20 [00:00<00:06, 2.87it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97457e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 15%|#5 | 3/20 [00:01<00:06, 2.68it/s][I 2024-05-22 13:01:46,811] Trial 42 finished with value: 2.974705202215368e-07 and parameters: {'lambda_l1': 2.241952369984708e-08, 'lambda_l2': 1.8880584030540923e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 15%|#5 | 3/20 [00:01<00:06, 2.68it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97471e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 20%|## | 4/20 [00:01<00:05, 2.83it/s][I 2024-05-22 13:01:47,131] Trial 43 finished with value: 2.9748702210105415e-07 and parameters: {'lambda_l1': 1.0199507520333984e-08, 'lambda_l2': 2.130651303908614e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 20%|## | 4/20 [00:01<00:05, 2.83it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97487e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 25%|##5 | 5/20 [00:01<00:05, 2.72it/s][I 2024-05-22 13:01:47,526] Trial 44 finished with value: 2.974846523730242e-07 and parameters: {'lambda_l1': 1.6736475077289633e-08, 'lambda_l2': 1.0112045153335179e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 25%|##5 | 5/20 [00:01<00:05, 2.72it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97485e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 30%|### | 6/20 [00:02<00:04, 2.89it/s][I 2024-05-22 13:01:47,829] Trial 45 finished with value: 3.176935636511711e-07 and parameters: {'lambda_l1': 5.882861681810232e-06, 'lambda_l2': 1.1233467846223274e-06}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 30%|### | 6/20 [00:02<00:04, 2.89it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 3.17694e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 35%|###5 | 7/20 [00:03<00:08, 1.61it/s][I 2024-05-22 13:01:49,021] Trial 46 finished with value: 0.00013806811413863104 and parameters: {'lambda_l1': 1.0772166664037463e-05, 'lambda_l2': 3.5907966551315282}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 35%|###5 | 7/20 [00:03<00:08, 1.61it/s] Did not meet early stopping. Best iteration is: [1000] valid_0's binary_logloss: 0.000138068 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 40%|#### | 8/20 [00:03<00:06, 1.91it/s][I 2024-05-22 13:01:49,331] Trial 47 finished with value: 3.3174632310732203e-07 and parameters: {'lambda_l1': 9.228838671620809e-07, 'lambda_l2': 1.2357091396245195e-05}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 40%|#### | 8/20 [00:03<00:06, 1.91it/s] Early stopping, best iteration is: [209] valid_0's binary_logloss: 3.31746e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 45%|####5 | 9/20 [00:03<00:05, 2.10it/s][I 2024-05-22 13:01:49,706] Trial 48 finished with value: 2.8970612681555003e-06 and parameters: {'lambda_l1': 0.004116041688790151, 'lambda_l2': 4.91159030541583e-07}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 45%|####5 | 9/20 [00:03<00:05, 2.10it/s] Early stopping, best iteration is: [206] valid_0's binary_logloss: 2.89706e-06 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 50%|##### | 10/20 [00:04<00:04, 2.33it/s][I 2024-05-22 13:01:50,028] Trial 49 finished with value: 5.975862396554039e-07 and parameters: {'lambda_l1': 2.4738617566052645e-07, 'lambda_l2': 0.003148588239925189}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 50%|##### | 10/20 [00:04<00:04, 2.33it/s] Early stopping, best iteration is: [283] valid_0's binary_logloss: 5.97586e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 55%|#####5 | 11/20 [00:04<00:03, 2.41it/s][I 2024-05-22 13:01:50,411] Trial 50 finished with value: 5.528528493352603e-07 and parameters: {'lambda_l1': 0.0005147056784853866, 'lambda_l2': 1.3373340341538605e-05}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 55%|#####5 | 11/20 [00:04<00:03, 2.41it/s] Early stopping, best iteration is: [204] valid_0's binary_logloss: 5.52853e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 60%|###### | 12/20 [00:05<00:03, 2.45it/s][I 2024-05-22 13:01:50,806] Trial 51 finished with value: 2.97483914403635e-07 and parameters: {'lambda_l1': 1.1161313264875577e-08, 'lambda_l2': 1.3457934495742431e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 60%|###### | 12/20 [00:05<00:03, 2.45it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97484e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 65%|######5 | 13/20 [00:05<00:02, 2.63it/s][I 2024-05-22 13:01:51,122] Trial 52 finished with value: 2.974834213929394e-07 and parameters: {'lambda_l1': 1.1851264993602946e-08, 'lambda_l2': 1.1579633227443254e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 65%|######5 | 13/20 [00:05<00:02, 2.63it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97483e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 70%|####### | 14/20 [00:05<00:02, 2.59it/s][I 2024-05-22 13:01:51,521] Trial 53 finished with value: 3.0224491028537356e-07 and parameters: {'lambda_l1': 1.8444434562217248e-07, 'lambda_l2': 1.904342905861566e-07}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 70%|####### | 14/20 [00:05<00:02, 2.59it/s] Early stopping, best iteration is: [204] valid_0's binary_logloss: 3.02245e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 75%|#######5 | 15/20 [00:06<00:02, 2.38it/s][I 2024-05-22 13:01:52,020] Trial 54 finished with value: 2.97111604544352e-07 and parameters: {'lambda_l1': 1.8697547616086913e-07, 'lambda_l2': 1.4195276461779107e-07}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 75%|#######5 | 15/20 [00:06<00:02, 2.38it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97112e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 80%|######## | 16/20 [00:06<00:01, 2.61it/s][I 2024-05-22 13:01:52,319] Trial 55 finished with value: 2.7975386625160922e-05 and parameters: {'lambda_l1': 9.1209069884061e-06, 'lambda_l2': 2.832296835692325e-07}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 80%|######## | 16/20 [00:06<00:01, 2.61it/s] Early stopping, best iteration is: [150] valid_0's binary_logloss: 2.79754e-05 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 85%|########5 | 17/20 [00:06<00:01, 2.77it/s][I 2024-05-22 13:01:52,623] Trial 56 finished with value: 3.012783257895089e-07 and parameters: {'lambda_l1': 5.249197497865728e-07, 'lambda_l2': 8.723247474648092e-06}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 85%|########5 | 17/20 [00:06<00:01, 2.77it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 3.01278e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 90%|######### | 18/20 [00:07<00:00, 2.31it/s][I 2024-05-22 13:01:53,223] Trial 57 finished with value: 6.216923691382048e-06 and parameters: {'lambda_l1': 1.2654511369988902e-07, 'lambda_l2': 0.026856741790607278}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 90%|######### | 18/20 [00:07<00:00, 2.31it/s] Early stopping, best iteration is: [494] valid_0's binary_logloss: 6.21692e-06 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 95%|#########5| 19/20 [00:07<00:00, 2.57it/s][I 2024-05-22 13:01:53,511] Trial 58 finished with value: 4.6829312861054814e-05 and parameters: {'lambda_l1': 0.02854037816163236, 'lambda_l2': 1.2681271167280724e-07}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 95%|#########5| 19/20 [00:07<00:00, 2.57it/s] Early stopping, best iteration is: [182] valid_0's binary_logloss: 4.68293e-05 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 100%|##########| 20/20 [00:08<00:00, 2.74it/s][I 2024-05-22 13:01:53,821] Trial 59 finished with value: 2.468284408525897e-07 and parameters: {'lambda_l1': 0.0001184573048303984, 'lambda_l2': 4.182514336496212e-06}. Best is trial 59 with value: 2.468284408525897e-07. regularization_factors, val_score: 0.000000: 100%|##########| 20/20 [00:08<00:00, 2.47it/s] Early stopping, best iteration is: [240] valid_0's binary_logloss: 2.46828e-07 min_child_samples, val_score: 0.000000: 0%| | 0/5 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 20%|## | 1/5 [00:00<00:01, 3.44it/s][I 2024-05-22 13:01:54,119] Trial 60 finished with value: 5.156782324432179e-06 and parameters: {'min_child_samples': 10}. Best is trial 60 with value: 5.156782324432179e-06. min_child_samples, val_score: 0.000000: 20%|## | 1/5 [00:00<00:01, 3.44it/s] Early stopping, best iteration is: [199] valid_0's binary_logloss: 5.15678e-06 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 40%|#### | 2/5 [00:00<00:00, 3.32it/s][I 2024-05-22 13:01:54,427] Trial 61 finished with value: 3.282327081150373e-07 and parameters: {'min_child_samples': 100}. Best is trial 61 with value: 3.282327081150373e-07. min_child_samples, val_score: 0.000000: 40%|#### | 2/5 [00:00<00:00, 3.32it/s] Early stopping, best iteration is: [269] valid_0's binary_logloss: 3.28233e-07 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 60%|###### | 3/5 [00:00<00:00, 3.41it/s][I 2024-05-22 13:01:54,712] Trial 62 finished with value: 3.6291058037098226e-06 and parameters: {'min_child_samples': 5}. Best is trial 61 with value: 3.282327081150373e-07. min_child_samples, val_score: 0.000000: 60%|###### | 3/5 [00:00<00:00, 3.41it/s] Early stopping, best iteration is: [176] valid_0's binary_logloss: 3.62911e-06 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 80%|######## | 4/5 [00:01<00:00, 3.78it/s][I 2024-05-22 13:01:54,932] Trial 63 finished with value: 3.8920361508245125e-07 and parameters: {'min_child_samples': 25}. Best is trial 61 with value: 3.282327081150373e-07. min_child_samples, val_score: 0.000000: 80%|######## | 4/5 [00:01<00:00, 3.78it/s] Early stopping, best iteration is: [204] valid_0's binary_logloss: 3.89204e-07 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 100%|##########| 5/5 [00:01<00:00, 3.62it/s][I 2024-05-22 13:01:55,228] Trial 64 finished with value: 2.1603725624802887e-07 and parameters: {'min_child_samples': 50}. Best is trial 64 with value: 2.1603725624802887e-07. min_child_samples, val_score: 0.000000: 100%|##########| 5/5 [00:01<00:00, 3.56it/s] Early stopping, best iteration is: [252] valid_0's binary_logloss: 2.16037e-07 In [12]: Copied! # save/load trainer dill_dump ( \"lgbm_trainer.dill\" , trainer ) trainer = dill_load ( \"lgbm_trainer.dill\" ) # save/load trainer dill_dump(\"lgbm_trainer.dill\", trainer) trainer = dill_load(\"lgbm_trainer.dill\") In [13]: Copied! pprint ( metrics_dict ) pprint(metrics_dict) {'cls_report': {'0': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 94}, '1': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 10}, 'accuracy': 1.0, 'macro avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}, 'weighted avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}}, 'cm': [[94, 0], [0, 10]], 'prec_rec_curve': [[0.09615384615384616, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 1.0]]} In [14]: Copied! pprint ( trainer . compute_metrics ( df_test , with_dynamic_binary_threshold = True )) pprint(trainer.compute_metrics(df_test, with_dynamic_binary_threshold=True)) {'cls_report': {'0': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 94}, '1': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 10}, 'accuracy': 1.0, 'macro avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}, 'weighted avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}}, 'cm': [[94, 0], [0, 10]], 'prec_rec_curve': [[0.09615384615384616, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 1.0]]}","title":"Train a testing model"},{"location":"notebooks/04_model_training_pipeline.html#mlflow","text":"tracking model registration in Minio permanent experiment deletion # in python mlflow.get_experiment_by_name(\"success6g_lgbm\").experiment_id # in cmd mlflow experiments delete -x 1 In [26]: Copied! mlflow_host = \"10.152.183.54\" mlflow_host_url = \"mlflow.mlflow.svc.cluster.local\" mlflow_port = \"5000\" os . environ [ \"AWS_ACCESS_KEY_ID\" ] = \"minioadmin\" os . environ [ \"AWS_SECRET_ACCESS_KEY\" ] = \"minioadmin\" os . environ [ \"MLFLOW_S3_ENDPOINT_URL\" ] = f \"http://10.152.183.156:9000\" mlflow . set_tracking_uri ( \"http://\" + mlflow_host + \":\" + mlflow_port ) experiment_id = get_or_create_experiment ( \"success6g\" ) mlflow . set_experiment ( experiment_id = experiment_id ) mlflow_host = \"10.152.183.54\" mlflow_host_url = \"mlflow.mlflow.svc.cluster.local\" mlflow_port = \"5000\" os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\" mlflow.set_tracking_uri(\"http://\" + mlflow_host + \":\" + mlflow_port) experiment_id = get_or_create_experiment(\"success6g\") mlflow.set_experiment(experiment_id=experiment_id) Out[26]: <Experiment: artifact_location='s3://mlflow/3', creation_time=1716388845676, experiment_id='3', last_update_time=1716388845676, lifecycle_stage='active', name='success6g', tags={}> In [27]: Copied! metrics_dict_flattened = flatten_dict ( metrics_dict ) # mlflow metrics can be only int, float not list del metrics_dict_flattened [ \"cm\" ] del metrics_dict_flattened [ \"prec_rec_curve\" ] metrics_dict_flattened = flatten_dict(metrics_dict) # mlflow metrics can be only int, float not list del metrics_dict_flattened[\"cm\"] del metrics_dict_flattened[\"prec_rec_curve\"] In [29]: Copied! run_name = \"test\" with mlflow . start_run ( experiment_id = experiment_id , run_name = run_name , nested = True ) as run : mlflow . log_params ( trainer . optimizer . best ) mlflow . log_metrics ( metrics_dict_flattened ) # Log tags mlflow . set_tags ( tags = { \"project\" : \"SUCCESS6G\" , \"optimizer_engine\" : \"optuna\" , \"model_family\" : \"ligtgbm\" , \"feature_set_version\" : 1 , } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow . pyfunc . log_model ( python_model = trainer , artifact_path = artifact_path , registered_model_name = registered_model_name , ) model_uri = mlflow . get_artifact_uri ( artifact_path ) print ( f \"Run ID: \\n { run . info . run_id } \\n Model uri: \\n { model_uri } \" ) run_name = \"test\" with mlflow.start_run( experiment_id=experiment_id, run_name=run_name, nested=True ) as run: mlflow.log_params(trainer.optimizer.best) mlflow.log_metrics(metrics_dict_flattened) # Log tags mlflow.set_tags( tags={ \"project\": \"SUCCESS6G\", \"optimizer_engine\": \"optuna\", \"model_family\": \"ligtgbm\", \"feature_set_version\": 1, } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow.pyfunc.log_model( python_model=trainer, artifact_path=artifact_path, registered_model_name=registered_model_name, ) model_uri = mlflow.get_artifact_uri(artifact_path) print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\") 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: typing.Union[pandas.core.frame.DataFrame, dict], skipping schema inference 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: <class 'pandas.core.frame.DataFrame'>, skipping schema inference Successfully registered model 'success6g_model'. 2024/05/22 14:44:22 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: success6g_model, version 1 Run ID: 24fedb96c5004767800fa32a3220c8dd Model uri: s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model Created version '1' of model 'success6g_model'.","title":"Mlflow"},{"location":"notebooks/04_model_training_pipeline.html#testing-of-predictions","text":"","title":"Testing of predictions"},{"location":"notebooks/04_model_training_pipeline.html#example-data","text":"In [30]: Copied! df_pd . iloc [: 2 ] df_pd.iloc[:2] Out[30]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc In [31]: Copied! # for testing using mlflow inference service request_dict = { \"engine_load\" : [ 100.0 , 100.0 ], \"engine_coolant_temp\" : [ 17.0 , 17.0 ], \"engine_speed\" : [ 904.5 , 906.0 ], \"vehicle_speed\" : [ 0.0 , 0.0 ], \"intake_air_temp\" : [ 10.0 , 11.0 ], \"maf\" : [ 12.55 , 12.36 ], \"throttle_position\" : [ 83.14 , 83.14 ], \"fuel_rg_pressure\" : [ 37270.0 , 37800.0 ], \"barometric_pressure\" : [ 101.0 , 101.0 ], \"control_voltage\" : [ 0.06 , 14.56 ], \"class\" : [ 1 , 1 ], \"car_id\" : [ \"123abc\" , \"123abc\" ], } # for testing using mlflow inference service request_dict = { \"engine_load\": [100.0, 100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"class\": [1, 1], \"car_id\": [\"123abc\", \"123abc\"], }","title":"Example data"},{"location":"notebooks/04_model_training_pipeline.html#trainer","text":"In [32]: Copied! trainer . predict ( df = df_pd . iloc [: 2 ] . drop ( columns = [ \"class\" ]), context = {}) trainer.predict(df=df_pd.iloc[:2].drop(columns=[\"class\"]), context={}) Out[32]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 0 14.269467 1 14.269467","title":"Trainer"},{"location":"notebooks/04_model_training_pipeline.html#downloaded-trainer","text":"In [33]: Copied! model_uri model_uri Out[33]: 's3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model' In [34]: Copied! loaded_trainer = mlflow . pyfunc . load_model ( model_uri ) loaded_trainer = mlflow.pyfunc.load_model(model_uri) Downloading artifacts: 0%| | 0/9 [00:00<?, ?it/s] In [35]: Copied! loaded_trainer . predict ( df_pd . iloc [: 2 ] . drop ( columns = [ \"class\" ])) loaded_trainer.predict(df_pd.iloc[:2].drop(columns=[\"class\"])) Out[35]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 0 14.269467 1 14.269467","title":"Downloaded Trainer"},{"location":"notebooks/04_model_training_pipeline.html#downloadedserved-trainer","text":"i.e. testing model locally In [36]: Copied! model_uri model_uri Out[36]: 's3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model' In [ ]: Copied! ! mlflow models serve - m \"s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model\" -- env - manager local - p 5000 ! mlflow models serve -m \"s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model\" --env-manager local -p 5000 Downloading artifacts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 2088.80it/s] 2024/05/22 14:45:07 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function' Downloading artifacts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 9/9 [00:00<00:00, 69.72it/s] 2024/05/22 14:45:07 INFO mlflow.pyfunc.backend: === Running command 'exec gunicorn --timeout=60 -b 127.0.0.1:5000 -w 1 ${GUNICORN_CMD_ARGS} -- mlflow.pyfunc.scoring_server.wsgi:app' [2024-05-22 14:45:07 +0000] [17971] [INFO] Starting gunicorn 22.0.0 [2024-05-22 14:45:07 +0000] [17971] [INFO] Listening at: http://127.0.0.1:5000 (17971) [2024-05-22 14:45:07 +0000] [17971] [INFO] Using worker: sync [2024-05-22 14:45:07 +0000] [17972] [INFO] Booting worker with pid: 17972 root@jupyter-5uperpalo:~/assignment# curl -X POST -H \"Content-Type:application/json\" --data '{\"inputs\": {\"engine_load\": [100.0 ,100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"car_id\": [\"123abc\", \"123abc\"]}}' http://127.0.0.1:5000/invocations {\"predictions\": [{\"class\": 14.26946728558632}, {\"class\": 14.26946728558632}]}","title":"Downloaded/Served Trainer"},{"location":"notebooks/04_model_training_pipeline.html#model-deployment-using-kserve","text":"https://mlflow.org/docs/latest/deployment/deploy-model-to-kubernetes/tutorial.html?highlight=kserve#step-7-deploying-the-model-to-kserve","title":"Model deployment using Kserve"},{"location":"notebooks/04_model_training_pipeline.html#using-model_uri","text":"since I configured minio as artifact store I can use model_uri - I was WRONG! procedure: make sure the kserve is able to reach minio * by [defining s3-secret and attaching it to service account](https://kserve.github.io/website/0.7/modelserving/storage/s3/s3/#create-s3-secret) * created ServiceAccount must be in a same namespace as the InferenceService we are going to create, [e.g. stackoverflow here](https://stackoverflow.com/a/70977798) * `If an application does not specify a serviceAccountName, it uses the \"default\" service account.`[e.g. here](https://kubernetes.io/docs/reference/access-authn-authz/rbac/#role-binding-examples) ``` apiVersion: v1 kind: Secret metadata: name: s3creds namespace: mlflow-kserve-success6g annotations: serving.kserve.io/s3-endpoint: 10.152.183.156:9000 # replace with your s3 endpoint e.g minio-service.kubeflow:9000 serving.kserve.io/s3-usehttps: \"0\" # by default 1, if testing with minio you can set to 0 # serving.kserve.io/s3-region: \"us-east-2\" serving.kserve.io/s3-useanoncredential: \"false\" # omitting this is the same as false, if true will ignore provided credential and use anonymous credentials type: Opaque stringData: # use `stringData` for raw credential string or `data` for base64 encoded string AWS_ACCESS_KEY_ID: \"minioadmin\" AWS_SECRET_ACCESS_KEY: \"minioadmin\" ``` `kubectl apply -f create-s3-secret.yaml` create namespace ``` kubectl create namespace mlflow-kserve-success6g namespace/mlflow-kserve-success6g created ``` specify configuration of the deployment in the file `mlflow-kserve-success6g_model_uri.yaml` ``` apiVersion: \"serving.kserve.io/v1beta1\" kind: \"InferenceService\" metadata: name: \"mlflow-success6g\" namespace: \"mlflow-kserve-success6g\" spec: predictor: model: modelFormat: name: mlflow protocolVersion: v2 storageUri: \"s3://mlflow/1/777cf64c922149a4b77c85987865deb0/artifacts/success6g_model\" ``` deploy ``` kubectl apply -f mlflow-kserve-success6g.yaml inferenceservice.serving.kserve.io/mlflow-success6g created ``` check the status of the deployment `kubectl get inferenceservice mlflow-success6g` specify testing json in `test-input.json` ``` { \"inputs\": [ { \"name\": \"input\", \"shape\": [13], \"datatype\": \"FP32\", \"data\": { \"engine_load\": [100.0 ,100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"car_id\": [\"123abc\", \"123abc\"], } } ] } ``` test json ``` SERVICE_HOSTNAME=$(kubectl get inferenceservice mlflow-success6g -n mlflow-kserve-success6g -o jsonpath='{.status.url}' | cut -d \"/\" -f 3) curl -v \\ -H \"Host: ${SERVICE_HOSTNAME}\" \\ -H \"Content-Type: application/json\" \\ -d @./test-input.json \\ http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/mlflow-success6g/infer ``` but after all the trouble this can still fail if you developed the model in different python version than kserve is using ``` pmulinka@saiacheron:~/kubernetes/kserve$ kubectl logs -p mlflow-success6g-predictor-00001-deployment-74ccf4fb5d-lktz9 -n mlflow-kserve-success6g Defaulted container \"kserve-container\" out of: kserve-container, queue-proxy, storage-initializer (init) Environment tarball not found at '/mnt/models/environment.tar.gz' Environment not found at './envs/environment' 2024-05-16 15:32:55,293 [mlserver.parallel] DEBUG - Starting response processing loop... 2024-05-16 15:32:55,298 [mlserver.rest] INFO - HTTP server running on http://0.0.0.0:8080 INFO: Started server process [1] INFO: Waiting for application startup. 2024-05-16 15:32:55,411 [mlserver.metrics] INFO - Metrics server running on http://0.0.0.0:8082 2024-05-16 15:32:55,412 [mlserver.metrics] INFO - Prometheus scraping endpoint can be accessed on http://0.0.0.0:8082/metrics INFO: Started server process [1] INFO: Waiting for application startup. INFO: Application startup complete. 2024-05-16 15:32:57,613 [mlserver.grpc] INFO - gRPC server running on http://0.0.0.0:9000 INFO: Application startup complete. INFO: Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit) INFO: Uvicorn running on http://0.0.0.0:8082 (Press CTRL+C to quit) 2024/05/16 15:32:58 WARNING mlflow.pyfunc: Detected one or more mismatches between the model's dependencies and the current Python environment: - mlflow (current: 2.3.1, required: mlflow==2.12.2) - cryptography (current: 38.0.4, required: cryptography==40.0.2) - cytoolz (current: uninstalled, required: cytoolz==0.12.0) - defusedxml (current: uninstalled, required: defusedxml==0.7.1) - distributed (current: uninstalled, required: distributed==2023.5.1) - lightgbm (current: 3.3.5, required: lightgbm==4.3.0) - lz4 (current: uninstalled, required: lz4==4.3.2) - numpy (current: 1.23.5, required: numpy==1.24.3) - optuna-integration (current: uninstalled, required: optuna-integration==3.6.0) - pandas (current: 2.0.1, required: pandas==2.2.2) - pyarrow (current: 11.0.0, required: pyarrow==12.0.0) - redis (current: uninstalled, required: redis==5.0.4) - scipy (current: 1.9.1, required: scipy==1.10.1) To fix the mismatches, call `mlflow.pyfunc.get_model_dependencies(model_uri)` to fetch the model's environment and install dependencies using the resulting environment file. 2024/05/16 15:32:58 WARNING mlflow.pyfunc: The version of Python that the model was saved in, `Python 3.10.11`, differs from the version of Python that is currently running, `Python 3.8.16`, and may be incompatible 2024-05-16 15:32:58,967 [mlserver] INFO - Couldn't load model 'mlflow-success6g'. Model will be removed from registry. 2024-05-16 15:32:58,967 [mlserver.parallel] ERROR - An error occurred processing a model update of type 'Load'. Traceback (most recent call last): File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/worker.py\", line 158, in _process_model_update await self._model_registry.load(model_settings) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 293, in load return await self._models[model_settings.name].load(model_settings) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 148, in load await self._load_model(new_model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 165, in _load_model model.ready = await model.load() File \"/opt/conda/lib/python3.8/site-packages/mlserver_mlflow/runtime.py\", line 155, in load self._model = mlflow.pyfunc.load_model(model_uri) File \"/opt/conda/lib/python3.8/site-packages/mlflow/pyfunc/__init__.py\", line 596, in load_model model_impl = importlib.import_module(conf[MAIN])._load_pyfunc(data_path) File \"/opt/conda/lib/python3.8/site-packages/mlflow/pyfunc/model.py\", line 291, in _load_pyfunc python_model = cloudpickle.load(f) ModuleNotFoundError: No module named 'inference_model' 2024-05-16 15:32:58,970 [mlserver] INFO - Couldn't load model 'mlflow-success6g'. Model will be removed from registry. 2024-05-16 15:32:58,975 [mlserver.parallel] ERROR - An error occurred processing a model update of type 'Unload'. Traceback (most recent call last): File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/worker.py\", line 160, in _process_model_update await self._model_registry.unload_version( File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 302, in unload_version await model_registry.unload_version(version) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 201, in unload_version model = await self.get_model(version) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 237, in get_model raise ModelNotFound(self._name, version) mlserver.errors.ModelNotFound: Model mlflow-success6g not found 2024-05-16 15:32:58,976 [mlserver] ERROR - Some of the models failed to load during startup! Traceback (most recent call last): File \"/opt/conda/lib/python3.8/site-packages/mlserver/server.py\", line 125, in start await asyncio.gather( File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 293, in load return await self._models[model_settings.name].load(model_settings) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 148, in load await self._load_model(new_model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 161, in _load_model model = await callback(model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/registry.py\", line 152, in load_model loaded = await pool.load_model(model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/pool.py\", line 74, in load_model await self._dispatcher.dispatch_update(load_message) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 123, in dispatch_update return await asyncio.gather( File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 138, in _dispatch_update return await self._dispatch(worker_update) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 146, in _dispatch return await self._wait_response(internal_id) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 152, in _wait_response inference_response = await async_response mlserver.parallel.errors.WorkerError: builtins.ModuleNotFoundError: No module named 'inference_model' 2024-05-16 15:32:58,978 [mlserver.parallel] INFO - Waiting for shutdown of default inference pool... 2024-05-16 15:32:59,163 [mlserver.parallel] INFO - Shutdown of default inference pool complete 2024-05-16 15:32:59,163 [mlserver.grpc] INFO - Waiting for gRPC server shutdown 2024-05-16 15:32:59,169 [mlserver.grpc] INFO - gRPC server shutdown complete INFO: Shutting down INFO: Shutting down INFO: Waiting for application shutdown. INFO: Application shutdown complete. INFO: Finished server process [1] INFO: Waiting for application shutdown. INFO: Application shutdown complete. INFO: Finished server process [1] ``` and some possible readiness probe issues * apparently I am not alone in this, e.g. [here](https://stackoverflow.com/questions/62569747/ml-model-pod-keeps-restarting-in-seldon-deployment) ``` pmulinka@saiacheron:~/kubernetes/kserve$ kubectl describe pod mlflow-success6g-predictor-00001-deployment-74ccf4fb5d-lktz9 -n mlflow-kserve-success6g Events: Type Reason Age From Message ---- ------ ---- ---- ------- Warning Unhealthy 4m11s (x268 over 34m) kubelet Readiness probe failed: Get \"http://10.1.4.204:8012/\": context deadline exceeded (Client.Timeout exceeded while awaiting headers) ```","title":"Using model_uri"},{"location":"notebooks/04_model_training_pipeline.html#using-docker-image","text":"create docker ``` sudo apt-get install python3.10-venv python3.10 -m venv python310venv source python310venv/bin/activate pip install mlflow pip install boto3 export AWS_ACCESS_KEY_ID=minioadmin export AWS_SECRET_ACCESS_KEY=minioadmin export MLFLOW_S3_ENDPOINT_URL=http://10.152.183.156:9000 mlflow models build-docker -m s3://mlflow/1/777cf64c922149a4b77c85987865deb0/artifacts/success6g_model -n 5uperpalo/mlflow-success6g --enable-mlserver ``` generate token and login to DockerHub, [generate DockerHub token](https://hub.docker.com/settings/security?generateToken=true) and login ``` (python310venv) pmulinka@iesc-gpu3:~/scripts$ docker login -u 5uperpalo Password: WARNING! Your password will be stored unencrypted in /home/pmulinka/.docker/config.json. Configure a credential helper to remove this warning. See https://docs.docker.com/engine/reference/commandline/login/#credentials-store Login Succeeded ``` push the image to dockerhub ``` (python310venv) pmulinka@iesc-gpu3:~/scripts$ docker push 5uperpalo/mlflow-success6g Using default tag: latest The push refers to repository [docker.io/5uperpalo/mlflow-success6g] e5f76b582f6c: Pushed 624821c51019: Pushed 93256a85bb86: Pushed 2f62dcdb5ae7: Pushed 1146ddf03b42: Pushed 639ddc87d13f: Pushed 4f18e59d57c4: Pushed 5761e01a334a: Pushed f31b6bde159a: Pushed c25ef1a18d31: Pushed 40a774d6610c: Pushed a2b215b4258b: Pushed b56a9203d2a0: Pushed 2536dbfb2294: Pushed 67ea405a7d49: Pushed a81c3606ee5c: Pushed 4a1518ebc26e: Mounted from library/ubuntu latest: digest: sha256:57f653a3c8d63519c07dccffc6eb12dba5006db07ebcd869467ce752fb1b0bda size: 3899 ``` specify configuration of the deployment in the file `mlflow-kserve-success6g_model_uri.yaml` ``` apiVersion: \"serving.kserve.io/v1beta1\" kind: \"InferenceService\" metadata: name: \"mlflow-success6g\" namespace: \"mlflow-kserve-success6g\" spec: predictor: containers: - name: \"mlflow-success6g\" image: \"5uperpalo/mlflow-success6g\" ports: - containerPort: 8080 protocol: TCP #env: #- name: PROTOCOL #value: \"v2\" resources: limits: cpu: 2 memory: 8Gi requests: cpu: 2 memory: 8Gi ``` deploy ``` pmulinka@saiacheron:~/kubernetes/kserve$ kubectl apply -f mlflow-kserve-success6g_docker_image.yaml inferenceservice.serving.kserve.io/mlflow-success6g created ``` check status of deployment ``` pmulinka@saiacheron:~$ kubectl get inferenceservice -n mlflow-kserve-success6g NAME URL READY PREV LATEST PREVROLLEDOUTREVISION LATESTREADYREVISION AGE mlflow-success6g http://mlflow-success6g.mlflow-kserve-success6g.svc.cluster.local False 100 mlflow-success6g-predictor-00001 6m4s pmulinka@saiacheron:~$ kubectl get pods -n mlflow-kserve-success6g NAME READY STATUS RESTARTS AGE mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 2/2 Running 1 (2m14s ago) 4m47s pmulinka@saiacheron:~$ kubectl get inferenceservice mlflow-success6g -n mlflow-kserve-success6g -o yaml apiVersion: serving.kserve.io/v1beta1 kind: InferenceService metadata: annotations: kubectl.kubernetes.io/last-applied-configuration: | {\"apiVersion\":\"serving.kserve.io/v1beta1\",\"kind\":\"InferenceService\",\"metadata\":{\"annotations\":{},\"name\":\"mlflow-success6g\",\"namespace\":\"mlflow-kserve-success6g\"},\"spec\":{\"predictor\":{\"containers\":[{\"image\":\"5uperpalo/mlflow-success6g\",\"name\":\"mlflow-success6g\",\"ports\":[{\"containerPort\":8080,\"protocol\":\"TCP\"}],\"resources\":{\"limits\":{\"cpu\":2,\"memory\":\"8Gi\"},\"requests\":{\"cpu\":2,\"memory\":\"8Gi\"}}}]}}} creationTimestamp: \"2024-05-28T14:33:05Z\" finalizers: - inferenceservice.finalizers generation: 1 name: mlflow-success6g namespace: mlflow-kserve-success6g resourceVersion: \"76010518\" uid: b9573ff3-0f67-4650-a0c7-51941a0fffe2 spec: predictor: containers: - image: 5uperpalo/mlflow-success6g name: kserve-container ports: - containerPort: 8080 protocol: TCP resources: limits: cpu: \"2\" memory: 8Gi requests: cpu: \"2\" memory: 8Gi status: address: url: http://mlflow-success6g.mlflow-kserve-success6g.svc.cluster.local components: predictor: address: url: http://mlflow-success6g-predictor.mlflow-kserve-success6g.svc.cluster.local latestCreatedRevision: mlflow-success6g-predictor-00001 latestReadyRevision: mlflow-success6g-predictor-00001 latestRolledoutRevision: mlflow-success6g-predictor-00001 traffic: - latestRevision: true percent: 100 revisionName: mlflow-success6g-predictor-00001 url: http://mlflow-success6g-predictor.mlflow-kserve-success6g.svc.cluster.local conditions: - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: Predictor ingress not created status: \"False\" type: IngressReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: PredictorConfigurationReady not ready severity: Info status: \"False\" type: LatestDeploymentReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" message: 'Revision \"mlflow-success6g-predictor-00001\" failed with message: Container failed with: container exited with no error.' reason: RevisionFailed severity: Info status: \"False\" type: PredictorConfigurationReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" message: Revision \"mlflow-success6g-predictor-00001\" failed to become ready. reason: RevisionMissing status: \"False\" type: PredictorReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" message: Revision \"mlflow-success6g-predictor-00001\" failed to become ready. reason: RevisionMissing severity: Info status: \"False\" type: PredictorRouteReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: Predictor ingress not created status: \"False\" type: Ready - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: PredictorRouteReady not ready severity: Info status: \"False\" type: RoutesReady modelStatus: copies: failedCopies: 0 totalCopies: 1 states: activeModelState: Loaded targetModelState: Pending transitionStatus: InProgress observedGeneration: 1 url: http://mlflow-success6g.mlflow-kserve-success6g.svc.cluster.local pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get events -n mlflow-kserve-success6g LAST SEEN TYPE REASON OBJECT MESSAGE 2m28s Warning BackOff pod/mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 Back-off restarting failed container kserve-container in pod mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7_mlflow-kserve-success6g(1ad2d6da-3a40-4b14-956a-0faee3ca9b3c) 2m28s Warning InternalError revision/mlflow-success6g-predictor-00001 failed to update PA \"mlflow-success6g-predictor-00001\": Operation cannot be fulfilled on podautoscalers.autoscaling.internal.knative.dev \"mlflow-success6g-predictor-00001\": the object has been modified; please apply your changes to the latest version and try again ``` debugging ``` # https://kserve.github.io/website/0.10/developer/debug/ pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get ksvc -n mlflow-kserve-success6g NAME URL LATESTCREATED LATESTREADY READY REASON mlflow-success6g-predictor http://mlflow-success6g-predictor.mlflow-kserve-success6g.svc.cluster.local mlflow-success6g-predictor-00001 mlflow-success6g-predictor-00001 False RevisionMissing # RevisionMissing Error pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get revision $(kubectl get configuration mlflow-success6g-predictor -n mlflow-kserve-success6g --output jsonpath=\"{.status.latestCreatedRevisionName}\") Error from server (NotFound): revisions.serving.knative.dev \"mlflow-success6g-predictor-00001\" not found pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get pod -l serving.kserve.io/inferenceservice=mlflow-success6g -n mlflow-kserve-success6g NAME READY STATUS RESTARTS AGE mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 0/2 CrashLoopBackOff 8 (4m51s ago) 43m pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get pod -l serving.kserve.io/inferenceservice=mlflow-success6g -n mlflow-kserve-success6g NAME READY STATUS RESTARTS AGE mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 2/2 Running 9 (7m7s ago) 45m pmulinka@saiacheron:~/kubernetes/kserve$ kubectl logs -l model=mlflow-success6g -n mlflow-kserve-success6g -c storage-initializer No resources found in mlflow-kserve-success6g namespace. pmulinka@saiacheron:~/kubernetes/kserve$ kubectl delete inferenceservice mlflow-success6g -n mlflow-kserve-success6g inferenceservice.serving.kserve.io \"mlflow-success6g\" deleted ```","title":"Using docker image"},{"location":"notebooks/04_model_training_pipeline.html#appendix","text":"output from making docker ``` (python310venv) pmulinka@iesc-gpu3:~/scripts$ mlflow models build-docker -m s3://mlflow/1/777cf64c922149a4b77c85987865deb0/artifacts/success6g_model -n 5uperpalo/mlflow-success6g --enable-mlserver 2024/05/16 22:20:25 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function' 2024/05/16 22:20:25 INFO mlflow.pyfunc.backend: Building docker image with name 5uperpalo/mlflow-success6g #0 building with \"default\" instance using docker driver #1 [internal] load build definition from Dockerfile #1 transferring dockerfile: 2.53kB done #1 DONE 0.1s #2 [internal] load metadata for docker.io/library/ubuntu:20.04 #2 DONE 1.6s #3 [internal] load .dockerignore #3 transferring context: 2B done #3 DONE 0.0s #4 [ 1/17] FROM docker.io/library/ubuntu:20.04@sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b #4 resolve docker.io/library/ubuntu:20.04@sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b 0.0s done #4 ... #5 [internal] load build context #5 transferring context: 197.37kB done #5 DONE 0.1s #4 [ 1/17] FROM docker.io/library/ubuntu:20.04@sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b #4 sha256:2abc4dfd83182546da40dfae3e391db0810ad4a130fb5a887c6ceb3df8e1d310 2.29kB / 2.29kB done #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 0B / 27.51MB 0.2s #4 sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b 1.13kB / 1.13kB done #4 sha256:cc9cc8169c9517ae035cf293b15f06922cb8c6c864d625a72b7b18667f264b70 424B / 424B done #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 3.15MB / 27.51MB 0.4s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 8.39MB / 27.51MB 0.7s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 11.53MB / 27.51MB 0.9s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 16.78MB / 27.51MB 1.2s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 22.02MB / 27.51MB 1.5s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 25.17MB / 27.51MB 1.7s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 27.26MB / 27.51MB 1.8s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 27.51MB / 27.51MB 1.9s done #4 extracting sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 #4 extracting sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 1.8s done #4 DONE 4.0s #6 [ 2/17] RUN apt-get -y update && DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get install -y --no-install-recommends wget curl nginx ca-certificates bzip2 build-essential cmake git-core #6 0.681 Get:1 http://security.ubuntu.com/ubuntu focal-security InRelease [114 kB] #6 0.682 Get:2 http://archive.ubuntu.com/ubuntu focal InRelease [265 kB] #6 1.324 Get:3 http://archive.ubuntu.com/ubuntu focal-updates InRelease [114 kB] #6 1.432 Get:4 http://security.ubuntu.com/ubuntu focal-security/universe amd64 Packages [1205 kB] #6 1.476 Get:5 http://archive.ubuntu.com/ubuntu focal-backports InRelease [108 kB] #6 1.690 Get:6 http://archive.ubuntu.com/ubuntu focal/restricted amd64 Packages [33.4 kB] #6 1.838 Get:7 http://archive.ubuntu.com/ubuntu focal/multiverse amd64 Packages [177 kB] #6 1.897 Get:8 http://archive.ubuntu.com/ubuntu focal/main amd64 Packages [1275 kB] #6 2.177 Get:9 http://security.ubuntu.com/ubuntu focal-security/restricted amd64 Packages [3608 kB] #6 2.537 Get:10 http://archive.ubuntu.com/ubuntu focal/universe amd64 Packages [11.3 MB] #6 3.807 Get:11 http://security.ubuntu.com/ubuntu focal-security/main amd64 Packages [3669 kB] #6 5.468 Get:12 http://security.ubuntu.com/ubuntu focal-security/multiverse amd64 Packages [29.8 kB] #6 7.649 Get:13 http://archive.ubuntu.com/ubuntu focal-updates/restricted amd64 Packages [3758 kB] #6 9.304 Get:14 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 Packages [1502 kB] #6 9.973 Get:15 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 Packages [4143 kB] #6 12.14 Get:16 http://archive.ubuntu.com/ubuntu focal-updates/multiverse amd64 Packages [32.5 kB] #6 12.15 Get:17 http://archive.ubuntu.com/ubuntu focal-backports/main amd64 Packages [55.2 kB] #6 12.18 Get:18 http://archive.ubuntu.com/ubuntu focal-backports/universe amd64 Packages [28.6 kB] #6 12.44 Fetched 31.5 MB in 12s (2621 kB/s) #6 12.44 Reading package lists... #6 13.60 Reading package lists... #6 14.67 Building dependency tree... #6 14.83 Reading state information... #6 15.04 bzip2 is already the newest version (1.0.8-2). #6 15.04 The following additional packages will be installed: #6 15.04 binutils binutils-common binutils-x86-64-linux-gnu cmake-data cpp cpp-9 #6 15.04 dpkg-dev fontconfig-config fonts-dejavu-core g++ g++-9 gcc gcc-9 gcc-9-base #6 15.04 git-man iproute2 libarchive13 libasan5 libasn1-8-heimdal libatomic1 #6 15.04 libbinutils libbrotli1 libbsd0 libc-dev-bin libc6-dev libcap2 libcap2-bin #6 15.04 libcc1-0 libcrypt-dev libctf-nobfd0 libctf0 libcurl3-gnutls libcurl4 #6 15.04 libdpkg-perl libelf1 liberror-perl libexpat1 libfontconfig1 libfreetype6 #6 15.04 libgcc-9-dev libgd3 libgdbm-compat4 libgdbm6 libgomp1 libgssapi-krb5-2 #6 15.04 libgssapi3-heimdal libhcrypto4-heimdal libheimbase1-heimdal #6 15.04 libheimntlm0-heimdal libhx509-5-heimdal libicu66 libisl22 libitm1 libjbig0 #6 15.04 libjpeg-turbo8 libjpeg8 libjsoncpp1 libk5crypto3 libkeyutils1 #6 15.04 libkrb5-26-heimdal libkrb5-3 libkrb5support0 libldap-2.4-2 libldap-common #6 15.04 liblsan0 libmnl0 libmpc3 libmpfr6 libnghttp2-14 #6 15.04 libnginx-mod-http-image-filter libnginx-mod-http-xslt-filter #6 15.04 libnginx-mod-mail libnginx-mod-stream libperl5.30 libpng16-16 libpsl5 #6 15.04 libquadmath0 librhash0 libroken18-heimdal librtmp1 libsasl2-2 #6 15.04 libsasl2-modules-db libsqlite3-0 libssh-4 libssl1.1 libstdc++-9-dev libtiff5 #6 15.04 libtsan0 libubsan1 libuv1 libwebp6 libwind0-heimdal libx11-6 libx11-data #6 15.04 libxau6 libxcb1 libxdmcp6 libxml2 libxpm4 libxslt1.1 libxtables12 #6 15.04 linux-libc-dev make nginx-common nginx-core openssl patch perl #6 15.04 perl-modules-5.30 tzdata ucf xz-utils #6 15.04 Suggested packages: #6 15.04 binutils-doc cmake-doc ninja-build cpp-doc gcc-9-locales debian-keyring #6 15.04 g++-multilib g++-9-multilib gcc-9-doc gcc-multilib manpages-dev autoconf #6 15.04 automake libtool flex bison gdb gcc-doc gcc-9-multilib gettext-base #6 15.04 git-daemon-run | git-daemon-sysvinit git-doc git-el git-email git-gui gitk #6 15.04 gitweb git-cvs git-mediawiki git-svn iproute2-doc lrzip glibc-doc gnupg #6 15.04 | gnupg2 bzr libgd-tools gdbm-l10n krb5-doc krb5-user libstdc++-9-doc #6 15.04 make-doc fcgiwrap nginx-doc ssl-cert ed diffutils-doc perl-doc #6 15.04 libterm-readline-gnu-perl | libterm-readline-perl-perl libb-debug-perl #6 15.04 liblocale-codes-perl #6 15.04 Recommended packages: #6 15.04 fakeroot gnupg | gnupg2 libalgorithm-merge-perl less ssh-client libatm1 #6 15.04 manpages manpages-dev libpam-cap libfile-fcntllock-perl #6 15.04 liblocale-gettext-perl krb5-locales publicsuffix libsasl2-modules netbase #6 15.30 The following NEW packages will be installed: #6 15.30 binutils binutils-common binutils-x86-64-linux-gnu build-essential #6 15.30 ca-certificates cmake cmake-data cpp cpp-9 curl dpkg-dev fontconfig-config #6 15.30 fonts-dejavu-core g++ g++-9 gcc gcc-9 gcc-9-base git git-man iproute2 #6 15.30 libarchive13 libasan5 libasn1-8-heimdal libatomic1 libbinutils libbrotli1 #6 15.30 libbsd0 libc-dev-bin libc6-dev libcap2 libcap2-bin libcc1-0 libcrypt-dev #6 15.30 libctf-nobfd0 libctf0 libcurl3-gnutls libcurl4 libdpkg-perl libelf1 #6 15.30 liberror-perl libexpat1 libfontconfig1 libfreetype6 libgcc-9-dev libgd3 #6 15.30 libgdbm-compat4 libgdbm6 libgomp1 libgssapi-krb5-2 libgssapi3-heimdal #6 15.30 libhcrypto4-heimdal libheimbase1-heimdal libheimntlm0-heimdal #6 15.30 libhx509-5-heimdal libicu66 libisl22 libitm1 libjbig0 libjpeg-turbo8 #6 15.30 libjpeg8 libjsoncpp1 libk5crypto3 libkeyutils1 libkrb5-26-heimdal libkrb5-3 #6 15.30 libkrb5support0 libldap-2.4-2 libldap-common liblsan0 libmnl0 libmpc3 #6 15.30 libmpfr6 libnghttp2-14 libnginx-mod-http-image-filter #6 15.30 libnginx-mod-http-xslt-filter libnginx-mod-mail libnginx-mod-stream #6 15.30 libperl5.30 libpng16-16 libpsl5 libquadmath0 librhash0 libroken18-heimdal #6 15.30 librtmp1 libsasl2-2 libsasl2-modules-db libsqlite3-0 libssh-4 libssl1.1 #6 15.30 libstdc++-9-dev libtiff5 libtsan0 libubsan1 libuv1 libwebp6 libwind0-heimdal #6 15.30 libx11-6 libx11-data libxau6 libxcb1 libxdmcp6 libxml2 libxpm4 libxslt1.1 #6 15.30 libxtables12 linux-libc-dev make nginx nginx-common nginx-core openssl patch #6 15.30 perl perl-modules-5.30 tzdata ucf wget xz-utils #6 15.41 0 upgraded, 119 newly installed, 0 to remove and 0 not upgraded. #6 15.41 Need to get 82.1 MB of archives. #6 15.41 After this operation, 389 MB of additional disk space will be used. #6 15.41 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 perl-modules-5.30 all 5.30.0-9ubuntu0.5 [2739 kB] #6 16.07 Get:2 http://archive.ubuntu.com/ubuntu focal/main amd64 libgdbm6 amd64 1.18.1-5 [27.4 kB] #6 16.07 Get:3 http://archive.ubuntu.com/ubuntu focal/main amd64 libgdbm-compat4 amd64 1.18.1-5 [6244 B] #6 16.07 Get:4 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libperl5.30 amd64 5.30.0-9ubuntu0.5 [3941 kB] #6 16.76 Get:5 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 perl amd64 5.30.0-9ubuntu0.5 [224 kB] #6 16.79 Get:6 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libssl1.1 amd64 1.1.1f-1ubuntu2.22 [1323 kB] #6 17.04 Get:7 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openssl amd64 1.1.1f-1ubuntu2.22 [622 kB] #6 17.15 Get:8 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 ca-certificates all 20230311ubuntu0.20.04.1 [152 kB] #6 17.18 Get:9 http://archive.ubuntu.com/ubuntu focal/main amd64 libbsd0 amd64 0.10.0-1 [45.4 kB] #6 17.20 Get:10 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcap2 amd64 1:2.32-1ubuntu0.1 [15.8 kB] #6 17.20 Get:11 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libelf1 amd64 0.176-1.1ubuntu0.1 [44.2 kB] #6 17.33 Get:12 http://archive.ubuntu.com/ubuntu focal/main amd64 libmnl0 amd64 1.0.4-2 [12.3 kB] #6 17.38 Get:13 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxtables12 amd64 1.8.4-3ubuntu2.1 [28.7 kB] #6 17.42 Get:14 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcap2-bin amd64 1:2.32-1ubuntu0.1 [26.2 kB] #6 17.46 Get:15 http://archive.ubuntu.com/ubuntu focal/main amd64 iproute2 amd64 5.5.0-1ubuntu1 [858 kB] #6 20.61 Get:16 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libexpat1 amd64 2.2.9-1ubuntu0.6 [74.6 kB] #6 21.00 Get:17 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 tzdata all 2024a-0ubuntu0.20.04 [301 kB] #6 22.38 Get:18 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libicu66 amd64 66.1-2ubuntu2.1 [8515 kB] #6 51.24 Get:19 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsqlite3-0 amd64 3.31.1-4ubuntu0.6 [549 kB] #6 53.10 Get:20 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxml2 amd64 2.9.10+dfsg-5ubuntu0.20.04.7 [640 kB] #6 55.32 Get:21 http://archive.ubuntu.com/ubuntu focal/main amd64 ucf all 3.0038+nmu1 [51.6 kB] #6 55.50 Get:21 http://archive.ubuntu.com/ubuntu focal/main amd64 ucf all 3.0038+nmu1 [51.6 kB] #6 55.60 Get:22 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 xz-utils amd64 5.2.4-1ubuntu1.1 [82.6 kB] #6 55.66 Get:23 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkrb5support0 amd64 1.17-6ubuntu4.4 [31.0 kB] #6 55.70 Get:24 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libk5crypto3 amd64 1.17-6ubuntu4.4 [79.9 kB] #6 55.72 Get:25 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkeyutils1 amd64 1.6-6ubuntu1.1 [10.3 kB] #6 55.73 Get:26 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkrb5-3 amd64 1.17-6ubuntu4.4 [330 kB] #6 55.80 Get:27 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgssapi-krb5-2 amd64 1.17-6ubuntu4.4 [121 kB] #6 55.82 Get:28 http://archive.ubuntu.com/ubuntu focal/main amd64 libpng16-16 amd64 1.6.37-2 [179 kB] #6 55.86 Get:29 http://archive.ubuntu.com/ubuntu focal/main amd64 libpsl5 amd64 0.21.0-1ubuntu1 [51.5 kB] #6 55.86 Get:30 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libuv1 amd64 1.34.2-1ubuntu1.5 [80.9 kB] #6 55.88 Get:31 http://archive.ubuntu.com/ubuntu focal/main amd64 libxau6 amd64 1:1.0.9-0ubuntu1 [7488 B] #6 55.96 Get:32 http://archive.ubuntu.com/ubuntu focal/main amd64 libxdmcp6 amd64 1:1.1.3-0ubuntu1 [10.6 kB] #6 56.01 Get:33 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb1 amd64 1.14-2 [44.7 kB] #6 56.08 Get:34 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-data all 2:1.6.9-2ubuntu1.6 [114 kB] #6 56.15 Get:35 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-6 amd64 2:1.6.9-2ubuntu1.6 [577 kB] #6 56.26 Get:36 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 wget amd64 1.20.3-1ubuntu2 [348 kB] #6 56.32 Get:37 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 binutils-common amd64 2.34-6ubuntu1.9 [208 kB] #6 56.37 Get:38 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libbinutils amd64 2.34-6ubuntu1.9 [475 kB] #6 56.46 Get:39 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libctf-nobfd0 amd64 2.34-6ubuntu1.9 [48.2 kB] #6 56.47 Get:40 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libctf0 amd64 2.34-6ubuntu1.9 [46.6 kB] #6 56.48 Get:41 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 binutils-x86-64-linux-gnu amd64 2.34-6ubuntu1.9 [1614 kB] #6 56.76 Get:42 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 binutils amd64 2.34-6ubuntu1.9 [3380 B] #6 56.85 Get:43 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libc-dev-bin amd64 2.31-0ubuntu9.15 [71.8 kB] #6 57.00 Get:44 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 linux-libc-dev amd64 5.4.0-182.202 [1134 kB] #6 57.28 Get:45 http://archive.ubuntu.com/ubuntu focal/main amd64 libcrypt-dev amd64 1:4.4.10-10ubuntu4 [104 kB] #6 57.30 Get:46 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libc6-dev amd64 2.31-0ubuntu9.15 [2519 kB] #6 57.75 Get:47 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 gcc-9-base amd64 9.4.0-1ubuntu1~20.04.2 [18.9 kB] #6 57.75 Get:48 http://archive.ubuntu.com/ubuntu focal/main amd64 libisl22 amd64 0.22.1-1 [592 kB] #6 57.87 Get:49 http://archive.ubuntu.com/ubuntu focal/main amd64 libmpfr6 amd64 4.0.2-1 [240 kB] #6 57.91 Get:50 http://archive.ubuntu.com/ubuntu focal/main amd64 libmpc3 amd64 1.1.0-1 [40.8 kB] #6 57.92 Get:51 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 cpp-9 amd64 9.4.0-1ubuntu1~20.04.2 [7502 kB] #6 59.27 Get:52 http://archive.ubuntu.com/ubuntu focal/main amd64 cpp amd64 4:9.3.0-1ubuntu2 [27.6 kB] #6 59.27 Get:53 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcc1-0 amd64 10.5.0-1ubuntu1~20.04 [48.8 kB] #6 59.36 Get:54 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgomp1 amd64 10.5.0-1ubuntu1~20.04 [102 kB] #6 59.54 Get:55 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libitm1 amd64 10.5.0-1ubuntu1~20.04 [26.2 kB] #6 59.55 Get:56 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libatomic1 amd64 10.5.0-1ubuntu1~20.04 [9284 B] #6 59.55 Get:57 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libasan5 amd64 9.4.0-1ubuntu1~20.04.2 [2752 kB] #6 60.09 Get:58 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 liblsan0 amd64 10.5.0-1ubuntu1~20.04 [835 kB] #6 60.24 Get:59 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libtsan0 amd64 10.5.0-1ubuntu1~20.04 [2016 kB] #6 60.61 Get:60 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libubsan1 amd64 10.5.0-1ubuntu1~20.04 [785 kB] #6 60.76 Get:61 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libquadmath0 amd64 10.5.0-1ubuntu1~20.04 [146 kB] #6 60.78 Get:62 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgcc-9-dev amd64 9.4.0-1ubuntu1~20.04.2 [2359 kB] #6 61.28 Get:63 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 gcc-9 amd64 9.4.0-1ubuntu1~20.04.2 [8276 kB] #6 62.82 Get:64 http://archive.ubuntu.com/ubuntu focal/main amd64 gcc amd64 4:9.3.0-1ubuntu2 [5208 B] #6 62.91 Get:65 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libstdc++-9-dev amd64 9.4.0-1ubuntu1~20.04.2 [1722 kB] #6 63.39 Get:66 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 g++-9 amd64 9.4.0-1ubuntu1~20.04.2 [8421 kB] #6 65.04 Get:67 http://archive.ubuntu.com/ubuntu focal/main amd64 g++ amd64 4:9.3.0-1ubuntu2 [1604 B] #6 65.06 Get:68 http://archive.ubuntu.com/ubuntu focal/main amd64 make amd64 4.2.1-1.2 [162 kB] #6 65.07 Get:69 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdpkg-perl all 1.19.7ubuntu3.2 [231 kB] #6 65.16 Get:70 http://archive.ubuntu.com/ubuntu focal/main amd64 patch amd64 2.7.6-6 [105 kB] #6 65.20 Get:71 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 dpkg-dev all 1.19.7ubuntu3.2 [679 kB] #6 65.32 Get:72 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 build-essential amd64 12.8ubuntu1.1 [4664 B] #6 65.32 Get:73 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 cmake-data all 3.16.3-1ubuntu1.20.04.1 [1613 kB] #6 65.61 Get:74 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libarchive13 amd64 3.4.0-2ubuntu1.2 [327 kB] #6 65.66 Get:75 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libbrotli1 amd64 1.0.7-6ubuntu0.1 [267 kB] #6 65.80 Get:76 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libroken18-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [42.5 kB] #6 65.94 Get:77 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libasn1-8-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [181 kB] #6 66.25 Get:78 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libheimbase1-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [30.4 kB] #6 66.29 Get:79 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libhcrypto4-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [88.1 kB] #6 66.55 Get:80 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwind0-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [47.7 kB] #6 66.71 Get:81 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libhx509-5-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [107 kB] #6 67.13 Get:82 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkrb5-26-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [207 kB] #6 67.89 Get:83 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libheimntlm0-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [15.1 kB] #6 67.92 Get:84 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgssapi3-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [96.5 kB] #6 68.05 Get:85 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsasl2-modules-db amd64 2.1.27+dfsg-2ubuntu0.1 [14.7 kB] #6 68.06 Get:86 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsasl2-2 amd64 2.1.27+dfsg-2ubuntu0.1 [49.3 kB] #6 68.29 Get:87 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libldap-common all 2.4.49+dfsg-2ubuntu1.10 [16.5 kB] #6 68.38 Get:88 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libldap-2.4-2 amd64 2.4.49+dfsg-2ubuntu1.10 [155 kB] #6 68.50 Get:89 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnghttp2-14 amd64 1.40.0-1ubuntu0.3 [79.9 kB] #6 68.53 Get:90 http://archive.ubuntu.com/ubuntu focal/main amd64 librtmp1 amd64 2.4+20151223.gitfa8646d.1-2build1 [54.9 kB] #6 68.54 Get:91 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libssh-4 amd64 0.9.3-2ubuntu2.5 [171 kB] #6 68.57 Get:92 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcurl4 amd64 7.68.0-1ubuntu2.22 [235 kB] #6 68.62 Get:93 http://archive.ubuntu.com/ubuntu focal/main amd64 libjsoncpp1 amd64 1.7.4-3.1ubuntu2 [75.6 kB] #6 68.63 Get:94 http://archive.ubuntu.com/ubuntu focal/main amd64 librhash0 amd64 1.3.9-1 [113 kB] #6 68.65 Get:95 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 cmake amd64 3.16.3-1ubuntu1.20.04.1 [3668 kB] #6 69.31 Get:96 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 curl amd64 7.68.0-1ubuntu2.22 [161 kB] #6 69.33 Get:97 http://archive.ubuntu.com/ubuntu focal/main amd64 fonts-dejavu-core all 2.37-1 [1041 kB] #6 69.60 Get:98 http://archive.ubuntu.com/ubuntu focal/main amd64 fontconfig-config all 2.13.1-2ubuntu3 [28.8 kB] #6 69.70 Get:99 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcurl3-gnutls amd64 7.68.0-1ubuntu2.22 [233 kB] #6 69.83 Get:100 http://archive.ubuntu.com/ubuntu focal/main amd64 liberror-perl all 0.17029-1 [26.5 kB] #6 69.84 Get:101 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 git-man all 1:2.25.1-1ubuntu3.11 [887 kB] #6 70.00 Get:102 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 git amd64 1:2.25.1-1ubuntu3.11 [4605 kB] #6 70.90 Get:103 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libfreetype6 amd64 2.10.1-2ubuntu0.3 [341 kB] #6 70.96 Get:104 http://archive.ubuntu.com/ubuntu focal/main amd64 libfontconfig1 amd64 2.13.1-2ubuntu3 [114 kB] #6 70.97 Get:105 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libjpeg-turbo8 amd64 2.0.3-0ubuntu1.20.04.3 [118 kB] #6 71.00 Get:106 http://archive.ubuntu.com/ubuntu focal/main amd64 libjpeg8 amd64 8c-2ubuntu8 [2194 B] #6 71.00 Get:107 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libjbig0 amd64 2.1-3.1ubuntu0.20.04.1 [27.3 kB] #6 71.00 Get:108 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwebp6 amd64 0.6.1-2ubuntu0.20.04.3 [185 kB] #6 71.14 Get:109 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libtiff5 amd64 4.1.0+git191117-2ubuntu0.20.04.12 [164 kB] #6 71.36 Get:110 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxpm4 amd64 1:3.5.12-1ubuntu0.20.04.2 [34.9 kB] #6 71.36 Get:111 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgd3 amd64 2.2.5-5.2ubuntu2.1 [118 kB] #6 71.39 Get:112 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 nginx-common all 1.18.0-0ubuntu1.4 [37.7 kB] #6 71.40 Get:113 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnginx-mod-http-image-filter amd64 1.18.0-0ubuntu1.4 [14.8 kB] #6 71.41 Get:114 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxslt1.1 amd64 1.1.34-4ubuntu0.20.04.1 [151 kB] #6 71.43 Get:115 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnginx-mod-http-xslt-filter amd64 1.18.0-0ubuntu1.4 [13.0 kB] #6 71.44 Get:116 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnginx-mod-mail amd64 1.18.0-0ubuntu1.4 [42.9 kB] #6 71.47 Get:117 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnginx-mod-stream amd64 1.18.0-0ubuntu1.4 [67.4 kB] #6 71.49 Get:118 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 nginx-core amd64 1.18.0-0ubuntu1.4 [425 kB] #6 71.58 Get:119 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 nginx all 1.18.0-0ubuntu1.4 [3620 B] #6 71.74 debconf: delaying package configuration, since apt-utils is not installed #6 71.80 Fetched 82.1 MB in 56s (1459 kB/s) #6 71.85 Selecting previously unselected package perl-modules-5.30. #6 71.85 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 4124 files and directories currently installed.) #6 71.86 Preparing to unpack .../000-perl-modules-5.30_5.30.0-9ubuntu0.5_all.deb ... #6 71.87 Unpacking perl-modules-5.30 (5.30.0-9ubuntu0.5) ... #6 72.25 Selecting previously unselected package libgdbm6:amd64. #6 72.25 Preparing to unpack .../001-libgdbm6_1.18.1-5_amd64.deb ... #6 72.26 Unpacking libgdbm6:amd64 (1.18.1-5) ... #6 72.33 Selecting previously unselected package libgdbm-compat4:amd64. #6 72.33 Preparing to unpack .../002-libgdbm-compat4_1.18.1-5_amd64.deb ... #6 72.34 Unpacking libgdbm-compat4:amd64 (1.18.1-5) ... #6 72.41 Selecting previously unselected package libperl5.30:amd64. #6 72.41 Preparing to unpack .../003-libperl5.30_5.30.0-9ubuntu0.5_amd64.deb ... #6 72.42 Unpacking libperl5.30:amd64 (5.30.0-9ubuntu0.5) ... #6 72.92 Selecting previously unselected package perl. #6 72.93 Preparing to unpack .../004-perl_5.30.0-9ubuntu0.5_amd64.deb ... #6 72.95 Unpacking perl (5.30.0-9ubuntu0.5) ... #6 73.07 Selecting previously unselected package libssl1.1:amd64. #6 73.08 Preparing to unpack .../005-libssl1.1_1.1.1f-1ubuntu2.22_amd64.deb ... #6 73.08 Unpacking libssl1.1:amd64 (1.1.1f-1ubuntu2.22) ... #6 73.27 Selecting previously unselected package openssl. #6 73.28 Preparing to unpack .../006-openssl_1.1.1f-1ubuntu2.22_amd64.deb ... #6 73.28 Unpacking openssl (1.1.1f-1ubuntu2.22) ... #6 73.43 Selecting previously unselected package ca-certificates. #6 73.43 Preparing to unpack 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75.93 Preparing to unpack .../022-libkrb5support0_1.17-6ubuntu4.4_amd64.deb ... #6 75.93 Unpacking libkrb5support0:amd64 (1.17-6ubuntu4.4) ... #6 76.00 Selecting previously unselected package libk5crypto3:amd64. #6 76.01 Preparing to unpack .../023-libk5crypto3_1.17-6ubuntu4.4_amd64.deb ... #6 76.01 Unpacking libk5crypto3:amd64 (1.17-6ubuntu4.4) ... #6 76.10 Selecting previously unselected package libkeyutils1:amd64. #6 76.10 Preparing to unpack .../024-libkeyutils1_1.6-6ubuntu1.1_amd64.deb ... #6 76.11 Unpacking libkeyutils1:amd64 (1.6-6ubuntu1.1) ... #6 76.17 Selecting previously unselected package libkrb5-3:amd64. #6 76.18 Preparing to unpack .../025-libkrb5-3_1.17-6ubuntu4.4_amd64.deb ... #6 76.18 Unpacking libkrb5-3:amd64 (1.17-6ubuntu4.4) ... #6 76.31 Selecting previously unselected package libgssapi-krb5-2:amd64. #6 76.31 Preparing to unpack .../026-libgssapi-krb5-2_1.17-6ubuntu4.4_amd64.deb ... #6 76.32 Unpacking libgssapi-krb5-2:amd64 (1.17-6ubuntu4.4) ... #6 76.41 Selecting previously unselected package libpng16-16:amd64. #6 76.41 Preparing to unpack .../027-libpng16-16_1.6.37-2_amd64.deb ... #6 76.42 Unpacking libpng16-16:amd64 (1.6.37-2) ... #6 76.52 Selecting previously unselected package libpsl5:amd64. #6 76.52 Preparing to unpack .../028-libpsl5_0.21.0-1ubuntu1_amd64.deb ... #6 76.53 Unpacking libpsl5:amd64 (0.21.0-1ubuntu1) ... #6 76.61 Selecting previously unselected package libuv1:amd64. #6 76.61 Preparing to unpack .../029-libuv1_1.34.2-1ubuntu1.5_amd64.deb ... #6 76.62 Unpacking libuv1:amd64 (1.34.2-1ubuntu1.5) ... #6 76.70 Selecting previously unselected package libxau6:amd64. #6 76.71 Preparing to unpack .../030-libxau6_1%3a1.0.9-0ubuntu1_amd64.deb ... #6 76.71 Unpacking libxau6:amd64 (1:1.0.9-0ubuntu1) ... #6 76.78 Selecting previously unselected package libxdmcp6:amd64. #6 76.79 Preparing to unpack .../031-libxdmcp6_1%3a1.1.3-0ubuntu1_amd64.deb ... #6 76.79 Unpacking libxdmcp6:amd64 (1:1.1.3-0ubuntu1) ... #6 76.86 Selecting previously unselected package libxcb1:amd64. #6 76.87 Preparing to unpack .../032-libxcb1_1.14-2_amd64.deb ... #6 76.87 Unpacking libxcb1:amd64 (1.14-2) ... #6 76.95 Selecting previously unselected package libx11-data. #6 76.95 Preparing to unpack .../033-libx11-data_2%3a1.6.9-2ubuntu1.6_all.deb ... #6 76.96 Unpacking libx11-data (2:1.6.9-2ubuntu1.6) ... #6 77.08 Selecting previously unselected package libx11-6:amd64. #6 77.08 Preparing to unpack .../034-libx11-6_2%3a1.6.9-2ubuntu1.6_amd64.deb ... #6 77.09 Unpacking libx11-6:amd64 (2:1.6.9-2ubuntu1.6) ... #6 77.22 Selecting previously unselected package wget. #6 77.22 Preparing to unpack .../035-wget_1.20.3-1ubuntu2_amd64.deb ... #6 77.23 Unpacking wget (1.20.3-1ubuntu2) ... #6 77.33 Selecting previously unselected package binutils-common:amd64. #6 77.33 Preparing to unpack .../036-binutils-common_2.34-6ubuntu1.9_amd64.deb ... #6 77.34 Unpacking binutils-common:amd64 (2.34-6ubuntu1.9) ... #6 77.44 Selecting previously unselected package libbinutils:amd64. #6 77.45 Preparing to unpack .../037-libbinutils_2.34-6ubuntu1.9_amd64.deb ... #6 77.45 Unpacking libbinutils:amd64 (2.34-6ubuntu1.9) ... #6 77.59 Selecting previously unselected package libctf-nobfd0:amd64. #6 77.59 Preparing to unpack .../038-libctf-nobfd0_2.34-6ubuntu1.9_amd64.deb ... #6 77.60 Unpacking libctf-nobfd0:amd64 (2.34-6ubuntu1.9) ... #6 77.68 Selecting previously unselected package libctf0:amd64. #6 77.68 Preparing to unpack .../039-libctf0_2.34-6ubuntu1.9_amd64.deb ... #6 77.69 Unpacking libctf0:amd64 (2.34-6ubuntu1.9) ... #6 77.76 Selecting previously unselected package binutils-x86-64-linux-gnu. #6 77.76 Preparing to unpack .../040-binutils-x86-64-linux-gnu_2.34-6ubuntu1.9_amd64.deb ... #6 77.77 Unpacking binutils-x86-64-linux-gnu (2.34-6ubuntu1.9) ... #6 78.04 Selecting previously unselected package binutils. #6 78.05 Preparing to unpack .../041-binutils_2.34-6ubuntu1.9_amd64.deb ... #6 78.05 Unpacking binutils (2.34-6ubuntu1.9) ... #6 78.12 Selecting previously unselected package libc-dev-bin. #6 78.12 Preparing to unpack .../042-libc-dev-bin_2.31-0ubuntu9.15_amd64.deb ... #6 78.13 Unpacking libc-dev-bin (2.31-0ubuntu9.15) ... #6 78.20 Selecting previously unselected package linux-libc-dev:amd64. #6 78.21 Preparing to unpack .../043-linux-libc-dev_5.4.0-182.202_amd64.deb ... #6 78.21 Unpacking linux-libc-dev:amd64 (5.4.0-182.202) ... #6 78.41 Selecting previously unselected package libcrypt-dev:amd64. #6 78.42 Preparing to unpack .../044-libcrypt-dev_1%3a4.4.10-10ubuntu4_amd64.deb ... #6 78.42 Unpacking libcrypt-dev:amd64 (1:4.4.10-10ubuntu4) ... #6 78.50 Selecting previously unselected package libc6-dev:amd64. #6 78.51 Preparing to unpack .../045-libc6-dev_2.31-0ubuntu9.15_amd64.deb ... #6 78.51 Unpacking libc6-dev:amd64 (2.31-0ubuntu9.15) ... #6 78.88 Selecting previously unselected package gcc-9-base:amd64. #6 78.88 Preparing to unpack .../046-gcc-9-base_9.4.0-1ubuntu1~20.04.2_amd64.deb ... #6 78.89 Unpacking gcc-9-base:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 78.97 Selecting previously unselected package libisl22:amd64. #6 78.97 Preparing to unpack .../047-libisl22_0.22.1-1_amd64.deb ... #6 78.98 Unpacking libisl22:amd64 (0.22.1-1) ... #6 79.12 Selecting previously unselected package libmpfr6:amd64. #6 79.12 Preparing to unpack .../048-libmpfr6_4.0.2-1_amd64.deb ... #6 79.13 Unpacking libmpfr6:amd64 (4.0.2-1) ... #6 79.24 Selecting previously unselected package libmpc3:amd64. #6 79.24 Preparing to unpack .../049-libmpc3_1.1.0-1_amd64.deb ... #6 79.25 Unpacking libmpc3:amd64 (1.1.0-1) ... #6 79.32 Selecting previously unselected package cpp-9. #6 79.32 Preparing to unpack .../050-cpp-9_9.4.0-1ubuntu1~20.04.2_amd64.deb ... #6 79.33 Unpacking cpp-9 (9.4.0-1ubuntu1~20.04.2) ... #6 80.06 Selecting previously unselected package cpp. #6 80.06 Preparing to unpack .../051-cpp_4%3a9.3.0-1ubuntu2_amd64.deb ... #6 80.07 Unpacking cpp (4:9.3.0-1ubuntu2) ... #6 80.14 Selecting previously unselected package libcc1-0:amd64. #6 80.14 Preparing to unpack .../052-libcc1-0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.15 Unpacking libcc1-0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.23 Selecting previously unselected package libgomp1:amd64. #6 80.23 Preparing to unpack .../053-libgomp1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.24 Unpacking libgomp1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.34 Selecting previously unselected package libitm1:amd64. #6 80.34 Preparing to unpack .../054-libitm1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.35 Unpacking libitm1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.43 Selecting previously unselected package libatomic1:amd64. #6 80.43 Preparing to unpack .../055-libatomic1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.44 Unpacking libatomic1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.51 Selecting previously unselected package libasan5:amd64. #6 80.51 Preparing to unpack .../056-libasan5_9.4.0-1ubuntu1~20.04.2_amd64.deb ... #6 80.52 Unpacking libasan5:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 80.87 Selecting previously unselected package liblsan0:amd64. #6 80.88 Preparing to unpack .../057-liblsan0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.88 Unpacking liblsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.05 Selecting previously unselected package libtsan0:amd64. #6 81.05 Preparing to unpack .../058-libtsan0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 81.06 Unpacking libtsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.35 Selecting previously unselected package libubsan1:amd64. #6 81.35 Preparing to unpack .../059-libubsan1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 81.36 Unpacking libubsan1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.52 Selecting previously unselected package libquadmath0:amd64. #6 81.52 Preparing to unpack .../060-libquadmath0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 81.53 Unpacking libquadmath0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.62 Selecting previously unselected package libgcc-9-dev:amd64. #6 81.62 Preparing to unpack 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Selecting previously unselected package cmake-data. #6 84.52 Preparing to unpack .../072-cmake-data_3.16.3-1ubuntu1.20.04.1_all.deb ... #6 84.54 Unpacking cmake-data (3.16.3-1ubuntu1.20.04.1) ... #6 84.99 Selecting previously unselected package libarchive13:amd64. #6 84.99 Preparing to unpack .../073-libarchive13_3.4.0-2ubuntu1.2_amd64.deb ... #6 85.00 Unpacking libarchive13:amd64 (3.4.0-2ubuntu1.2) ... #6 85.12 Selecting previously unselected package libbrotli1:amd64. #6 85.12 Preparing to unpack .../074-libbrotli1_1.0.7-6ubuntu0.1_amd64.deb ... #6 85.13 Unpacking libbrotli1:amd64 (1.0.7-6ubuntu0.1) ... #6 85.24 Selecting previously unselected package libroken18-heimdal:amd64. #6 85.25 Preparing to unpack .../075-libroken18-heimdal_7.7.0+dfsg-1ubuntu1.4_amd64.deb ... #6 85.25 Unpacking libroken18-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 85.33 Selecting previously unselected package libasn1-8-heimdal:amd64. #6 85.33 Preparing to unpack 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.../084-libsasl2-modules-db_2.1.27+dfsg-2ubuntu0.1_amd64.deb ... #6 86.17 Unpacking libsasl2-modules-db:amd64 (2.1.27+dfsg-2ubuntu0.1) ... #6 86.25 Selecting previously unselected package libsasl2-2:amd64. #6 86.25 Preparing to unpack .../085-libsasl2-2_2.1.27+dfsg-2ubuntu0.1_amd64.deb ... #6 86.26 Unpacking libsasl2-2:amd64 (2.1.27+dfsg-2ubuntu0.1) ... #6 86.33 Selecting previously unselected package libldap-common. #6 86.34 Preparing to unpack .../086-libldap-common_2.4.49+dfsg-2ubuntu1.10_all.deb ... #6 86.34 Unpacking libldap-common (2.4.49+dfsg-2ubuntu1.10) ... #6 86.42 Selecting previously unselected package libldap-2.4-2:amd64. #6 86.42 Preparing to unpack .../087-libldap-2.4-2_2.4.49+dfsg-2ubuntu1.10_amd64.deb ... #6 86.43 Unpacking libldap-2.4-2:amd64 (2.4.49+dfsg-2ubuntu1.10) ... #6 86.53 Selecting previously unselected package libnghttp2-14:amd64. #6 86.54 Preparing to unpack .../088-libnghttp2-14_1.40.0-1ubuntu0.3_amd64.deb ... #6 86.54 Unpacking libnghttp2-14:amd64 (1.40.0-1ubuntu0.3) ... #6 86.63 Selecting previously unselected package librtmp1:amd64. #6 86.64 Preparing to unpack .../089-librtmp1_2.4+20151223.gitfa8646d.1-2build1_amd64.deb ... #6 86.64 Unpacking librtmp1:amd64 (2.4+20151223.gitfa8646d.1-2build1) ... #6 86.73 Selecting previously unselected package libssh-4:amd64. #6 86.74 Preparing to unpack .../090-libssh-4_0.9.3-2ubuntu2.5_amd64.deb ... #6 86.74 Unpacking libssh-4:amd64 (0.9.3-2ubuntu2.5) ... #6 86.85 Selecting previously unselected package libcurl4:amd64. #6 86.85 Preparing to unpack .../091-libcurl4_7.68.0-1ubuntu2.22_amd64.deb ... #6 86.86 Unpacking libcurl4:amd64 (7.68.0-1ubuntu2.22) ... #6 86.96 Selecting previously unselected package libjsoncpp1:amd64. #6 86.97 Preparing to unpack .../092-libjsoncpp1_1.7.4-3.1ubuntu2_amd64.deb ... #6 86.97 Unpacking libjsoncpp1:amd64 (1.7.4-3.1ubuntu2) ... #6 87.07 Selecting previously unselected package librhash0:amd64. #6 87.07 Preparing to unpack .../093-librhash0_1.3.9-1_amd64.deb ... #6 87.08 Unpacking librhash0:amd64 (1.3.9-1) ... #6 87.16 Selecting previously unselected package cmake. #6 87.16 Preparing to unpack .../094-cmake_3.16.3-1ubuntu1.20.04.1_amd64.deb ... #6 87.17 Unpacking cmake (3.16.3-1ubuntu1.20.04.1) ... #6 87.60 Selecting previously unselected package curl. #6 87.60 Preparing to unpack .../095-curl_7.68.0-1ubuntu2.22_amd64.deb ... #6 87.61 Unpacking curl (7.68.0-1ubuntu2.22) ... #6 87.71 Selecting previously unselected package fonts-dejavu-core. #6 87.71 Preparing to unpack .../096-fonts-dejavu-core_2.37-1_all.deb ... #6 87.72 Unpacking fonts-dejavu-core (2.37-1) ... #6 87.90 Selecting previously unselected package fontconfig-config. #6 87.91 Preparing to unpack .../097-fontconfig-config_2.13.1-2ubuntu3_all.deb ... #6 87.92 Unpacking fontconfig-config (2.13.1-2ubuntu3) ... #6 88.00 Selecting previously unselected package libcurl3-gnutls:amd64. #6 88.00 Preparing to unpack .../098-libcurl3-gnutls_7.68.0-1ubuntu2.22_amd64.deb ... #6 88.01 Unpacking libcurl3-gnutls:amd64 (7.68.0-1ubuntu2.22) ... #6 88.11 Selecting previously unselected package liberror-perl. #6 88.12 Preparing to unpack .../099-liberror-perl_0.17029-1_all.deb ... #6 88.12 Unpacking liberror-perl (0.17029-1) ... #6 88.19 Selecting previously unselected package git-man. #6 88.19 Preparing to unpack .../100-git-man_1%3a2.25.1-1ubuntu3.11_all.deb ... #6 88.20 Unpacking git-man (1:2.25.1-1ubuntu3.11) ... #6 88.37 Selecting previously unselected package git. #6 88.38 Preparing to unpack .../101-git_1%3a2.25.1-1ubuntu3.11_amd64.deb ... #6 88.43 Unpacking git (1:2.25.1-1ubuntu3.11) ... #6 88.98 Selecting previously unselected package libfreetype6:amd64. #6 88.99 Preparing to unpack .../102-libfreetype6_2.10.1-2ubuntu0.3_amd64.deb ... #6 89.00 Unpacking libfreetype6:amd64 (2.10.1-2ubuntu0.3) ... #6 89.12 Selecting previously unselected package libfontconfig1:amd64. #6 89.12 Preparing to unpack .../103-libfontconfig1_2.13.1-2ubuntu3_amd64.deb ... #6 89.13 Unpacking libfontconfig1:amd64 (2.13.1-2ubuntu3) ... #6 89.23 Selecting previously unselected package libjpeg-turbo8:amd64. #6 89.23 Preparing to unpack .../104-libjpeg-turbo8_2.0.3-0ubuntu1.20.04.3_amd64.deb ... #6 89.24 Unpacking libjpeg-turbo8:amd64 (2.0.3-0ubuntu1.20.04.3) ... #6 89.32 Selecting previously unselected package libjpeg8:amd64. #6 89.33 Preparing to unpack .../105-libjpeg8_8c-2ubuntu8_amd64.deb ... #6 89.33 Unpacking libjpeg8:amd64 (8c-2ubuntu8) ... #6 89.40 Selecting previously unselected package libjbig0:amd64. #6 89.40 Preparing to unpack .../106-libjbig0_2.1-3.1ubuntu0.20.04.1_amd64.deb ... #6 89.41 Unpacking libjbig0:amd64 (2.1-3.1ubuntu0.20.04.1) ... #6 89.49 Selecting previously unselected package libwebp6:amd64. #6 89.49 Preparing to unpack .../107-libwebp6_0.6.1-2ubuntu0.20.04.3_amd64.deb ... #6 89.50 Unpacking libwebp6:amd64 (0.6.1-2ubuntu0.20.04.3) ... #6 89.60 Selecting previously unselected package libtiff5:amd64. #6 89.60 Preparing to unpack .../108-libtiff5_4.1.0+git191117-2ubuntu0.20.04.12_amd64.deb ... #6 89.61 Unpacking libtiff5:amd64 (4.1.0+git191117-2ubuntu0.20.04.12) ... #6 89.71 Selecting previously unselected package libxpm4:amd64. #6 89.71 Preparing to unpack .../109-libxpm4_1%3a3.5.12-1ubuntu0.20.04.2_amd64.deb ... #6 89.72 Unpacking libxpm4:amd64 (1:3.5.12-1ubuntu0.20.04.2) ... #6 89.80 Selecting previously unselected package libgd3:amd64. #6 89.80 Preparing to unpack .../110-libgd3_2.2.5-5.2ubuntu2.1_amd64.deb ... #6 89.81 Unpacking libgd3:amd64 (2.2.5-5.2ubuntu2.1) ... #6 89.92 Selecting previously unselected package nginx-common. #6 89.92 Preparing to unpack .../111-nginx-common_1.18.0-0ubuntu1.4_all.deb ... #6 89.99 Unpacking nginx-common (1.18.0-0ubuntu1.4) ... #6 90.08 Selecting previously unselected package libnginx-mod-http-image-filter. #6 90.08 Preparing to unpack .../112-libnginx-mod-http-image-filter_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.09 Unpacking libnginx-mod-http-image-filter (1.18.0-0ubuntu1.4) ... #6 90.17 Selecting previously unselected package libxslt1.1:amd64. #6 90.17 Preparing to unpack .../113-libxslt1.1_1.1.34-4ubuntu0.20.04.1_amd64.deb ... #6 90.18 Unpacking libxslt1.1:amd64 (1.1.34-4ubuntu0.20.04.1) ... #6 90.28 Selecting previously unselected package libnginx-mod-http-xslt-filter. #6 90.28 Preparing to unpack .../114-libnginx-mod-http-xslt-filter_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.29 Unpacking libnginx-mod-http-xslt-filter (1.18.0-0ubuntu1.4) ... #6 90.37 Selecting previously unselected package libnginx-mod-mail. #6 90.37 Preparing to unpack .../115-libnginx-mod-mail_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.38 Unpacking libnginx-mod-mail (1.18.0-0ubuntu1.4) ... #6 90.46 Selecting previously unselected package libnginx-mod-stream. #6 90.46 Preparing to unpack .../116-libnginx-mod-stream_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.47 Unpacking libnginx-mod-stream (1.18.0-0ubuntu1.4) ... #6 90.56 Selecting previously unselected package nginx-core. #6 90.56 Preparing to unpack .../117-nginx-core_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.57 Unpacking nginx-core (1.18.0-0ubuntu1.4) ... #6 90.69 Selecting previously unselected package nginx. #6 90.70 Preparing to unpack .../118-nginx_1.18.0-0ubuntu1.4_all.deb ... #6 90.71 Unpacking nginx (1.18.0-0ubuntu1.4) ... #6 90.79 Setting up libexpat1:amd64 (2.2.9-1ubuntu0.6) ... #6 90.81 Setting up libxau6:amd64 (1:1.0.9-0ubuntu1) ... #6 90.84 Setting up libkeyutils1:amd64 (1.6-6ubuntu1.1) ... #6 90.86 Setting up libpsl5:amd64 (0.21.0-1ubuntu1) ... #6 90.88 Setting up perl-modules-5.30 (5.30.0-9ubuntu0.5) ... #6 90.90 Setting up libssl1.1:amd64 (1.1.1f-1ubuntu2.22) ... #6 91.02 Setting up libbrotli1:amd64 (1.0.7-6ubuntu0.1) ... #6 91.04 Setting up libsqlite3-0:amd64 (3.31.1-4ubuntu0.6) ... #6 91.06 Setting up binutils-common:amd64 (2.34-6ubuntu1.9) ... #6 91.08 Setting up libnghttp2-14:amd64 (1.40.0-1ubuntu0.3) ... #6 91.11 Setting up linux-libc-dev:amd64 (5.4.0-182.202) ... #6 91.13 Setting up libctf-nobfd0:amd64 (2.34-6ubuntu1.9) ... #6 91.15 Setting up nginx-common (1.18.0-0ubuntu1.4) ... #6 91.69 Setting up libgomp1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 91.71 Setting up libldap-common (2.4.49+dfsg-2ubuntu1.10) ... #6 91.74 Setting up libjbig0:amd64 (2.1-3.1ubuntu0.20.04.1) ... #6 91.76 Setting up libcap2:amd64 (1:2.32-1ubuntu0.1) ... #6 91.79 Setting up libkrb5support0:amd64 (1.17-6ubuntu4.4) ... #6 91.81 Setting up libsasl2-modules-db:amd64 (2.1.27+dfsg-2ubuntu0.1) ... #6 91.83 Setting up tzdata (2024a-0ubuntu0.20.04) ... #6 91.96 #6 91.96 Current default time zone: 'Etc/UTC' #6 91.96 Local time is now: Thu May 16 20:22:03 UTC 2024. #6 91.96 Universal Time is now: Thu May 16 20:22:03 UTC 2024. #6 91.96 Run 'dpkg-reconfigure tzdata' if you wish to change it. #6 91.96 #6 91.99 Setting up libcap2-bin (1:2.32-1ubuntu0.1) ... #6 92.01 Setting up libuv1:amd64 (1.34.2-1ubuntu1.5) ... #6 92.03 Setting up libx11-data (2:1.6.9-2ubuntu1.6) ... #6 92.05 Setting up make (4.2.1-1.2) ... #6 92.08 Setting up libmpfr6:amd64 (4.0.2-1) ... #6 92.10 Setting up librtmp1:amd64 (2.4+20151223.gitfa8646d.1-2build1) ... #6 92.12 Setting up xz-utils (5.2.4-1ubuntu1.1) ... #6 92.14 update-alternatives: using /usr/bin/xz to provide /usr/bin/lzma (lzma) in auto mode #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzma.1.gz because associated file /usr/share/man/man1/xz.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/unlzma.1.gz because associated file /usr/share/man/man1/unxz.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzcat.1.gz because associated file /usr/share/man/man1/xzcat.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzmore.1.gz because associated file /usr/share/man/man1/xzmore.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzless.1.gz because associated file /usr/share/man/man1/xzless.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzdiff.1.gz because associated file /usr/share/man/man1/xzdiff.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzcmp.1.gz because associated file /usr/share/man/man1/xzcmp.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzgrep.1.gz because associated file /usr/share/man/man1/xzgrep.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzegrep.1.gz because associated file /usr/share/man/man1/xzegrep.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzfgrep.1.gz because associated file /usr/share/man/man1/xzfgrep.1.gz (of link group lzma) doesn't exist #6 92.16 Setting up libquadmath0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.18 Setting up libpng16-16:amd64 (1.6.37-2) ... #6 92.20 Setting up libmpc3:amd64 (1.1.0-1) ... #6 92.22 Setting up libmnl0:amd64 (1.0.4-2) ... #6 92.24 Setting up libatomic1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.26 Setting up patch (2.7.6-6) ... #6 92.28 Setting up libwebp6:amd64 (0.6.1-2ubuntu0.20.04.3) ... #6 92.30 Setting up fonts-dejavu-core (2.37-1) ... #6 92.40 Setting up ucf (3.0038+nmu1) ... #6 92.53 Setting up libk5crypto3:amd64 (1.17-6ubuntu4.4) ... #6 92.55 Setting up libjpeg-turbo8:amd64 (2.0.3-0ubuntu1.20.04.3) ... #6 92.57 Setting up libxtables12:amd64 (1.8.4-3ubuntu2.1) ... #6 92.59 Setting up libsasl2-2:amd64 (2.1.27+dfsg-2ubuntu0.1) ... #6 92.61 Setting up libroken18-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 92.64 Setting up libubsan1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.66 Setting up librhash0:amd64 (1.3.9-1) ... #6 92.68 Setting up libcrypt-dev:amd64 (1:4.4.10-10ubuntu4) ... #6 92.70 Setting up git-man (1:2.25.1-1ubuntu3.11) ... #6 92.72 Setting up libisl22:amd64 (0.22.1-1) ... #6 92.74 Setting up cmake-data (3.16.3-1ubuntu1.20.04.1) ... #6 92.77 Setting up libkrb5-3:amd64 (1.17-6ubuntu4.4) ... #6 92.79 Setting up libbinutils:amd64 (2.34-6ubuntu1.9) ... #6 92.81 Setting up libc-dev-bin (2.31-0ubuntu9.15) ... #6 92.83 Setting up openssl (1.1.1f-1ubuntu2.22) ... #6 92.86 Setting up libbsd0:amd64 (0.10.0-1) ... #6 92.88 Setting up libelf1:amd64 (0.176-1.1ubuntu0.1) ... #6 92.91 Setting up libcc1-0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.93 Setting up liblsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.95 Setting up libitm1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.97 Setting up libjsoncpp1:amd64 (1.7.4-3.1ubuntu2) ... #6 92.99 Setting up libheimbase1-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 93.01 Setting up libgdbm6:amd64 (1.18.1-5) ... #6 93.03 Setting up gcc-9-base:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 93.05 Setting up libtsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 93.07 Setting up libctf0:amd64 (2.34-6ubuntu1.9) ... #6 93.09 Setting up libjpeg8:amd64 (8c-2ubuntu8) ... #6 93.11 Setting up libnginx-mod-mail (1.18.0-0ubuntu1.4) ... #6 93.16 Setting up libxdmcp6:amd64 (1:1.1.3-0ubuntu1) ... #6 93.18 Setting up libxcb1:amd64 (1.14-2) ... #6 93.20 Setting up wget (1.20.3-1ubuntu2) ... #6 93.22 Setting up fontconfig-config (2.13.1-2ubuntu3) ... #6 93.55 Setting up iproute2 (5.5.0-1ubuntu1) ... #6 93.75 Setting up libicu66:amd64 (66.1-2ubuntu2.1) ... #6 93.77 Setting up libasn1-8-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 93.79 Setting up libnginx-mod-stream (1.18.0-0ubuntu1.4) ... #6 93.84 Setting up libhcrypto4-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 93.86 Setting up ca-certificates (20230311ubuntu0.20.04.1) ... #6 95.13 Updating certificates in /etc/ssl/certs... #6 96.88 137 added, 0 removed; done. #6 96.95 Setting up libasan5:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 96.98 Setting up libwind0-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.00 Setting up libfreetype6:amd64 (2.10.1-2ubuntu0.3) ... #6 97.02 Setting up libgssapi-krb5-2:amd64 (1.17-6ubuntu4.4) ... #6 97.04 Setting up libgdbm-compat4:amd64 (1.18.1-5) ... #6 97.06 Setting up libssh-4:amd64 (0.9.3-2ubuntu2.5) ... #6 97.08 Setting up cpp-9 (9.4.0-1ubuntu1~20.04.2) ... #6 97.10 Setting up libperl5.30:amd64 (5.30.0-9ubuntu0.5) ... #6 97.12 Setting up libc6-dev:amd64 (2.31-0ubuntu9.15) ... #6 97.14 Setting up libx11-6:amd64 (2:1.6.9-2ubuntu1.6) ... #6 97.16 Setting up libtiff5:amd64 (4.1.0+git191117-2ubuntu0.20.04.12) ... #6 97.18 Setting up libfontconfig1:amd64 (2.13.1-2ubuntu3) ... #6 97.20 Setting up libxml2:amd64 (2.9.10+dfsg-5ubuntu0.20.04.7) ... #6 97.22 Setting up binutils-x86-64-linux-gnu (2.34-6ubuntu1.9) ... #6 97.24 Setting up libxpm4:amd64 (1:3.5.12-1ubuntu0.20.04.2) ... #6 97.26 Setting up libarchive13:amd64 (3.4.0-2ubuntu1.2) ... #6 97.28 Setting up libhx509-5-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.30 Setting up binutils (2.34-6ubuntu1.9) ... #6 97.32 Setting up libgcc-9-dev:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 97.34 Setting up perl (5.30.0-9ubuntu0.5) ... #6 97.39 Setting up libgd3:amd64 (2.2.5-5.2ubuntu2.1) ... #6 97.41 Setting up libdpkg-perl (1.19.7ubuntu3.2) ... #6 97.43 Setting up cpp (4:9.3.0-1ubuntu2) ... #6 97.46 Setting up libxslt1.1:amd64 (1.1.34-4ubuntu0.20.04.1) ... #6 97.48 Setting up libkrb5-26-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.50 Setting up gcc-9 (9.4.0-1ubuntu1~20.04.2) ... #6 97.52 Setting up libstdc++-9-dev:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 97.54 Setting up libnginx-mod-http-image-filter (1.18.0-0ubuntu1.4) ... #6 97.59 Setting up libheimntlm0-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.61 Setting up gcc (4:9.3.0-1ubuntu2) ... #6 97.65 Setting up dpkg-dev (1.19.7ubuntu3.2) ... #6 97.69 Setting up liberror-perl (0.17029-1) ... #6 97.71 Setting up libnginx-mod-http-xslt-filter (1.18.0-0ubuntu1.4) ... #6 97.75 Setting up libgssapi3-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.77 Setting up g++-9 (9.4.0-1ubuntu1~20.04.2) ... #6 97.79 Setting up g++ (4:9.3.0-1ubuntu2) ... #6 97.85 update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode #6 97.85 update-alternatives: warning: skip creation of /usr/share/man/man1/c++.1.gz because associated file /usr/share/man/man1/g++.1.gz (of link group c++) doesn't exist #6 97.87 Setting up build-essential (12.8ubuntu1.1) ... #6 97.89 Setting up nginx-core (1.18.0-0ubuntu1.4) ... #6 97.98 invoke-rc.d: could not determine current runlevel #6 97.99 invoke-rc.d: policy-rc.d denied execution of start. #6 98.00 Setting up nginx (1.18.0-0ubuntu1.4) ... #6 98.02 Setting up libldap-2.4-2:amd64 (2.4.49+dfsg-2ubuntu1.10) ... #6 98.04 Setting up libcurl3-gnutls:amd64 (7.68.0-1ubuntu2.22) ... #6 98.06 Setting up git (1:2.25.1-1ubuntu3.11) ... #6 98.14 Setting up libcurl4:amd64 (7.68.0-1ubuntu2.22) ... #6 98.16 Setting up curl (7.68.0-1ubuntu2.22) ... #6 98.18 Setting up cmake (3.16.3-1ubuntu1.20.04.1) ... #6 98.20 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #6 98.25 Processing triggers for ca-certificates (20230311ubuntu0.20.04.1) ... #6 98.26 Updating certificates in /etc/ssl/certs... #6 99.63 0 added, 0 removed; done. #6 99.63 Running hooks in /etc/ca-certificates/update.d... #6 99.63 done. #6 DONE 100.6s #7 [ 3/17] RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev #7 0.464 Reading package lists... #7 1.538 Building dependency tree... #7 1.711 Reading state information... #7 1.915 curl is already the newest version (7.68.0-1ubuntu2.22). #7 1.915 tzdata is already the newest version (2024a-0ubuntu0.20.04). #7 1.915 tzdata set to manually installed. #7 1.915 wget is already the newest version (1.20.3-1ubuntu2). #7 1.915 xz-utils is already the newest version (5.2.4-1ubuntu1.1). #7 1.915 xz-utils set to manually installed. #7 1.915 The following additional packages will be installed: #7 1.915 binfmt-support bzip2-doc file icu-devtools libclang-cpp10 libdrm-amdgpu1 #7 1.915 libdrm-common libdrm-intel1 libdrm-nouveau2 libdrm-radeon1 libdrm2 libedit2 #7 1.915 libevent-2.1-7 libexpat1-dev libfontconfig1-dev libfontenc1 libfreetype-dev #7 1.915 libfreetype6-dev libgcrypt20-dev libgl1 libgl1-mesa-dri libglapi-mesa #7 1.915 libglib2.0-0 libglib2.0-data libglvnd0 libglx-mesa0 libglx0 libgmp-dev #7 1.915 libgmpxx4ldbl libgnutls-dane0 libgnutls-openssl27 libgnutls28-dev #7 1.915 libgnutlsxx28 libgpg-error-dev libice-dev libice6 libicu-dev libidn2-dev #7 1.915 libllvm10 libllvm12 libmagic-mgc libmagic1 libmpdec2 libncurses-dev libnspr4 #7 1.915 libnspr4-dev libnss3 libnss3-dev libp11-kit-dev libpciaccess0 libpfm4 #7 1.915 libpipeline1 libpng-dev libpng-tools libpthread-stubs0-dev libpython3-stdlib #7 1.915 libpython3.8-minimal libpython3.8-stdlib libreadline8 libsensors-config #7 1.915 libsensors5 libsm-dev libsm6 libtasn1-6-dev libtasn1-doc libtcl8.6 libtk8.6 #7 1.915 libunbound8 libutempter0 libvulkan1 libwayland-client0 libx11-dev #7 1.915 libx11-xcb1 libxau-dev libxaw7 libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 #7 1.915 libxcb-present0 libxcb-randr0 libxcb-shape0 libxcb-shm0 libxcb-sync1 #7 1.915 libxcb-xfixes0 libxcb1-dev libxcomposite1 libxdmcp-dev libxext-dev libxext6 #7 1.915 libxfixes3 libxft-dev libxft2 libxi6 libxinerama1 libxkbfile1 libxmlsec1 #7 1.915 libxmlsec1-gcrypt libxmlsec1-gnutls libxmlsec1-nss libxmlsec1-openssl #7 1.915 libxmu6 libxmuu1 libxrandr2 libxrender-dev libxrender1 libxshmfence1 #7 1.915 libxslt1-dev libxss-dev libxss1 libxt-dev libxt6 libxtst6 libxv1 #7 1.915 libxxf86dga1 libxxf86vm1 libyaml-0-2 libz3-4 libz3-dev llvm-10 llvm-10-dev #7 1.915 llvm-10-runtime llvm-10-tools llvm-runtime mesa-vulkan-drivers mime-support #7 1.915 nettle-dev pkg-config python3 python3-minimal python3-pkg-resources #7 1.915 python3-pygments python3-yaml python3.8 python3.8-minimal readline-common #7 1.915 shared-mime-info tcl tcl-dev tcl8.6 tcl8.6-dev tk tk8.6 tk8.6-dev uuid-dev #7 1.915 x11-common x11-utils x11proto-core-dev x11proto-dev x11proto-scrnsaver-dev #7 1.915 x11proto-xext-dev xbitmaps xdg-user-dirs xorg-sgml-doctools xterm xtrans-dev #7 1.917 Suggested packages: #7 1.917 freetype2-doc libgcrypt20-doc gmp-doc libgmp10-doc libmpfr-dev dns-root-data #7 1.917 gnutls-bin gnutls-doc libice-doc icu-doc liblzma-doc ncurses-doc pciutils #7 1.917 readline-doc lm-sensors libsm-doc sqlite3-doc libssl-doc libx11-doc #7 1.917 libxcb-doc libxext-doc libxt-doc llvm-10-doc python3-doc python3-tk #7 1.917 python3-venv python3-setuptools python-pygments-doc ttf-bitstream-vera #7 1.917 python3.8-venv python3.8-doc tcl-doc tcl-tclreadline tcl8.6-doc tk-doc #7 1.917 tk8.6-doc mesa-utils xfonts-cyrillic #7 2.228 The following NEW packages will be installed: #7 2.228 binfmt-support bzip2-doc file icu-devtools libbz2-dev libclang-cpp10 #7 2.228 libdrm-amdgpu1 libdrm-common libdrm-intel1 libdrm-nouveau2 libdrm-radeon1 #7 2.228 libdrm2 libedit2 libevent-2.1-7 libexpat1-dev libffi-dev libfontconfig1-dev #7 2.228 libfontenc1 libfreetype-dev libfreetype6-dev libgcrypt20-dev libgl1 #7 2.228 libgl1-mesa-dri libglapi-mesa libglib2.0-0 libglib2.0-data libglvnd0 #7 2.228 libglx-mesa0 libglx0 libgmp-dev libgmpxx4ldbl libgnutls-dane0 #7 2.228 libgnutls-openssl27 libgnutls28-dev libgnutlsxx28 libgpg-error-dev #7 2.228 libice-dev libice6 libicu-dev libidn2-dev libllvm10 libllvm12 liblzma-dev #7 2.228 libmagic-mgc libmagic1 libmpdec2 libncurses-dev libncursesw5-dev libnspr4 #7 2.228 libnspr4-dev libnss3 libnss3-dev libp11-kit-dev libpciaccess0 libpfm4 #7 2.228 libpipeline1 libpng-dev libpng-tools libpthread-stubs0-dev libpython3-stdlib #7 2.229 libpython3.8-minimal libpython3.8-stdlib libreadline-dev libreadline8 #7 2.229 libsensors-config libsensors5 libsm-dev libsm6 libsqlite3-dev libssl-dev #7 2.229 libtasn1-6-dev libtasn1-doc libtcl8.6 libtk8.6 libunbound8 libutempter0 #7 2.229 libvulkan1 libwayland-client0 libx11-dev libx11-xcb1 libxau-dev libxaw7 #7 2.229 libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 libxcb-present0 libxcb-randr0 #7 2.229 libxcb-shape0 libxcb-shm0 libxcb-sync1 libxcb-xfixes0 libxcb1-dev #7 2.229 libxcomposite1 libxdmcp-dev libxext-dev libxext6 libxfixes3 libxft-dev #7 2.229 libxft2 libxi6 libxinerama1 libxkbfile1 libxml2-dev libxmlsec1 #7 2.229 libxmlsec1-dev libxmlsec1-gcrypt libxmlsec1-gnutls libxmlsec1-nss #7 2.229 libxmlsec1-openssl libxmu6 libxmuu1 libxrandr2 libxrender-dev libxrender1 #7 2.229 libxshmfence1 libxslt1-dev libxss-dev libxss1 libxt-dev libxt6 libxtst6 #7 2.229 libxv1 libxxf86dga1 libxxf86vm1 libyaml-0-2 libz3-4 libz3-dev llvm llvm-10 #7 2.229 llvm-10-dev llvm-10-runtime llvm-10-tools llvm-runtime mesa-vulkan-drivers #7 2.229 mime-support nettle-dev pkg-config python3 python3-minimal #7 2.229 python3-pkg-resources python3-pygments python3-yaml python3.8 #7 2.229 python3.8-minimal readline-common shared-mime-info tcl tcl-dev tcl8.6 #7 2.229 tcl8.6-dev tk tk-dev tk8.6 tk8.6-dev uuid-dev x11-common x11-utils #7 2.229 x11proto-core-dev x11proto-dev x11proto-scrnsaver-dev x11proto-xext-dev #7 2.229 xbitmaps xdg-user-dirs xorg-sgml-doctools xterm xtrans-dev zlib1g-dev #7 2.493 0 upgraded, 167 newly installed, 0 to remove and 0 not upgraded. #7 2.493 Need to get 138 MB of archives. #7 2.493 After this operation, 1062 MB of additional disk space will be used. #7 2.493 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libpython3.8-minimal amd64 3.8.10-0ubuntu1~20.04.9 [718 kB] #7 3.260 Get:2 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3.8-minimal amd64 3.8.10-0ubuntu1~20.04.9 [1890 kB] #7 4.013 Get:3 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-minimal amd64 3.8.2-0ubuntu2 [23.6 kB] #7 4.022 Get:4 http://archive.ubuntu.com/ubuntu focal/main amd64 mime-support all 3.64ubuntu1 [30.6 kB] #7 4.038 Get:5 http://archive.ubuntu.com/ubuntu focal/main amd64 libmpdec2 amd64 2.4.2-3 [81.1 kB] #7 4.067 Get:6 http://archive.ubuntu.com/ubuntu focal/main amd64 readline-common all 8.0-4 [53.5 kB] #7 4.089 Get:7 http://archive.ubuntu.com/ubuntu focal/main amd64 libreadline8 amd64 8.0-4 [131 kB] #7 4.143 Get:8 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libpython3.8-stdlib amd64 3.8.10-0ubuntu1~20.04.9 [1674 kB] #7 4.808 Get:9 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3.8 amd64 3.8.10-0ubuntu1~20.04.9 [387 kB] #7 4.961 Get:10 http://archive.ubuntu.com/ubuntu focal/main amd64 libpython3-stdlib amd64 3.8.2-0ubuntu2 [7068 B] #7 4.963 Get:11 http://archive.ubuntu.com/ubuntu focal/main amd64 python3 amd64 3.8.2-0ubuntu2 [47.6 kB] #7 4.983 Get:12 http://archive.ubuntu.com/ubuntu focal/main amd64 libmagic-mgc amd64 1:5.38-4 [218 kB] #7 5.068 Get:13 http://archive.ubuntu.com/ubuntu focal/main amd64 libmagic1 amd64 1:5.38-4 [75.9 kB] #7 5.100 Get:14 http://archive.ubuntu.com/ubuntu focal/main amd64 file amd64 1:5.38-4 [23.3 kB] #7 5.108 Get:15 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglib2.0-0 amd64 2.64.6-1~ubuntu20.04.7 [1289 kB] #7 5.621 Get:16 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglib2.0-data all 2.64.6-1~ubuntu20.04.7 [5944 B] #7 5.623 Get:17 http://archive.ubuntu.com/ubuntu focal/main amd64 libyaml-0-2 amd64 0.2.2-1 [48.9 kB] #7 5.640 Get:18 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-pkg-resources all 45.2.0-1ubuntu0.1 [130 kB] #7 5.693 Get:19 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-yaml amd64 5.3.1-1ubuntu0.1 [136 kB] #7 5.752 Get:20 http://archive.ubuntu.com/ubuntu focal/main amd64 shared-mime-info amd64 1.15-1 [430 kB] #7 5.921 Get:21 http://archive.ubuntu.com/ubuntu focal/main amd64 xdg-user-dirs amd64 0.17-2ubuntu1 [48.3 kB] #7 5.940 Get:22 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-common all 2.4.107-8ubuntu1~20.04.2 [5396 B] #7 5.941 Get:23 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm2 amd64 2.4.107-8ubuntu1~20.04.2 [34.1 kB] #7 5.952 Get:24 http://archive.ubuntu.com/ubuntu focal/main amd64 libedit2 amd64 3.1-20191231-1 [87.0 kB] #7 5.991 Get:25 http://archive.ubuntu.com/ubuntu focal/main amd64 libpipeline1 amd64 1.5.2-2build1 [27.7 kB] #7 6.000 Get:26 http://archive.ubuntu.com/ubuntu focal/main amd64 libxext6 amd64 2:1.3.4-0ubuntu1 [29.1 kB] #7 6.016 Get:27 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmuu1 amd64 2:1.1.3-0ubuntu1 [9728 B] #7 6.019 Get:28 http://archive.ubuntu.com/ubuntu focal/universe amd64 binfmt-support amd64 2.2.0-2 [58.2 kB] #7 6.041 Get:29 http://archive.ubuntu.com/ubuntu focal/main amd64 bzip2-doc all 1.0.8-2 [501 kB] #7 6.241 Get:30 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 icu-devtools amd64 66.1-2ubuntu2.1 [189 kB] #7 6.320 Get:31 http://archive.ubuntu.com/ubuntu focal/main amd64 libbz2-dev amd64 1.0.8-2 [30.2 kB] #7 6.329 Get:32 http://archive.ubuntu.com/ubuntu focal/main amd64 libllvm10 amd64 1:10.0.0-4ubuntu1 [15.3 MB] #7 12.57 Get:33 http://archive.ubuntu.com/ubuntu focal/universe amd64 libclang-cpp10 amd64 1:10.0.0-4ubuntu1 [9944 kB] #7 16.83 Get:34 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-amdgpu1 amd64 2.4.107-8ubuntu1~20.04.2 [18.6 kB] #7 16.83 Get:35 http://archive.ubuntu.com/ubuntu focal/main amd64 libpciaccess0 amd64 0.16-0ubuntu1 [17.9 kB] #7 16.84 Get:36 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-intel1 amd64 2.4.107-8ubuntu1~20.04.2 [60.3 kB] #7 16.86 Get:37 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-nouveau2 amd64 2.4.107-8ubuntu1~20.04.2 [16.6 kB] #7 16.87 Get:38 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-radeon1 amd64 2.4.107-8ubuntu1~20.04.2 [19.7 kB] #7 16.87 Get:39 http://archive.ubuntu.com/ubuntu focal/main amd64 libevent-2.1-7 amd64 2.1.11-stable-1 [138 kB] #7 16.93 Get:40 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libexpat1-dev amd64 2.2.9-1ubuntu0.6 [116 kB] #7 16.98 Get:41 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 zlib1g-dev amd64 1:1.2.11.dfsg-2ubuntu1.5 [155 kB] #7 17.04 Get:42 http://archive.ubuntu.com/ubuntu focal/main amd64 libpng-dev amd64 1.6.37-2 [175 kB] #7 17.11 Get:43 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libfreetype-dev amd64 2.10.1-2ubuntu0.3 [493 kB] #7 17.30 Get:44 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libfreetype6-dev amd64 2.10.1-2ubuntu0.3 [9808 B] #7 17.31 Get:45 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 uuid-dev amd64 2.34-0.1ubuntu9.6 [33.6 kB] #7 17.32 Get:46 http://archive.ubuntu.com/ubuntu focal/main amd64 pkg-config amd64 0.29.1-0ubuntu4 [45.5 kB] #7 17.34 Get:47 http://archive.ubuntu.com/ubuntu focal/main amd64 libfontconfig1-dev amd64 2.13.1-2ubuntu3 [731 kB] #7 17.63 Get:48 http://archive.ubuntu.com/ubuntu focal/main amd64 libfontenc1 amd64 1:1.1.4-0ubuntu1 [14.0 kB] #7 17.64 Get:49 http://archive.ubuntu.com/ubuntu focal/main amd64 libgpg-error-dev amd64 1.37-1 [109 kB] #7 17.68 Get:50 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgcrypt20-dev amd64 1.8.5-5ubuntu1.1 [471 kB] #7 17.87 Get:51 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglapi-mesa amd64 21.2.6-0ubuntu0.1~20.04.2 [27.4 kB] #7 17.88 Get:52 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libllvm12 amd64 1:12.0.0-3ubuntu1~20.04.5 [18.8 MB] #7 25.65 Get:53 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsensors-config all 1:3.6.0-2ubuntu1.1 [6052 B] #7 25.67 Get:54 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsensors5 amd64 1:3.6.0-2ubuntu1.1 [27.2 kB] #7 25.67 Get:55 http://archive.ubuntu.com/ubuntu focal/main amd64 libvulkan1 amd64 1.2.131.2-1 [93.3 kB] #7 25.68 Get:56 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl1-mesa-dri amd64 21.2.6-0ubuntu0.1~20.04.2 [11.0 MB] #7 30.46 Get:57 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-xcb1 amd64 2:1.6.9-2ubuntu1.6 [9448 B] #7 30.46 Get:58 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-dri2-0 amd64 1.14-2 [6920 B] #7 30.47 Get:59 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-dri3-0 amd64 1.14-2 [6552 B] #7 30.47 Get:60 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-glx0 amd64 1.14-2 [22.1 kB] #7 30.57 Get:61 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-present0 amd64 1.14-2 [5560 B] #7 30.62 Get:62 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-shm0 amd64 1.14-2 [5584 B] #7 30.67 Get:63 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-sync1 amd64 1.14-2 [8884 B] #7 30.73 Get:64 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-xfixes0 amd64 1.14-2 [9296 B] #7 30.79 Get:65 http://archive.ubuntu.com/ubuntu focal/main amd64 libxfixes3 amd64 1:5.0.3-2 [10.9 kB] #7 30.86 Get:66 http://archive.ubuntu.com/ubuntu focal/main amd64 libxshmfence1 amd64 1.3-1 [5028 B] #7 30.90 Get:67 http://archive.ubuntu.com/ubuntu focal/main amd64 libxxf86vm1 amd64 1:1.1.4-1build1 [10.2 kB] #7 30.96 Get:68 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglx-mesa0 amd64 21.2.6-0ubuntu0.1~20.04.2 [137 kB] #7 31.14 Get:69 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgmpxx4ldbl amd64 2:6.2.0+dfsg-4ubuntu0.1 [9144 B] #7 31.19 Get:70 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgmp-dev amd64 2:6.2.0+dfsg-4ubuntu0.1 [320 kB] #7 31.31 Get:71 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutls-openssl27 amd64 3.6.13-2ubuntu1.11 [29.8 kB] #7 31.40 Get:72 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libunbound8 amd64 1.9.4-2ubuntu1.5 [353 kB] #7 31.66 Get:73 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutls-dane0 amd64 3.6.13-2ubuntu1.11 [29.1 kB] #7 31.71 Get:74 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutlsxx28 amd64 3.6.13-2ubuntu1.11 [14.7 kB] #7 31.76 Get:75 http://archive.ubuntu.com/ubuntu focal/main amd64 libidn2-dev amd64 2.2.0-2 [64.6 kB] #7 31.81 Get:76 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libp11-kit-dev amd64 0.23.20-1ubuntu0.1 [65.2 kB] #7 31.86 Get:77 http://archive.ubuntu.com/ubuntu focal/main amd64 libtasn1-6-dev amd64 4.16.0-2 [85.8 kB] #7 31.92 Get:78 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 nettle-dev amd64 3.5.1+really3.5.1-2ubuntu0.2 [987 kB] #7 32.16 Get:79 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutls28-dev amd64 3.6.13-2ubuntu1.11 [873 kB] #7 32.38 Get:80 http://archive.ubuntu.com/ubuntu focal/main amd64 x11-common all 1:7.7+19ubuntu14 [22.3 kB] #7 32.43 Get:81 http://archive.ubuntu.com/ubuntu focal/main amd64 libice6 amd64 2:1.0.10-0ubuntu1 [41.0 kB] #7 32.48 Get:82 http://archive.ubuntu.com/ubuntu focal/main amd64 xorg-sgml-doctools all 1:1.11-1 [12.9 kB] #7 32.57 Get:83 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-dev all 2019.2-1ubuntu1 [594 kB] #7 32.88 Get:84 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-core-dev all 2019.2-1ubuntu1 [2620 B] #7 32.92 Get:85 http://archive.ubuntu.com/ubuntu focal/main amd64 libice-dev amd64 2:1.0.10-0ubuntu1 [47.8 kB] #7 32.97 Get:86 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libicu-dev amd64 66.1-2ubuntu2.1 [9451 kB] #7 34.76 Get:87 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libncurses-dev amd64 6.2-0ubuntu2.1 [340 kB] #7 34.86 Get:88 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libncursesw5-dev amd64 6.2-0ubuntu2.1 [988 B] #7 34.91 Get:89 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnspr4 amd64 2:4.35-0ubuntu0.20.04.1 [108 kB] #7 34.97 Get:90 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnspr4-dev amd64 2:4.35-0ubuntu0.20.04.1 [207 kB] #7 35.05 Get:91 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnss3 amd64 2:3.98-0ubuntu0.20.04.2 [1391 kB] #7 35.35 Get:92 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnss3-dev amd64 2:3.98-0ubuntu0.20.04.2 [246 kB] #7 35.44 Get:93 http://archive.ubuntu.com/ubuntu focal/main amd64 libpng-tools amd64 1.6.37-2 [26.1 kB] #7 35.53 Get:94 http://archive.ubuntu.com/ubuntu focal/main amd64 libpthread-stubs0-dev amd64 0.4-1 [5384 B] #7 35.57 Get:95 http://archive.ubuntu.com/ubuntu focal/main amd64 libreadline-dev amd64 8.0-4 [141 kB] #7 35.75 Get:96 http://archive.ubuntu.com/ubuntu focal/main amd64 libsm6 amd64 2:1.2.3-1 [16.1 kB] #7 35.80 Get:97 http://archive.ubuntu.com/ubuntu focal/main amd64 libsm-dev amd64 2:1.2.3-1 [17.0 kB] #7 35.85 Get:98 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsqlite3-dev amd64 3.31.1-4ubuntu0.6 [696 kB] #7 36.05 Get:99 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libssl-dev amd64 1.1.1f-1ubuntu2.22 [1586 kB] #7 36.41 Get:100 http://archive.ubuntu.com/ubuntu focal/main amd64 libtcl8.6 amd64 8.6.10+dfsg-1 [902 kB] #7 36.61 Get:101 http://archive.ubuntu.com/ubuntu focal/main amd64 libxrender1 amd64 1:0.9.10-1 [18.7 kB] #7 36.66 Get:102 http://archive.ubuntu.com/ubuntu focal/main amd64 libxft2 amd64 2.3.3-0ubuntu1 [39.2 kB] #7 36.71 Get:103 http://archive.ubuntu.com/ubuntu focal/main amd64 libxss1 amd64 1:1.2.3-1 [8140 B] #7 36.75 Get:104 http://archive.ubuntu.com/ubuntu focal/main amd64 libtk8.6 amd64 8.6.10-1 [714 kB] #7 36.96 Get:105 http://archive.ubuntu.com/ubuntu focal/main amd64 libutempter0 amd64 1.1.6-4 [8256 B] #7 37.01 Get:106 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwayland-client0 amd64 1.18.0-1ubuntu0.1 [23.9 kB] #7 37.10 Get:107 http://archive.ubuntu.com/ubuntu focal/main amd64 libxau-dev amd64 1:1.0.9-0ubuntu1 [9552 B] #7 37.15 Get:108 http://archive.ubuntu.com/ubuntu focal/main amd64 libxdmcp-dev amd64 1:1.1.3-0ubuntu1 [25.3 kB] #7 37.21 Get:109 http://archive.ubuntu.com/ubuntu focal/main amd64 xtrans-dev all 1.4.0-1 [68.9 kB] #7 37.30 Get:110 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb1-dev amd64 1.14-2 [80.5 kB] #7 37.36 Get:111 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-dev amd64 2:1.6.9-2ubuntu1.6 [648 kB] #7 37.54 Get:112 http://archive.ubuntu.com/ubuntu focal/main amd64 libxt6 amd64 1:1.1.5-1 [160 kB] #7 37.61 Get:113 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmu6 amd64 2:1.1.3-0ubuntu1 [45.8 kB] #7 37.66 Get:114 http://archive.ubuntu.com/ubuntu focal/main amd64 libxaw7 amd64 2:1.0.13-1 [173 kB] #7 37.73 Get:115 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-randr0 amd64 1.14-2 [16.3 kB] #7 37.82 Get:116 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-shape0 amd64 1.14-2 [5928 B] #7 37.86 Get:117 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcomposite1 amd64 1:0.4.5-1 [6976 B] #7 37.91 Get:118 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-xext-dev all 2019.2-1ubuntu1 [2616 B] #7 37.95 Get:119 http://archive.ubuntu.com/ubuntu focal/main amd64 libxext-dev amd64 2:1.3.4-0ubuntu1 [82.2 kB] #7 38.11 Get:120 http://archive.ubuntu.com/ubuntu focal/main amd64 libxrender-dev amd64 1:0.9.10-1 [24.9 kB] #7 38.17 Get:121 http://archive.ubuntu.com/ubuntu focal/main amd64 libxft-dev amd64 2.3.3-0ubuntu1 [49.1 kB] #7 38.23 Get:122 http://archive.ubuntu.com/ubuntu focal/main amd64 libxi6 amd64 2:1.7.10-0ubuntu1 [29.9 kB] #7 38.28 Get:123 http://archive.ubuntu.com/ubuntu focal/main amd64 libxinerama1 amd64 2:1.1.4-2 [6904 B] #7 38.33 Get:124 http://archive.ubuntu.com/ubuntu focal/main amd64 libxkbfile1 amd64 1:1.1.0-1 [65.3 kB] #7 38.40 Get:125 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxml2-dev amd64 2.9.10+dfsg-5ubuntu0.20.04.7 [735 kB] #7 38.62 Get:126 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1 amd64 1.2.28-2 [127 kB] #7 38.72 Get:127 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-gcrypt amd64 1.2.28-2 [41.5 kB] #7 38.82 Get:128 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-gnutls amd64 1.2.28-2 [33.4 kB] #7 38.88 Get:129 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-nss amd64 1.2.28-2 [61.8 kB] #7 38.96 Get:130 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-openssl amd64 1.2.28-2 [75.9 kB] #7 39.03 Get:131 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxslt1-dev amd64 1.1.34-4ubuntu0.20.04.1 [219 kB] #7 39.14 Get:132 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-dev amd64 1.2.28-2 [387 kB] #7 39.25 Get:133 http://archive.ubuntu.com/ubuntu focal/main amd64 libxrandr2 amd64 2:1.5.2-0ubuntu1 [18.5 kB] #7 39.29 Get:134 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-scrnsaver-dev all 2019.2-1ubuntu1 [2624 B] #7 39.33 Get:135 http://archive.ubuntu.com/ubuntu focal/main amd64 libxss-dev amd64 1:1.2.3-1 [11.9 kB] #7 39.37 Get:136 http://archive.ubuntu.com/ubuntu focal/main amd64 libxt-dev amd64 1:1.1.5-1 [395 kB] #7 39.50 Get:137 http://archive.ubuntu.com/ubuntu focal/main amd64 libxtst6 amd64 2:1.2.3-1 [12.8 kB] #7 39.59 Get:138 http://archive.ubuntu.com/ubuntu focal/main amd64 libxv1 amd64 2:1.0.11-1 [10.7 kB] #7 39.64 Get:139 http://archive.ubuntu.com/ubuntu focal/main amd64 libxxf86dga1 amd64 2:1.1.5-0ubuntu1 [12.0 kB] #7 39.69 Get:140 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10-runtime amd64 1:10.0.0-4ubuntu1 [180 kB] #7 39.88 Get:141 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-runtime amd64 1:10.0-50~exp1 [2916 B] #7 39.93 Get:142 http://archive.ubuntu.com/ubuntu focal/main amd64 libpfm4 amd64 4.10.1+git20-g7700f49-2 [266 kB] #7 40.02 Get:143 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10 amd64 1:10.0.0-4ubuntu1 [5214 kB] #7 41.04 Get:144 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm amd64 1:10.0-50~exp1 [3880 B] #7 41.08 Get:145 http://archive.ubuntu.com/ubuntu focal/main amd64 libffi-dev amd64 3.3-4 [57.0 kB] #7 41.13 Get:146 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-pygments all 2.3.1+dfsg-1ubuntu2.2 [579 kB] #7 41.29 Get:147 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10-tools amd64 1:10.0.0-4ubuntu1 [317 kB] #7 41.38 Get:148 http://archive.ubuntu.com/ubuntu focal/universe amd64 libz3-4 amd64 4.8.7-4build1 [6792 kB] #7 42.66 Get:149 http://archive.ubuntu.com/ubuntu focal/universe amd64 libz3-dev amd64 4.8.7-4build1 [67.5 kB] #7 42.81 Get:150 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10-dev amd64 1:10.0.0-4ubuntu1 [26.0 MB] #7 47.54 Get:151 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 mesa-vulkan-drivers amd64 21.2.6-0ubuntu0.1~20.04.2 [5788 kB] #7 48.60 Get:152 http://archive.ubuntu.com/ubuntu focal/main amd64 tcl8.6 amd64 8.6.10+dfsg-1 [14.8 kB] #7 48.65 Get:153 http://archive.ubuntu.com/ubuntu focal/universe amd64 tcl amd64 8.6.9+1 [5112 B] #7 48.69 Get:154 http://archive.ubuntu.com/ubuntu focal/main amd64 tcl8.6-dev amd64 8.6.10+dfsg-1 [905 kB] #7 48.89 Get:155 http://archive.ubuntu.com/ubuntu focal/universe amd64 tcl-dev amd64 8.6.9+1 [5760 B] #7 48.94 Get:156 http://archive.ubuntu.com/ubuntu focal/main amd64 tk8.6 amd64 8.6.10-1 [12.5 kB] #7 48.98 Get:157 http://archive.ubuntu.com/ubuntu focal/universe amd64 tk amd64 8.6.9+1 [3240 B] #7 49.02 Get:158 http://archive.ubuntu.com/ubuntu focal/main amd64 tk8.6-dev amd64 8.6.10-1 [711 kB] #7 49.20 Get:159 http://archive.ubuntu.com/ubuntu focal/universe amd64 tk-dev amd64 8.6.9+1 [3076 B] #7 49.29 Get:160 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglvnd0 amd64 1.3.2-1~ubuntu0.20.04.2 [48.1 kB] #7 49.42 Get:161 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglx0 amd64 1.3.2-1~ubuntu0.20.04.2 [32.5 kB] #7 49.48 Get:162 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl1 amd64 1.3.2-1~ubuntu0.20.04.2 [85.8 kB] #7 49.57 Get:163 http://archive.ubuntu.com/ubuntu focal/main amd64 x11-utils amd64 7.7+5 [199 kB] #7 49.68 Get:164 http://archive.ubuntu.com/ubuntu focal/main amd64 xbitmaps all 1.1.1-2 [28.1 kB] #7 49.72 Get:165 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 xterm amd64 353-1ubuntu1.20.04.2 [765 kB] #7 49.91 Get:166 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 liblzma-dev amd64 5.2.4-1ubuntu1.1 [147 kB] #7 49.98 Get:167 http://archive.ubuntu.com/ubuntu focal/main amd64 libtasn1-doc all 4.16.0-2 [303 kB] #7 50.23 debconf: delaying package configuration, since apt-utils is not installed #7 50.28 Fetched 138 MB in 48s (2890 kB/s) #7 50.34 Selecting previously unselected package libpython3.8-minimal:amd64. #7 50.34 (Reading database ... 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libfontconfig1-dev:amd64. #7 58.61 Preparing to unpack .../036-libfontconfig1-dev_2.13.1-2ubuntu3_amd64.deb ... #7 58.61 Unpacking libfontconfig1-dev:amd64 (2.13.1-2ubuntu3) ... #7 58.74 Selecting previously unselected package libfontenc1:amd64. #7 58.74 Preparing to unpack .../037-libfontenc1_1%3a1.1.4-0ubuntu1_amd64.deb ... #7 58.75 Unpacking libfontenc1:amd64 (1:1.1.4-0ubuntu1) ... #7 58.82 Selecting previously unselected package libgpg-error-dev. #7 58.82 Preparing to unpack .../038-libgpg-error-dev_1.37-1_amd64.deb ... #7 58.83 Unpacking libgpg-error-dev (1.37-1) ... #7 58.91 Selecting previously unselected package libgcrypt20-dev. #7 58.91 Preparing to unpack .../039-libgcrypt20-dev_1.8.5-5ubuntu1.1_amd64.deb ... #7 58.92 Unpacking libgcrypt20-dev (1.8.5-5ubuntu1.1) ... #7 59.06 Selecting previously unselected package libglapi-mesa:amd64. #7 59.06 Preparing to unpack .../040-libglapi-mesa_21.2.6-0ubuntu0.1~20.04.2_amd64.deb ... #7 59.07 Unpacking libglapi-mesa:amd64 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#7 62.71 Preparing to unpack .../050-libxcb-present0_1.14-2_amd64.deb ... #7 62.71 Unpacking libxcb-present0:amd64 (1.14-2) ... #7 62.79 Selecting previously unselected package libxcb-shm0:amd64. #7 62.79 Preparing to unpack .../051-libxcb-shm0_1.14-2_amd64.deb ... #7 62.80 Unpacking libxcb-shm0:amd64 (1.14-2) ... #7 62.87 Selecting previously unselected package libxcb-sync1:amd64. #7 62.87 Preparing to unpack .../052-libxcb-sync1_1.14-2_amd64.deb ... #7 62.88 Unpacking libxcb-sync1:amd64 (1.14-2) ... #7 62.96 Selecting previously unselected package libxcb-xfixes0:amd64. #7 62.96 Preparing to unpack .../053-libxcb-xfixes0_1.14-2_amd64.deb ... #7 62.97 Unpacking libxcb-xfixes0:amd64 (1.14-2) ... #7 63.04 Selecting previously unselected package libxfixes3:amd64. #7 63.05 Preparing to unpack .../054-libxfixes3_1%3a5.0.3-2_amd64.deb ... #7 63.05 Unpacking libxfixes3:amd64 (1:5.0.3-2) ... #7 63.13 Selecting previously unselected package libxshmfence1:amd64. #7 63.13 Preparing to unpack .../055-libxshmfence1_1.3-1_amd64.deb ... #7 63.14 Unpacking libxshmfence1:amd64 (1.3-1) ... #7 63.21 Selecting previously unselected package libxxf86vm1:amd64. #7 63.21 Preparing to unpack .../056-libxxf86vm1_1%3a1.1.4-1build1_amd64.deb ... #7 63.22 Unpacking libxxf86vm1:amd64 (1:1.1.4-1build1) ... #7 63.30 Selecting previously unselected package libglx-mesa0:amd64. #7 63.30 Preparing to unpack .../057-libglx-mesa0_21.2.6-0ubuntu0.1~20.04.2_amd64.deb ... #7 63.31 Unpacking libglx-mesa0:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 63.41 Selecting previously unselected package libgmpxx4ldbl:amd64. #7 63.41 Preparing to unpack .../058-libgmpxx4ldbl_2%3a6.2.0+dfsg-4ubuntu0.1_amd64.deb ... #7 63.42 Unpacking libgmpxx4ldbl:amd64 (2:6.2.0+dfsg-4ubuntu0.1) ... #7 63.48 Selecting previously unselected package libgmp-dev:amd64. #7 63.49 Preparing to unpack .../059-libgmp-dev_2%3a6.2.0+dfsg-4ubuntu0.1_amd64.deb ... #7 63.50 Unpacking libgmp-dev:amd64 (2:6.2.0+dfsg-4ubuntu0.1) ... #7 63.61 Selecting previously unselected package libgnutls-openssl27:amd64. #7 63.61 Preparing to unpack .../060-libgnutls-openssl27_3.6.13-2ubuntu1.11_amd64.deb ... #7 63.62 Unpacking libgnutls-openssl27:amd64 (3.6.13-2ubuntu1.11) ... #7 63.69 Selecting previously unselected package libunbound8:amd64. #7 63.69 Preparing to unpack .../061-libunbound8_1.9.4-2ubuntu1.5_amd64.deb ... #7 63.70 Unpacking libunbound8:amd64 (1.9.4-2ubuntu1.5) ... #7 63.82 Selecting previously unselected package libgnutls-dane0:amd64. #7 63.83 Preparing to unpack .../062-libgnutls-dane0_3.6.13-2ubuntu1.11_amd64.deb ... #7 63.83 Unpacking libgnutls-dane0:amd64 (3.6.13-2ubuntu1.11) ... #7 63.91 Selecting previously unselected package libgnutlsxx28:amd64. #7 63.91 Preparing to unpack .../063-libgnutlsxx28_3.6.13-2ubuntu1.11_amd64.deb ... #7 63.92 Unpacking libgnutlsxx28:amd64 (3.6.13-2ubuntu1.11) ... #7 63.98 Selecting previously unselected package libidn2-dev:amd64. #7 63.99 Preparing to unpack .../064-libidn2-dev_2.2.0-2_amd64.deb ... #7 63.99 Unpacking libidn2-dev:amd64 (2.2.0-2) ... #7 64.07 Selecting previously unselected package libp11-kit-dev:amd64. #7 64.08 Preparing to unpack .../065-libp11-kit-dev_0.23.20-1ubuntu0.1_amd64.deb ... #7 64.08 Unpacking libp11-kit-dev:amd64 (0.23.20-1ubuntu0.1) ... #7 64.16 Selecting previously unselected package libtasn1-6-dev:amd64. #7 64.17 Preparing to unpack .../066-libtasn1-6-dev_4.16.0-2_amd64.deb ... #7 64.18 Unpacking libtasn1-6-dev:amd64 (4.16.0-2) ... #7 64.26 Selecting previously unselected package nettle-dev:amd64. #7 64.26 Preparing to unpack .../067-nettle-dev_3.5.1+really3.5.1-2ubuntu0.2_amd64.deb ... #7 64.27 Unpacking nettle-dev:amd64 (3.5.1+really3.5.1-2ubuntu0.2) ... #7 64.44 Selecting previously unselected package libgnutls28-dev:amd64. #7 64.44 Preparing to unpack .../068-libgnutls28-dev_3.6.13-2ubuntu1.11_amd64.deb ... #7 64.45 Unpacking libgnutls28-dev:amd64 (3.6.13-2ubuntu1.11) ... #7 64.63 Selecting previously unselected package x11-common. #7 64.64 Preparing to unpack .../069-x11-common_1%3a7.7+19ubuntu14_all.deb ... #7 64.67 dpkg-query: no packages found matching nux-tools #7 64.69 Unpacking x11-common (1:7.7+19ubuntu14) ... #7 64.77 Selecting previously unselected package libice6:amd64. #7 64.78 Preparing to unpack .../070-libice6_2%3a1.0.10-0ubuntu1_amd64.deb ... #7 64.78 Unpacking libice6:amd64 (2:1.0.10-0ubuntu1) ... #7 64.85 Selecting previously unselected package xorg-sgml-doctools. #7 64.86 Preparing to unpack .../071-xorg-sgml-doctools_1%3a1.11-1_all.deb ... #7 64.87 Unpacking xorg-sgml-doctools (1:1.11-1) ... #7 64.93 Selecting previously unselected package x11proto-dev. #7 64.94 Preparing to unpack .../072-x11proto-dev_2019.2-1ubuntu1_all.deb ... #7 64.94 Unpacking x11proto-dev (2019.2-1ubuntu1) ... #7 65.08 Selecting previously unselected package x11proto-core-dev. #7 65.09 Preparing to unpack .../073-x11proto-core-dev_2019.2-1ubuntu1_all.deb ... #7 65.10 Unpacking x11proto-core-dev (2019.2-1ubuntu1) ... #7 65.16 Selecting previously unselected package libice-dev:amd64. #7 65.16 Preparing to unpack .../074-libice-dev_2%3a1.0.10-0ubuntu1_amd64.deb ... #7 65.17 Unpacking libice-dev:amd64 (2:1.0.10-0ubuntu1) ... #7 65.25 Selecting previously unselected package libicu-dev:amd64. #7 65.25 Preparing to unpack .../075-libicu-dev_66.1-2ubuntu2.1_amd64.deb ... #7 65.26 Unpacking libicu-dev:amd64 (66.1-2ubuntu2.1) ... #7 66.22 Selecting previously unselected package libncurses-dev:amd64. #7 66.23 Preparing to unpack .../076-libncurses-dev_6.2-0ubuntu2.1_amd64.deb ... #7 66.24 Unpacking libncurses-dev:amd64 (6.2-0ubuntu2.1) ... #7 66.36 Selecting previously unselected package libncursesw5-dev:amd64. #7 66.36 Preparing to unpack .../077-libncursesw5-dev_6.2-0ubuntu2.1_amd64.deb ... #7 66.37 Unpacking libncursesw5-dev:amd64 (6.2-0ubuntu2.1) ... #7 66.44 Selecting previously unselected package libnspr4:amd64. #7 66.44 Preparing to unpack .../078-libnspr4_2%3a4.35-0ubuntu0.20.04.1_amd64.deb ... #7 66.45 Unpacking libnspr4:amd64 (2:4.35-0ubuntu0.20.04.1) ... #7 66.54 Selecting previously unselected package libnspr4-dev. #7 66.54 Preparing to unpack .../079-libnspr4-dev_2%3a4.35-0ubuntu0.20.04.1_amd64.deb ... #7 66.55 Unpacking libnspr4-dev (2:4.35-0ubuntu0.20.04.1) ... #7 66.66 Selecting previously unselected package libnss3:amd64. #7 66.67 Preparing to unpack .../080-libnss3_2%3a3.98-0ubuntu0.20.04.2_amd64.deb ... #7 66.67 Unpacking libnss3:amd64 (2:3.98-0ubuntu0.20.04.2) ... #7 66.88 Selecting previously unselected package libnss3-dev:amd64. #7 66.88 Preparing to unpack .../081-libnss3-dev_2%3a3.98-0ubuntu0.20.04.2_amd64.deb ... #7 66.89 Unpacking libnss3-dev:amd64 (2:3.98-0ubuntu0.20.04.2) ... #7 67.00 Selecting previously unselected package libpng-tools. #7 67.00 Preparing to unpack .../082-libpng-tools_1.6.37-2_amd64.deb ... #7 67.01 Unpacking libpng-tools (1.6.37-2) ... #7 67.07 Selecting previously unselected package libpthread-stubs0-dev:amd64. #7 67.07 Preparing to unpack .../083-libpthread-stubs0-dev_0.4-1_amd64.deb ... #7 67.08 Unpacking libpthread-stubs0-dev:amd64 (0.4-1) ... #7 67.17 Selecting previously unselected package libreadline-dev:amd64. #7 67.18 Preparing to unpack .../084-libreadline-dev_8.0-4_amd64.deb ... #7 67.19 Unpacking libreadline-dev:amd64 (8.0-4) ... #7 67.29 Selecting previously unselected package libsm6:amd64. #7 67.29 Preparing to unpack .../085-libsm6_2%3a1.2.3-1_amd64.deb ... #7 67.30 Unpacking libsm6:amd64 (2:1.2.3-1) ... #7 67.37 Selecting previously unselected package libsm-dev:amd64. #7 67.37 Preparing to unpack .../086-libsm-dev_2%3a1.2.3-1_amd64.deb ... #7 67.38 Unpacking libsm-dev:amd64 (2:1.2.3-1) ... #7 67.45 Selecting previously unselected package libsqlite3-dev:amd64. #7 67.45 Preparing to unpack .../087-libsqlite3-dev_3.31.1-4ubuntu0.6_amd64.deb ... #7 67.46 Unpacking libsqlite3-dev:amd64 (3.31.1-4ubuntu0.6) ... #7 67.60 Selecting previously unselected package libssl-dev:amd64. #7 67.60 Preparing to unpack .../088-libssl-dev_1.1.1f-1ubuntu2.22_amd64.deb ... #7 67.61 Unpacking libssl-dev:amd64 (1.1.1f-1ubuntu2.22) ... #7 67.84 Selecting previously unselected package libtcl8.6:amd64. #7 67.85 Preparing to unpack .../089-libtcl8.6_8.6.10+dfsg-1_amd64.deb ... #7 67.85 Unpacking libtcl8.6:amd64 (8.6.10+dfsg-1) ... #7 68.05 Selecting previously unselected package libxrender1:amd64. #7 68.05 Preparing to unpack .../090-libxrender1_1%3a0.9.10-1_amd64.deb ... #7 68.06 Unpacking libxrender1:amd64 (1:0.9.10-1) ... #7 68.13 Selecting previously unselected package libxft2:amd64. #7 68.14 Preparing to unpack .../091-libxft2_2.3.3-0ubuntu1_amd64.deb ... #7 68.14 Unpacking libxft2:amd64 (2.3.3-0ubuntu1) ... #7 68.23 Selecting previously unselected package libxss1:amd64. #7 68.23 Preparing to unpack .../092-libxss1_1%3a1.2.3-1_amd64.deb ... #7 68.24 Unpacking libxss1:amd64 (1:1.2.3-1) ... #7 68.31 Selecting previously unselected package libtk8.6:amd64. #7 68.31 Preparing to unpack .../093-libtk8.6_8.6.10-1_amd64.deb ... #7 68.32 Unpacking libtk8.6:amd64 (8.6.10-1) ... #7 68.47 Selecting previously unselected package libutempter0:amd64. #7 68.47 Preparing to unpack .../094-libutempter0_1.1.6-4_amd64.deb ... #7 68.48 Unpacking libutempter0:amd64 (1.1.6-4) ... #7 68.55 Selecting previously unselected package libwayland-client0:amd64. #7 68.56 Preparing to unpack .../095-libwayland-client0_1.18.0-1ubuntu0.1_amd64.deb ... #7 68.56 Unpacking libwayland-client0:amd64 (1.18.0-1ubuntu0.1) ... #7 68.63 Selecting previously unselected package libxau-dev:amd64. #7 68.63 Preparing to unpack .../096-libxau-dev_1%3a1.0.9-0ubuntu1_amd64.deb ... #7 68.64 Unpacking libxau-dev:amd64 (1:1.0.9-0ubuntu1) ... #7 68.70 Selecting previously unselected package libxdmcp-dev:amd64. #7 68.71 Preparing to unpack .../097-libxdmcp-dev_1%3a1.1.3-0ubuntu1_amd64.deb ... #7 68.71 Unpacking libxdmcp-dev:amd64 (1:1.1.3-0ubuntu1) ... #7 68.78 Selecting previously unselected package xtrans-dev. #7 68.78 Preparing to unpack .../098-xtrans-dev_1.4.0-1_all.deb ... #7 68.79 Unpacking xtrans-dev (1.4.0-1) ... #7 68.87 Selecting previously unselected package libxcb1-dev:amd64. #7 68.87 Preparing to unpack .../099-libxcb1-dev_1.14-2_amd64.deb ... #7 68.88 Unpacking libxcb1-dev:amd64 (1.14-2) ... #7 68.96 Selecting previously unselected package libx11-dev:amd64. #7 68.96 Preparing to unpack .../100-libx11-dev_2%3a1.6.9-2ubuntu1.6_amd64.deb ... #7 68.97 Unpacking libx11-dev:amd64 (2:1.6.9-2ubuntu1.6) ... #7 69.12 Selecting previously unselected package libxt6:amd64. #7 69.12 Preparing to unpack .../101-libxt6_1%3a1.1.5-1_amd64.deb ... #7 69.12 Unpacking libxt6:amd64 (1:1.1.5-1) ... #7 69.22 Selecting previously unselected package libxmu6:amd64. #7 69.22 Preparing to unpack .../102-libxmu6_2%3a1.1.3-0ubuntu1_amd64.deb ... #7 69.23 Unpacking libxmu6:amd64 (2:1.1.3-0ubuntu1) ... #7 69.31 Selecting previously unselected package libxaw7:amd64. #7 69.32 Preparing to unpack .../103-libxaw7_2%3a1.0.13-1_amd64.deb ... #7 69.32 Unpacking libxaw7:amd64 (2:1.0.13-1) ... #7 69.43 Selecting previously unselected package libxcb-randr0:amd64. #7 69.43 Preparing to unpack .../104-libxcb-randr0_1.14-2_amd64.deb ... #7 69.44 Unpacking libxcb-randr0:amd64 (1.14-2) ... #7 69.51 Selecting previously unselected package libxcb-shape0:amd64. #7 69.52 Preparing to unpack .../105-libxcb-shape0_1.14-2_amd64.deb ... #7 69.52 Unpacking libxcb-shape0:amd64 (1.14-2) ... #7 69.59 Selecting previously unselected package libxcomposite1:amd64. #7 69.60 Preparing to unpack .../106-libxcomposite1_1%3a0.4.5-1_amd64.deb ... #7 69.61 Unpacking libxcomposite1:amd64 (1:0.4.5-1) ... #7 69.67 Selecting previously unselected package x11proto-xext-dev. #7 69.67 Preparing to unpack .../107-x11proto-xext-dev_2019.2-1ubuntu1_all.deb ... #7 69.68 Unpacking x11proto-xext-dev (2019.2-1ubuntu1) ... #7 69.74 Selecting previously unselected package libxext-dev:amd64. #7 69.75 Preparing to unpack .../108-libxext-dev_2%3a1.3.4-0ubuntu1_amd64.deb ... #7 69.75 Unpacking libxext-dev:amd64 (2:1.3.4-0ubuntu1) ... #7 69.84 Selecting previously unselected package libxrender-dev:amd64. #7 69.84 Preparing to unpack .../109-libxrender-dev_1%3a0.9.10-1_amd64.deb ... #7 69.84 Unpacking libxrender-dev:amd64 (1:0.9.10-1) ... #7 69.90 Selecting previously unselected package libxft-dev:amd64. #7 69.90 Preparing to unpack .../110-libxft-dev_2.3.3-0ubuntu1_amd64.deb ... #7 69.91 Unpacking libxft-dev:amd64 (2.3.3-0ubuntu1) ... #7 69.98 Selecting previously unselected package libxi6:amd64. #7 69.99 Preparing to unpack .../111-libxi6_2%3a1.7.10-0ubuntu1_amd64.deb ... #7 69.99 Unpacking libxi6:amd64 (2:1.7.10-0ubuntu1) ... #7 70.07 Selecting previously unselected package libxinerama1:amd64. #7 70.07 Preparing to unpack .../112-libxinerama1_2%3a1.1.4-2_amd64.deb ... #7 70.08 Unpacking libxinerama1:amd64 (2:1.1.4-2) ... #7 70.15 Selecting previously unselected package libxkbfile1:amd64. #7 70.15 Preparing to unpack .../113-libxkbfile1_1%3a1.1.0-1_amd64.deb ... #7 70.16 Unpacking libxkbfile1:amd64 (1:1.1.0-1) ... #7 70.23 Selecting previously unselected package libxml2-dev:amd64. #7 70.24 Preparing to unpack .../114-libxml2-dev_2.9.10+dfsg-5ubuntu0.20.04.7_amd64.deb ... #7 70.25 Unpacking libxml2-dev:amd64 (2.9.10+dfsg-5ubuntu0.20.04.7) ... #7 70.40 Selecting previously unselected package libxmlsec1:amd64. #7 70.41 Preparing to unpack .../115-libxmlsec1_1.2.28-2_amd64.deb ... #7 70.42 Unpacking libxmlsec1:amd64 (1.2.28-2) ... #7 70.52 Selecting previously unselected package libxmlsec1-gcrypt:amd64. #7 70.52 Preparing to unpack .../116-libxmlsec1-gcrypt_1.2.28-2_amd64.deb ... #7 70.53 Unpacking libxmlsec1-gcrypt:amd64 (1.2.28-2) ... #7 70.61 Selecting previously unselected package libxmlsec1-gnutls:amd64. #7 70.62 Preparing to unpack .../117-libxmlsec1-gnutls_1.2.28-2_amd64.deb ... #7 70.62 Unpacking libxmlsec1-gnutls:amd64 (1.2.28-2) ... #7 70.69 Selecting previously unselected package libxmlsec1-nss:amd64. #7 70.69 Preparing to unpack .../118-libxmlsec1-nss_1.2.28-2_amd64.deb ... #7 70.70 Unpacking libxmlsec1-nss:amd64 (1.2.28-2) ... #7 70.79 Selecting previously unselected package libxmlsec1-openssl:amd64. #7 70.80 Preparing to unpack .../119-libxmlsec1-openssl_1.2.28-2_amd64.deb ... #7 70.80 Unpacking libxmlsec1-openssl:amd64 (1.2.28-2) ... #7 70.89 Selecting previously unselected package libxslt1-dev:amd64. #7 70.89 Preparing to unpack .../120-libxslt1-dev_1.1.34-4ubuntu0.20.04.1_amd64.deb ... #7 70.90 Unpacking libxslt1-dev:amd64 (1.1.34-4ubuntu0.20.04.1) ... #7 71.00 Selecting previously unselected package libxmlsec1-dev. #7 71.01 Preparing to unpack .../121-libxmlsec1-dev_1.2.28-2_amd64.deb ... #7 71.01 Unpacking libxmlsec1-dev (1.2.28-2) ... #7 71.15 Selecting previously unselected package libxrandr2:amd64. #7 71.15 Preparing to unpack .../122-libxrandr2_2%3a1.5.2-0ubuntu1_amd64.deb ... #7 71.15 Unpacking libxrandr2:amd64 (2:1.5.2-0ubuntu1) ... #7 71.21 Selecting previously unselected package x11proto-scrnsaver-dev. #7 71.22 Preparing to unpack .../123-x11proto-scrnsaver-dev_2019.2-1ubuntu1_all.deb ... #7 71.22 Unpacking x11proto-scrnsaver-dev (2019.2-1ubuntu1) ... #7 71.28 Selecting previously unselected package libxss-dev:amd64. #7 71.28 Preparing to unpack .../124-libxss-dev_1%3a1.2.3-1_amd64.deb ... #7 71.29 Unpacking libxss-dev:amd64 (1:1.2.3-1) ... #7 71.36 Selecting previously unselected package libxt-dev:amd64. #7 71.36 Preparing to unpack .../125-libxt-dev_1%3a1.1.5-1_amd64.deb ... #7 71.37 Unpacking libxt-dev:amd64 (1:1.1.5-1) ... #7 71.50 Selecting previously unselected package libxtst6:amd64. #7 71.51 Preparing to unpack .../126-libxtst6_2%3a1.2.3-1_amd64.deb ... #7 71.52 Unpacking libxtst6:amd64 (2:1.2.3-1) ... #7 71.58 Selecting previously unselected package libxv1:amd64. #7 71.59 Preparing to unpack .../127-libxv1_2%3a1.0.11-1_amd64.deb ... #7 71.59 Unpacking libxv1:amd64 (2:1.0.11-1) ... #7 71.66 Selecting previously unselected package libxxf86dga1:amd64. #7 71.66 Preparing to unpack .../128-libxxf86dga1_2%3a1.1.5-0ubuntu1_amd64.deb ... #7 71.66 Unpacking libxxf86dga1:amd64 (2:1.1.5-0ubuntu1) ... #7 71.73 Selecting previously unselected package llvm-10-runtime. #7 71.74 Preparing to unpack .../129-llvm-10-runtime_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 71.74 Unpacking llvm-10-runtime (1:10.0.0-4ubuntu1) ... #7 71.84 Selecting previously unselected package llvm-runtime. #7 71.84 Preparing to unpack .../130-llvm-runtime_1%3a10.0-50~exp1_amd64.deb ... #7 71.85 Unpacking llvm-runtime (1:10.0-50~exp1) ... #7 71.93 Selecting previously unselected package libpfm4:amd64. #7 71.93 Preparing to unpack .../131-libpfm4_4.10.1+git20-g7700f49-2_amd64.deb ... #7 71.94 Unpacking libpfm4:amd64 (4.10.1+git20-g7700f49-2) ... #7 72.05 Selecting previously unselected package llvm-10. #7 72.05 Preparing to unpack .../132-llvm-10_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 72.06 Unpacking llvm-10 (1:10.0.0-4ubuntu1) ... #7 72.62 Selecting previously unselected package llvm. #7 72.62 Preparing to unpack .../133-llvm_1%3a10.0-50~exp1_amd64.deb ... #7 72.63 Unpacking llvm (1:10.0-50~exp1) ... #7 72.71 Selecting previously unselected package libffi-dev:amd64. #7 72.71 Preparing to unpack .../134-libffi-dev_3.3-4_amd64.deb ... #7 72.72 Unpacking libffi-dev:amd64 (3.3-4) ... #7 72.81 Selecting previously unselected package python3-pygments. #7 72.81 Preparing to unpack .../135-python3-pygments_2.3.1+dfsg-1ubuntu2.2_all.deb ... #7 72.82 Unpacking python3-pygments (2.3.1+dfsg-1ubuntu2.2) ... #7 72.95 Selecting previously unselected package llvm-10-tools. #7 72.95 Preparing to unpack .../136-llvm-10-tools_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 72.96 Unpacking llvm-10-tools (1:10.0.0-4ubuntu1) ... #7 73.12 Selecting previously unselected package libz3-4:amd64. #7 73.12 Preparing to unpack .../137-libz3-4_4.8.7-4build1_amd64.deb ... #7 73.13 Unpacking libz3-4:amd64 (4.8.7-4build1) ... #7 73.78 Selecting previously unselected package libz3-dev:amd64. #7 73.78 Preparing to unpack .../138-libz3-dev_4.8.7-4build1_amd64.deb ... #7 73.79 Unpacking libz3-dev:amd64 (4.8.7-4build1) ... #7 73.88 Selecting previously unselected package llvm-10-dev. #7 73.89 Preparing to unpack .../139-llvm-10-dev_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 73.90 Unpacking llvm-10-dev (1:10.0.0-4ubuntu1) ... #7 76.60 Selecting previously unselected package mesa-vulkan-drivers:amd64. #7 76.60 Preparing to unpack .../140-mesa-vulkan-drivers_21.2.6-0ubuntu0.1~20.04.2_amd64.deb ... #7 76.61 Unpacking mesa-vulkan-drivers:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 77.24 Selecting previously unselected package tcl8.6. #7 77.24 Preparing to unpack .../141-tcl8.6_8.6.10+dfsg-1_amd64.deb ... #7 77.25 Unpacking tcl8.6 (8.6.10+dfsg-1) ... #7 77.32 Selecting previously unselected package tcl. #7 77.32 Preparing to unpack .../142-tcl_8.6.9+1_amd64.deb ... #7 77.34 Unpacking tcl (8.6.9+1) ... #7 77.40 Selecting previously unselected package tcl8.6-dev:amd64. #7 77.41 Preparing to unpack .../143-tcl8.6-dev_8.6.10+dfsg-1_amd64.deb ... #7 77.42 Unpacking tcl8.6-dev:amd64 (8.6.10+dfsg-1) ... #7 77.59 Selecting previously unselected package tcl-dev:amd64. #7 77.59 Preparing to unpack .../144-tcl-dev_8.6.9+1_amd64.deb ... #7 77.60 Unpacking tcl-dev:amd64 (8.6.9+1) ... #7 77.68 Selecting previously unselected package tk8.6. #7 77.69 Preparing to unpack .../145-tk8.6_8.6.10-1_amd64.deb ... #7 77.69 Unpacking tk8.6 (8.6.10-1) ... #7 77.76 Selecting previously unselected package tk. #7 77.77 Preparing to unpack .../146-tk_8.6.9+1_amd64.deb ... #7 77.78 Unpacking tk (8.6.9+1) ... #7 77.85 Selecting previously unselected package tk8.6-dev:amd64. #7 77.85 Preparing to unpack .../147-tk8.6-dev_8.6.10-1_amd64.deb ... #7 77.86 Unpacking tk8.6-dev:amd64 (8.6.10-1) ... #7 78.01 Selecting previously unselected package tk-dev:amd64. #7 78.01 Preparing to unpack .../148-tk-dev_8.6.9+1_amd64.deb ... #7 78.02 Unpacking tk-dev:amd64 (8.6.9+1) ... #7 78.10 Selecting previously unselected package libglvnd0:amd64. #7 78.10 Preparing to unpack .../149-libglvnd0_1.3.2-1~ubuntu0.20.04.2_amd64.deb ... #7 78.11 Unpacking libglvnd0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 78.21 Selecting previously unselected package libglx0:amd64. #7 78.21 Preparing to unpack .../150-libglx0_1.3.2-1~ubuntu0.20.04.2_amd64.deb ... #7 78.22 Unpacking libglx0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 78.31 Selecting previously unselected package libgl1:amd64. #7 78.31 Preparing to unpack .../151-libgl1_1.3.2-1~ubuntu0.20.04.2_amd64.deb ... #7 78.32 Unpacking libgl1:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 78.41 Selecting previously unselected package x11-utils. #7 78.42 Preparing to unpack .../152-x11-utils_7.7+5_amd64.deb ... #7 78.42 Unpacking x11-utils (7.7+5) ... #7 78.52 Selecting previously unselected package xbitmaps. #7 78.53 Preparing to unpack .../153-xbitmaps_1.1.1-2_all.deb ... #7 78.54 Unpacking xbitmaps (1.1.1-2) ... #7 78.63 Selecting previously unselected package xterm. #7 78.63 Preparing to unpack .../154-xterm_353-1ubuntu1.20.04.2_amd64.deb ... #7 78.64 Unpacking xterm (353-1ubuntu1.20.04.2) ... #7 78.78 Selecting previously unselected package liblzma-dev:amd64. #7 78.78 Preparing to unpack .../155-liblzma-dev_5.2.4-1ubuntu1.1_amd64.deb ... #7 78.79 Unpacking liblzma-dev:amd64 (5.2.4-1ubuntu1.1) ... #7 78.88 Selecting previously unselected package libtasn1-doc. #7 78.89 Preparing to unpack .../156-libtasn1-doc_4.16.0-2_all.deb ... #7 78.89 Unpacking libtasn1-doc (4.16.0-2) ... #7 79.03 Setting up libpipeline1:amd64 (1.5.2-2build1) ... #7 79.05 Setting up libxcb-dri3-0:amd64 (1.14-2) ... #7 79.08 Setting up bzip2-doc (1.0.8-2) ... #7 79.10 Setting up libx11-xcb1:amd64 (2:1.6.9-2ubuntu1.6) ... #7 79.12 Setting up libpciaccess0:amd64 (0.16-0ubuntu1) ... #7 79.14 Setting up libncurses-dev:amd64 (6.2-0ubuntu2.1) ... #7 79.16 Setting up libgnutls-openssl27:amd64 (3.6.13-2ubuntu1.11) ... #7 79.18 Setting up libxcb-xfixes0:amd64 (1.14-2) ... #7 79.20 Setting up mime-support (3.64ubuntu1) ... #7 79.27 Setting up libncursesw5-dev:amd64 (6.2-0ubuntu2.1) ... #7 79.29 Setting up libxrender1:amd64 (1:0.9.10-1) ... #7 79.31 Setting up xdg-user-dirs (0.17-2ubuntu1) ... #7 79.37 Setting up libpng-tools (1.6.37-2) ... #7 79.39 Setting up libmagic-mgc (1:5.38-4) ... #7 79.41 Setting up libyaml-0-2:amd64 (0.2.2-1) ... #7 79.44 Setting up libglib2.0-0:amd64 (2.64.6-1~ubuntu20.04.7) ... #7 79.46 No schema files found: doing nothing. #7 79.47 Setting up libglvnd0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 79.50 Setting up libxcb-glx0:amd64 (1.14-2) ... #7 79.52 Setting up libtasn1-doc (4.16.0-2) ... #7 79.54 Setting up libedit2:amd64 (3.1-20191231-1) ... #7 79.56 Setting up libxcb-shape0:amd64 (1.14-2) ... #7 79.59 Setting up x11-common (1:7.7+19ubuntu14) ... #7 79.84 update-rc.d: warning: start and stop actions are no longer supported; falling back to defaults #7 79.85 invoke-rc.d: could not determine current runlevel #7 79.86 invoke-rc.d: policy-rc.d denied execution of start. #7 79.87 Setting up libsensors-config (1:3.6.0-2ubuntu1.1) ... #7 79.91 Setting up libmagic1:amd64 (1:5.38-4) ... #7 79.95 Setting up libxext6:amd64 (2:1.3.4-0ubuntu1) ... #7 79.97 Setting up libxcb-shm0:amd64 (1.14-2) ... #7 79.99 Setting up file (1:5.38-4) ... #7 80.01 Setting up libffi-dev:amd64 (3.3-4) ... #7 80.04 Setting up libpthread-stubs0-dev:amd64 (0.4-1) ... #7 80.06 Setting up libxxf86vm1:amd64 (1:1.1.4-1build1) ... #7 80.08 Setting up libllvm10:amd64 (1:10.0.0-4ubuntu1) ... #7 80.10 Setting up libxcb-present0:amd64 (1.14-2) ... #7 80.12 Setting up xtrans-dev (1.4.0-1) ... #7 80.15 Setting up libfontenc1:amd64 (1:1.1.4-0ubuntu1) ... #7 80.17 Setting up libz3-4:amd64 (4.8.7-4build1) ... #7 80.19 Setting up libglib2.0-data (2.64.6-1~ubuntu20.04.7) ... #7 80.21 Setting up libgmpxx4ldbl:amd64 (2:6.2.0+dfsg-4ubuntu0.1) ... #7 80.24 Setting up libpfm4:amd64 (4.10.1+git20-g7700f49-2) ... #7 80.26 Setting up libexpat1-dev:amd64 (2.2.9-1ubuntu0.6) ... #7 80.28 Setting up libsqlite3-dev:amd64 (3.31.1-4ubuntu0.6) ... #7 80.30 Setting up libgpg-error-dev (1.37-1) ... #7 80.32 Setting up libnspr4:amd64 (2:4.35-0ubuntu0.20.04.1) ... #7 80.35 Setting up libxfixes3:amd64 (1:5.0.3-2) ... #7 80.37 Setting up libxcb-sync1:amd64 (1.14-2) ... #7 80.39 Setting up uuid-dev:amd64 (2.34-0.1ubuntu9.6) ... #7 80.41 Setting up libllvm12:amd64 (1:12.0.0-3ubuntu1~20.04.5) ... #7 80.44 Setting up shared-mime-info (1.15-1) ... #7 83.97 Setting up libxinerama1:amd64 (2:1.1.4-2) ... #7 83.99 Setting up libxv1:amd64 (2:1.0.11-1) ... #7 84.01 Setting up libssl-dev:amd64 (1.1.1f-1ubuntu2.22) ... #7 84.03 Setting up libevent-2.1-7:amd64 (2.1.11-stable-1) ... #7 84.06 Setting up libxrandr2:amd64 (2:1.5.2-0ubuntu1) ... #7 84.08 Setting up libtcl8.6:amd64 (8.6.10+dfsg-1) ... #7 84.10 Setting up binfmt-support (2.2.0-2) ... #7 84.34 invoke-rc.d: could not determine current runlevel #7 84.35 invoke-rc.d: policy-rc.d denied execution of start. #7 84.35 Setting up icu-devtools (66.1-2ubuntu2.1) ... #7 84.37 Setting up pkg-config (0.29.1-0ubuntu4) ... #7 84.45 Setting up libgnutlsxx28:amd64 (3.6.13-2ubuntu1.11) ... #7 84.47 Setting up libsensors5:amd64 (1:3.6.0-2ubuntu1.1) ... #7 84.49 Setting up libidn2-dev:amd64 (2.2.0-2) ... #7 84.51 Setting up libglapi-mesa:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 84.54 Setting up libvulkan1:amd64 (1.2.131.2-1) ... #7 84.56 Setting up libutempter0:amd64 (1.1.6-4) ... #7 84.58 Setting up liblzma-dev:amd64 (5.2.4-1ubuntu1.1) ... #7 84.60 Setting up libxcb-dri2-0:amd64 (1.14-2) ... #7 84.63 Setting up zlib1g-dev:amd64 (1:1.2.11.dfsg-2ubuntu1.5) ... #7 84.65 Setting up libxshmfence1:amd64 (1.3-1) ... #7 84.67 Setting up libxcb-randr0:amd64 (1.14-2) ... #7 84.70 Setting up xorg-sgml-doctools (1:1.11-1) ... #7 84.72 Setting up libxss1:amd64 (1:1.2.3-1) ... #7 84.74 Setting up libxkbfile1:amd64 (1:1.1.0-1) ... #7 84.76 Setting up libmpdec2:amd64 (2.4.2-3) ... #7 84.78 Setting up libxmlsec1:amd64 (1.2.28-2) ... #7 84.81 Setting up libtasn1-6-dev:amd64 (4.16.0-2) ... #7 84.83 Setting up libdrm-common (2.4.107-8ubuntu1~20.04.2) ... #7 84.85 Setting up libxcomposite1:amd64 (1:0.4.5-1) ... #7 84.90 Setting up readline-common (8.0-4) ... #7 84.93 Setting up libicu-dev:amd64 (66.1-2ubuntu2.1) ... #7 84.95 Setting up libxmuu1:amd64 (2:1.1.3-0ubuntu1) ... #7 84.98 Setting up xbitmaps (1.1.1-2) ... #7 85.00 Setting up libp11-kit-dev:amd64 (0.23.20-1ubuntu0.1) ... #7 85.02 Setting up libbz2-dev:amd64 (1.0.8-2) ... #7 85.04 Setting up libwayland-client0:amd64 (1.18.0-1ubuntu0.1) ... #7 85.06 Setting up x11proto-dev (2019.2-1ubuntu1) ... #7 85.08 Setting up libnspr4-dev (2:4.35-0ubuntu0.20.04.1) ... #7 85.11 Setting up libz3-dev:amd64 (4.8.7-4build1) ... #7 85.13 Setting up libice6:amd64 (2:1.0.10-0ubuntu1) ... #7 85.15 Setting up tcl8.6 (8.6.10+dfsg-1) ... #7 85.18 Setting up libxft2:amd64 (2.3.3-0ubuntu1) ... #7 85.20 Setting up libgmp-dev:amd64 (2:6.2.0+dfsg-4ubuntu0.1) ... #7 85.22 Setting up libxau-dev:amd64 (1:1.0.9-0ubuntu1) ... #7 85.24 Setting up libxi6:amd64 (2:1.7.10-0ubuntu1) ... #7 85.27 Setting up nettle-dev:amd64 (3.5.1+really3.5.1-2ubuntu0.2) ... #7 85.29 Setting up libice-dev:amd64 (2:1.0.10-0ubuntu1) ... #7 85.31 Setting up libxmlsec1-openssl:amd64 (1.2.28-2) ... #7 85.34 Setting up libxtst6:amd64 (2:1.2.3-1) ... #7 85.36 Setting up tcl8.6-dev:amd64 (8.6.10+dfsg-1) ... #7 85.38 Setting up libtk8.6:amd64 (8.6.10-1) ... #7 85.40 Setting up libreadline8:amd64 (8.0-4) ... #7 85.42 Setting up libpng-dev:amd64 (1.6.37-2) ... #7 85.45 Setting up libxxf86dga1:amd64 (2:1.1.5-0ubuntu1) ... #7 85.47 Setting up libnss3:amd64 (2:3.98-0ubuntu0.20.04.2) ... #7 85.49 Setting up libreadline-dev:amd64 (8.0-4) ... #7 85.51 Setting up libclang-cpp10 (1:10.0.0-4ubuntu1) ... #7 85.54 Setting up llvm-10-runtime (1:10.0.0-4ubuntu1) ... #7 85.56 Setting up libfreetype-dev:amd64 (2.10.1-2ubuntu0.3) ... #7 85.59 Setting up libxdmcp-dev:amd64 (1:1.1.3-0ubuntu1) ... #7 85.61 Setting up libxml2-dev:amd64 (2.9.10+dfsg-5ubuntu0.20.04.7) ... #7 85.65 Setting up libunbound8:amd64 (1.9.4-2ubuntu1.5) ... #7 85.67 Setting up libgcrypt20-dev (1.8.5-5ubuntu1.1) ... #7 85.69 Setting up x11proto-core-dev (2019.2-1ubuntu1) ... #7 85.71 Setting up llvm-runtime (1:10.0-50~exp1) ... #7 85.74 Setting up libxmlsec1-gcrypt:amd64 (1.2.28-2) ... #7 85.76 Setting up libxmlsec1-nss:amd64 (1.2.28-2) ... #7 85.78 Setting up x11proto-xext-dev (2019.2-1ubuntu1) ... #7 85.80 Setting up libdrm2:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 85.82 Setting up libnss3-dev:amd64 (2:3.98-0ubuntu0.20.04.2) ... #7 85.85 Setting up tcl (8.6.9+1) ... #7 85.87 Setting up libpython3.8-stdlib:amd64 (3.8.10-0ubuntu1~20.04.9) ... #7 85.89 Setting up python3.8 (3.8.10-0ubuntu1~20.04.9) ... #7 86.58 Setting up libxmlsec1-gnutls:amd64 (1.2.28-2) ... #7 86.61 Setting up x11proto-scrnsaver-dev (2019.2-1ubuntu1) ... #7 86.63 Setting up libsm6:amd64 (2:1.2.3-1) ... #7 86.65 Setting up libpython3-stdlib:amd64 (3.8.2-0ubuntu2) ... #7 86.67 Setting up libdrm-amdgpu1:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.69 Setting up tk8.6 (8.6.10-1) ... #7 86.72 Setting up mesa-vulkan-drivers:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 86.75 Setting up libgnutls-dane0:amd64 (3.6.13-2ubuntu1.11) ... #7 86.77 Setting up libdrm-nouveau2:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.79 Setting up libxcb1-dev:amd64 (1.14-2) ... #7 86.81 Setting up libxslt1-dev:amd64 (1.1.34-4ubuntu0.20.04.1) ... #7 86.84 Setting up libsm-dev:amd64 (2:1.2.3-1) ... #7 86.86 Setting up libdrm-radeon1:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.88 Setting up llvm-10 (1:10.0.0-4ubuntu1) ... #7 86.90 Setting up libdrm-intel1:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.93 Setting up libgl1-mesa-dri:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 86.96 Setting up libx11-dev:amd64 (2:1.6.9-2ubuntu1.6) ... #7 86.99 Setting up python3 (3.8.2-0ubuntu2) ... #7 87.18 Setting up libfreetype6-dev:amd64 (2.10.1-2ubuntu0.3) ... #7 87.21 Setting up python3-pygments (2.3.1+dfsg-1ubuntu2.2) ... #7 87.83 Setting up llvm (1:10.0-50~exp1) ... #7 87.85 Setting up libxt6:amd64 (1:1.1.5-1) ... #7 87.87 Setting up tcl-dev:amd64 (8.6.9+1) ... #7 87.90 Setting up libxext-dev:amd64 (2:1.3.4-0ubuntu1) ... #7 87.92 Setting up tk (8.6.9+1) ... #7 87.94 Setting up libxrender-dev:amd64 (1:0.9.10-1) ... #7 87.96 Setting up python3-pkg-resources (45.2.0-1ubuntu0.1) ... #7 88.28 Setting up libgnutls28-dev:amd64 (3.6.13-2ubuntu1.11) ... #7 88.31 Setting up libxmu6:amd64 (2:1.1.3-0ubuntu1) ... #7 88.33 Setting up libfontconfig1-dev:amd64 (2.13.1-2ubuntu3) ... #7 88.35 Setting up libglx-mesa0:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 88.37 Setting up libglx0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 88.40 Setting up python3-yaml (5.3.1-1ubuntu0.1) ... #7 88.69 Setting up libxaw7:amd64 (2:1.0.13-1) ... #7 88.73 Setting up libxss-dev:amd64 (1:1.2.3-1) ... #7 88.75 Setting up libgl1:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 88.77 Setting up libxt-dev:amd64 (1:1.1.5-1) ... #7 88.79 Setting up libxmlsec1-dev (1.2.28-2) ... #7 88.82 Setting up x11-utils (7.7+5) ... #7 88.93 Setting up xterm (353-1ubuntu1.20.04.2) ... #7 89.00 update-alternatives: using /usr/bin/xterm to provide /usr/bin/x-terminal-emulator (x-terminal-emulator) in auto mode #7 89.00 update-alternatives: warning: skip creation of /usr/share/man/man1/x-terminal-emulator.1.gz because associated file /usr/share/man/man1/xterm.1.gz (of link group x-terminal-emulator) doesn't exist #7 89.02 update-alternatives: using /usr/bin/lxterm to provide /usr/bin/x-terminal-emulator (x-terminal-emulator) in auto mode #7 89.02 update-alternatives: warning: skip creation of /usr/share/man/man1/x-terminal-emulator.1.gz because associated file /usr/share/man/man1/lxterm.1.gz (of link group x-terminal-emulator) doesn't exist #7 89.04 Setting up libxft-dev:amd64 (2.3.3-0ubuntu1) ... #7 89.06 Setting up llvm-10-tools (1:10.0.0-4ubuntu1) ... #7 89.08 Setting up llvm-10-dev (1:10.0.0-4ubuntu1) ... #7 89.10 Setting up tk8.6-dev:amd64 (8.6.10-1) ... #7 89.12 Setting up tk-dev:amd64 (8.6.9+1) ... #7 89.14 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #7 DONE 90.5s #8 [ 4/17] RUN git clone --depth 1 --branch $(git ls-remote --tags --sort=v:refname https://github.com/pyenv/pyenv.git | grep -o -E 'v[1-9]+(\\.[1-9]+)+$' | tail -1) https://github.com/pyenv/pyenv.git /root/.pyenv #8 0.857 Cloning into '/root/.pyenv'... #8 1.882 Note: switching to 'aabaa892438d05d96775042a129b1580fcd60105'. #8 1.882 #8 1.882 You are in 'detached HEAD' state. You can look around, make experimental #8 1.882 changes and commit them, and you can discard any commits you make in this #8 1.882 state without impacting any branches by switching back to a branch. #8 1.882 #8 1.882 If you want to create a new branch to retain commits you create, you may #8 1.882 do so (now or later) by using -c with the switch command. Example: #8 1.882 #8 1.882 git switch -c #8 1.882 #8 1.882 Or undo this operation with: #8 1.882 #8 1.882 git switch - #8 1.882 #8 1.882 Turn off this advice by setting config variable advice.detachedHead to false #8 1.882 #8 DONE 2.0s #9 [ 5/17] RUN apt install -y python3.8 python3.8-distutils && ln -s -f $(which python3.8) /usr/bin/python && wget https://bootstrap.pypa.io/get-pip.py -O /tmp/get-pip.py && python /tmp/get-pip.py #9 0.502 #9 0.502 WARNING: apt does not have a stable CLI interface. Use with caution in scripts. #9 0.502 #9 0.563 Reading package lists... #9 1.644 Building dependency tree... #9 1.808 Reading state information... #9 2.060 python3.8 is already the newest version (3.8.10-0ubuntu1~20.04.9). #9 2.060 python3.8 set to manually installed. #9 2.060 The following NEW packages will be installed: #9 2.060 python3-distutils python3-lib2to3 #9 2.331 0 upgraded, 2 newly installed, 0 to remove and 0 not upgraded. #9 2.331 Need to get 218 kB of archives. #9 2.331 After this operation, 2115 kB of additional disk space will be used. #9 2.331 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-lib2to3 all 3.8.10-0ubuntu1~20.04 [76.3 kB] #9 2.755 Get:2 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-distutils all 3.8.10-0ubuntu1~20.04 [141 kB] #9 3.105 debconf: delaying package configuration, since apt-utils is not installed #9 3.173 Fetched 218 kB in 1s (254 kB/s) #9 3.227 Selecting previously unselected package python3-lib2to3. #9 3.227 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 24296 files and directories currently installed.) #9 3.256 Preparing to unpack .../python3-lib2to3_3.8.10-0ubuntu1~20.04_all.deb ... #9 3.263 Unpacking python3-lib2to3 (3.8.10-0ubuntu1~20.04) ... #9 3.357 Selecting previously unselected package python3-distutils. #9 3.358 Preparing to unpack .../python3-distutils_3.8.10-0ubuntu1~20.04_all.deb ... #9 3.365 Unpacking python3-distutils (3.8.10-0ubuntu1~20.04) ... #9 3.486 Setting up python3-lib2to3 (3.8.10-0ubuntu1~20.04) ... #9 3.636 Setting up python3-distutils (3.8.10-0ubuntu1~20.04) ... #9 3.862 --2024-05-16 20:23:48-- https://bootstrap.pypa.io/get-pip.py #9 3.866 Resolving bootstrap.pypa.io (bootstrap.pypa.io)... 151.101.64.175, 151.101.128.175, 151.101.0.175, ... #9 3.930 Connecting to bootstrap.pypa.io (bootstrap.pypa.io)|151.101.64.175|:443... connected. #9 4.180 HTTP request sent, awaiting response... 200 OK #9 4.192 Length: 2635835 (2.5M) [text/x-python] #9 4.192 Saving to: '/tmp/get-pip.py' #9 4.206 #9 4.206 0K .......... .......... .......... .......... .......... 1% 968K 3s #9 4.244 50K .......... .......... .......... .......... .......... 3% 4.78M 2s #9 4.254 100K .......... .......... .......... .......... .......... 5% 7.08M 1s #9 4.261 150K .......... .......... .......... .......... .......... 7% 7.48M 1s #9 4.267 200K .......... .......... .......... .......... .......... 9% 6.58M 1s #9 4.275 250K .......... .......... .......... .......... .......... 11% 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Collecting setuptools #9 6.909 Downloading setuptools-69.5.1-py3-none-any.whl.metadata (6.2 kB) #9 6.992 Collecting wheel #9 7.007 Downloading wheel-0.43.0-py3-none-any.whl.metadata (2.2 kB) #9 7.077 Downloading pip-24.0-py3-none-any.whl (2.1 MB) #9 7.224 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 2.1/2.1 MB 15.0 MB/s eta 0:00:00 #9 7.243 Downloading setuptools-69.5.1-py3-none-any.whl (894 kB) #9 7.297 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 894.6/894.6 kB 18.5 MB/s eta 0:00:00 #9 7.315 Downloading wheel-0.43.0-py3-none-any.whl (65 kB) #9 7.331 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 65.8/65.8 kB 4.8 MB/s eta 0:00:00 #9 7.407 Installing collected packages: wheel, setuptools, pip #9 9.087 Successfully installed pip-24.0 setuptools-69.5.1 wheel-0.43.0 #9 9.088 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #9 DONE 9.6s #10 [ 6/17] RUN pip install virtualenv #10 1.047 Collecting virtualenv #10 1.095 Downloading virtualenv-20.26.2-py3-none-any.whl.metadata (4.4 kB) #10 1.182 Collecting distlib < 1,>=0.3.7 (from virtualenv) #10 1.197 Downloading distlib-0.3.8-py2.py3-none-any.whl.metadata (5.1 kB) #10 1.276 Collecting filelock < 4,>=3.12.2 (from virtualenv) #10 1.291 Downloading filelock-3.14.0-py3-none-any.whl.metadata (2.8 kB) #10 1.368 Collecting platformdirs < 5,>=3.9.1 (from virtualenv) #10 1.383 Downloading platformdirs-4.2.2-py3-none-any.whl.metadata (11 kB) #10 1.439 Downloading virtualenv-20.26.2-py3-none-any.whl (3.9 MB) #10 1.656 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.9/3.9 MB 18.3 MB/s eta 0:00:00 #10 1.673 Downloading distlib-0.3.8-py2.py3-none-any.whl (468 kB) #10 1.706 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 468.9/468.9 kB 17.0 MB/s eta 0:00:00 #10 1.724 Downloading filelock-3.14.0-py3-none-any.whl (12 kB) #10 1.752 Downloading platformdirs-4.2.2-py3-none-any.whl (18 kB) #10 1.871 Installing collected packages: distlib, platformdirs, filelock, virtualenv #10 2.123 Successfully installed distlib-0.3.8 filelock-3.14.0 platformdirs-4.2.2 virtualenv-20.26.2 #10 2.123 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #10 DONE 2.3s #11 [ 7/17] RUN apt-get install -y --no-install-recommends openjdk-11-jdk maven #11 0.605 Reading package lists... #11 1.699 Building dependency tree... #11 1.870 Reading state information... #11 2.070 The following additional packages will be installed: #11 2.070 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.070 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.070 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.070 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.070 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.070 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.070 libguice-java libharfbuzz0b libhawtjni-runtime-java libjansi-java #11 2.070 libjansi-native-java libjsr305-java liblcms2-2 libmaven-parent-java #11 2.070 libmaven-resolver-java libmaven-shared-utils-java libmaven3-core-java #11 2.070 libpcsclite1 libplexus-cipher-java libplexus-classworlds-java #11 2.070 libplexus-component-annotations-java libplexus-interpolation-java #11 2.070 libplexus-sec-dispatcher-java libplexus-utils2-java libsisu-inject-java #11 2.070 libsisu-plexus-java libslf4j-java libwagon-file-java #11 2.071 libwagon-http-shaded-java libwagon-provider-api-java openjdk-11-jdk-headless #11 2.071 openjdk-11-jre openjdk-11-jre-headless #11 2.072 Suggested packages: #11 2.072 default-jre libaopalliance-java-doc libasound2-plugins alsa-utils #11 2.072 libatinject-jsr330-api-java-doc libservlet3.1-java libcommons-io-java-doc #11 2.072 libcommons-lang3-java-doc cups-common libasm-java libcglib-java #11 2.072 libjsr305-java-doc liblcms2-utils libmaven-shared-utils-java-doc #11 2.072 liblogback-java pcscd libplexus-cipher-java-doc #11 2.072 libplexus-classworlds-java-doc libplexus-sec-dispatcher-java-doc #11 2.072 libplexus-utils2-java-doc junit4 testng libcommons-logging-java #11 2.072 liblog4j1.2-java openjdk-11-demo openjdk-11-source visualvm libnss-mdns #11 2.072 fonts-dejavu-extra fonts-ipafont-gothic fonts-ipafont-mincho #11 2.072 fonts-wqy-microhei | fonts-wqy-zenhei fonts-indic #11 2.072 Recommended packages: #11 2.072 alsa-ucm-conf alsa-topology-conf dbus libatk-wrapper-java-jni #11 2.072 fonts-dejavu-extra #11 2.267 The following NEW packages will be installed: #11 2.268 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.268 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.268 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.268 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.268 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.268 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.268 libguice-java libharfbuzz0b libhawtjni-runtime-java libjansi-java #11 2.268 libjansi-native-java libjsr305-java liblcms2-2 libmaven-parent-java #11 2.268 libmaven-resolver-java libmaven-shared-utils-java libmaven3-core-java #11 2.268 libpcsclite1 libplexus-cipher-java libplexus-classworlds-java #11 2.268 libplexus-component-annotations-java libplexus-interpolation-java #11 2.268 libplexus-sec-dispatcher-java libplexus-utils2-java libsisu-inject-java #11 2.268 libsisu-plexus-java libslf4j-java libwagon-file-java #11 2.269 libwagon-http-shaded-java libwagon-provider-api-java maven openjdk-11-jdk #11 2.269 openjdk-11-jdk-headless openjdk-11-jre openjdk-11-jre-headless #11 2.386 0 upgraded, 51 newly installed, 0 to remove and 0 not upgraded. #11 2.386 Need to get 125 MB of archives. #11 2.386 After this operation, 277 MB of additional disk space will be used. #11 2.386 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdbus-1-3 amd64 1.12.16-2ubuntu2.3 [179 kB] #11 2.602 Get:2 http://archive.ubuntu.com/ubuntu focal/main amd64 java-common all 0.72 [6816 B] #11 2.603 Get:3 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libavahi-common-data amd64 0.7-4ubuntu7.3 [21.4 kB] #11 2.607 Get:4 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libavahi-common3 amd64 0.7-4ubuntu7.3 [21.9 kB] #11 2.610 Get:5 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libavahi-client3 amd64 0.7-4ubuntu7.3 [25.5 kB] #11 2.615 Get:6 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcups2 amd64 2.3.1-9ubuntu1.6 [233 kB] #11 2.667 Get:7 http://archive.ubuntu.com/ubuntu focal/main amd64 liblcms2-2 amd64 2.9-4 [140 kB] #11 2.690 Get:8 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libasound2-data all 1.2.2-2.1ubuntu2.5 [20.1 kB] #11 2.694 Get:9 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libasound2 amd64 1.2.2-2.1ubuntu2.5 [335 kB] #11 2.753 Get:10 http://archive.ubuntu.com/ubuntu focal/main amd64 libgraphite2-3 amd64 1.3.13-11build1 [73.5 kB] #11 2.765 Get:11 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libharfbuzz0b amd64 2.6.4-1ubuntu4.2 [391 kB] #11 2.928 Get:12 http://archive.ubuntu.com/ubuntu focal/main amd64 libpcsclite1 amd64 1.8.26-3 [22.0 kB] #11 3.024 Get:13 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jre-headless amd64 11.0.22+7-0ubuntu2~20.04.1 [38.3 MB] #11 10.09 Get:14 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 ca-certificates-java all 20190405ubuntu1.1 [12.4 kB] #11 10.10 Get:15 http://archive.ubuntu.com/ubuntu focal/universe amd64 libapache-pom-java all 18-1 [4720 B] #11 10.10 Get:16 http://archive.ubuntu.com/ubuntu focal/universe amd64 libatinject-jsr330-api-java all 1.0+ds1-5 [5348 B] #11 10.10 Get:17 http://archive.ubuntu.com/ubuntu focal/universe amd64 libgeronimo-interceptor-3.0-spec-java all 1.0.1-4fakesync [8616 B] #11 10.10 Get:18 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcdi-api-java all 1.2-2 [54.5 kB] #11 10.10 Get:19 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcommons-cli-java all 1.4-1 [53.8 kB] #11 10.10 Get:20 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcommons-parent-java all 43-1 [10.8 kB] #11 10.10 Get:21 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libcommons-io-java all 2.6-2ubuntu0.20.04.1 [198 kB] #11 10.11 Get:22 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcommons-lang3-java all 3.8-2 [458 kB] #11 10.28 Get:23 http://archive.ubuntu.com/ubuntu focal/universe amd64 libgeronimo-annotation-1.3-spec-java all 1.0-1 [10.7 kB] #11 10.33 Get:24 http://archive.ubuntu.com/ubuntu focal/main amd64 libgif7 amd64 5.1.9-1 [32.2 kB] #11 10.38 Get:25 http://archive.ubuntu.com/ubuntu focal/universe amd64 libjsr305-java all 0.1~+svn49-11 [27.0 kB] #11 10.41 Get:26 http://archive.ubuntu.com/ubuntu focal/universe amd64 libguava-java all 19.0-1 [2028 kB] #11 10.83 Get:27 http://archive.ubuntu.com/ubuntu focal/universe amd64 libaopalliance-java all 20070526-6 [9084 B] #11 10.83 Get:28 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libguice-java all 4.2.1-1ubuntu0.2 [1410 kB] #11 11.08 Get:29 http://archive.ubuntu.com/ubuntu focal/universe amd64 libhawtjni-runtime-java all 1.17-1 [28.8 kB] #11 11.08 Get:30 http://archive.ubuntu.com/ubuntu focal/universe amd64 libjansi-native-java all 1.8-1 [23.8 kB] #11 11.08 Get:31 http://archive.ubuntu.com/ubuntu focal/universe amd64 libjansi-java all 1.18-1 [56.8 kB] #11 11.09 Get:32 http://archive.ubuntu.com/ubuntu focal/universe amd64 libmaven-parent-java all 31-2 [5140 B] #11 11.09 Get:33 http://archive.ubuntu.com/ubuntu focal/universe amd64 libplexus-utils2-java all 3.3.0-1 [250 kB] #11 11.23 Get:34 http://archive.ubuntu.com/ubuntu focal/universe amd64 libwagon-provider-api-java all 3.3.4-1 [48.5 kB] #11 11.36 Get:35 http://archive.ubuntu.com/ubuntu focal/universe amd64 libmaven-resolver-java all 1.4.1-1 [555 kB] #11 11.52 Get:36 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focal/universe amd64 libsisu-plexus-java all 0.3.3-3 [182 kB] #11 11.79 Get:45 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libmaven3-core-java all 3.6.3-1ubuntu0.1 [1535 kB] #11 12.26 Get:46 http://archive.ubuntu.com/ubuntu focal/universe amd64 libwagon-file-java all 3.3.4-1 [7928 B] #11 12.26 Get:47 http://archive.ubuntu.com/ubuntu focal/universe amd64 libwagon-http-shaded-java all 3.3.4-1 [1855 kB] #11 12.59 Get:48 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 maven all 3.6.3-1ubuntu0.1 [17.8 kB] #11 12.60 Get:49 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jre amd64 11.0.22+7-0ubuntu2~20.04.1 [194 kB] #11 12.63 Get:50 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jdk-headless amd64 11.0.22+7-0ubuntu2~20.04.1 [73.7 MB] #11 25.68 Get:51 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jdk amd64 11.0.22+7-0ubuntu2~20.04.1 [1338 kB] #11 26.04 debconf: delaying package configuration, since apt-utils is not installed #11 26.11 Fetched 125 MB in 24s (5286 kB/s) #11 26.18 Selecting previously unselected package libdbus-1-3:amd64. #11 26.18 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 24567 files and directories currently installed.) #11 26.21 Preparing to unpack .../00-libdbus-1-3_1.12.16-2ubuntu2.3_amd64.deb ... #11 26.22 Unpacking libdbus-1-3:amd64 (1.12.16-2ubuntu2.3) ... #11 26.33 Selecting previously unselected package java-common. #11 26.33 Preparing to unpack .../01-java-common_0.72_all.deb ... #11 26.34 Unpacking java-common (0.72) ... #11 26.40 Selecting previously unselected package libavahi-common-data:amd64. #11 26.41 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previously unselected package libasound2-data. #11 26.91 Preparing to unpack .../07-libasound2-data_1.2.2-2.1ubuntu2.5_all.deb ... #11 26.91 Unpacking libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 27.01 Selecting previously unselected package libasound2:amd64. #11 27.01 Preparing to unpack .../08-libasound2_1.2.2-2.1ubuntu2.5_amd64.deb ... #11 27.02 Unpacking libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 27.14 Selecting previously unselected package libgraphite2-3:amd64. #11 27.14 Preparing to unpack .../09-libgraphite2-3_1.3.13-11build1_amd64.deb ... #11 27.15 Unpacking libgraphite2-3:amd64 (1.3.13-11build1) ... #11 27.24 Selecting previously unselected package libharfbuzz0b:amd64. #11 27.24 Preparing to unpack .../10-libharfbuzz0b_2.6.4-1ubuntu4.2_amd64.deb ... #11 27.25 Unpacking libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 27.37 Selecting previously unselected package libpcsclite1:amd64. #11 27.38 Preparing to unpack .../11-libpcsclite1_1.8.26-3_amd64.deb ... #11 27.38 Unpacking libpcsclite1:amd64 (1.8.26-3) ... #11 27.47 Selecting previously unselected package openjdk-11-jre-headless:amd64. #11 27.47 Preparing to unpack .../12-openjdk-11-jre-headless_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 27.48 Unpacking openjdk-11-jre-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 31.00 Selecting previously unselected package ca-certificates-java. #11 31.00 Preparing to unpack .../13-ca-certificates-java_20190405ubuntu1.1_all.deb ... #11 31.02 Unpacking ca-certificates-java (20190405ubuntu1.1) ... #11 31.08 Selecting previously unselected package libapache-pom-java. #11 31.09 Preparing to unpack .../14-libapache-pom-java_18-1_all.deb ... #11 31.10 Unpacking libapache-pom-java (18-1) ... #11 31.16 Selecting previously unselected package libatinject-jsr330-api-java. #11 31.16 Preparing to unpack .../15-libatinject-jsr330-api-java_1.0+ds1-5_all.deb ... #11 31.17 Unpacking libatinject-jsr330-api-java (1.0+ds1-5) ... #11 31.23 Selecting previously unselected package libgeronimo-interceptor-3.0-spec-java. #11 31.24 Preparing to unpack .../16-libgeronimo-interceptor-3.0-spec-java_1.0.1-4fakesync_all.deb ... #11 31.25 Unpacking libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 31.31 Selecting previously unselected package libcdi-api-java. #11 31.31 Preparing to unpack .../17-libcdi-api-java_1.2-2_all.deb ... #11 31.32 Unpacking libcdi-api-java (1.2-2) ... #11 31.39 Selecting previously unselected package libcommons-cli-java. #11 31.39 Preparing to unpack .../18-libcommons-cli-java_1.4-1_all.deb ... #11 31.40 Unpacking libcommons-cli-java (1.4-1) ... #11 31.47 Selecting previously unselected package libcommons-parent-java. #11 31.48 Preparing to unpack .../19-libcommons-parent-java_43-1_all.deb ... #11 31.49 Unpacking libcommons-parent-java (43-1) ... #11 31.55 Selecting previously unselected package libcommons-io-java. #11 31.56 Preparing to unpack .../20-libcommons-io-java_2.6-2ubuntu0.20.04.1_all.deb ... #11 31.56 Unpacking libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 31.66 Selecting previously unselected package libcommons-lang3-java. #11 31.66 Preparing to unpack .../21-libcommons-lang3-java_3.8-2_all.deb ... #11 31.67 Unpacking libcommons-lang3-java (3.8-2) ... #11 31.78 Selecting previously unselected package libgeronimo-annotation-1.3-spec-java. #11 31.78 Preparing to unpack .../22-libgeronimo-annotation-1.3-spec-java_1.0-1_all.deb ... #11 31.79 Unpacking libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 31.86 Selecting previously unselected package libgif7:amd64. #11 31.87 Preparing to unpack .../23-libgif7_5.1.9-1_amd64.deb ... #11 31.88 Unpacking libgif7:amd64 (5.1.9-1) ... #11 31.95 Selecting previously unselected package libjsr305-java. #11 31.95 Preparing to unpack .../24-libjsr305-java_0.1~+svn49-11_all.deb ... #11 31.96 Unpacking libjsr305-java (0.1~+svn49-11) ... #11 32.03 Selecting previously unselected package libguava-java. #11 32.03 Preparing to unpack .../25-libguava-java_19.0-1_all.deb ... #11 32.04 Unpacking libguava-java (19.0-1) ... #11 32.29 Selecting previously unselected package libaopalliance-java. #11 32.29 Preparing to unpack .../26-libaopalliance-java_20070526-6_all.deb ... #11 32.30 Unpacking libaopalliance-java (20070526-6) ... #11 32.36 Selecting previously unselected package libguice-java. #11 32.37 Preparing to unpack .../27-libguice-java_4.2.1-1ubuntu0.2_all.deb ... #11 32.37 Unpacking libguice-java (4.2.1-1ubuntu0.2) ... #11 32.59 Selecting previously unselected package libhawtjni-runtime-java. #11 32.60 Preparing to unpack .../28-libhawtjni-runtime-java_1.17-1_all.deb ... #11 32.60 Unpacking libhawtjni-runtime-java (1.17-1) ... #11 32.67 Selecting previously unselected package libjansi-native-java. #11 32.68 Preparing to unpack .../29-libjansi-native-java_1.8-1_all.deb ... #11 32.68 Unpacking libjansi-native-java (1.8-1) ... #11 32.75 Selecting previously unselected package libjansi-java. #11 32.75 Preparing to unpack .../30-libjansi-java_1.18-1_all.deb ... #11 32.76 Unpacking libjansi-java (1.18-1) ... #11 32.83 Selecting previously unselected package libmaven-parent-java. #11 32.84 Preparing to unpack .../31-libmaven-parent-java_31-2_all.deb ... #11 32.85 Unpacking libmaven-parent-java (31-2) ... #11 32.91 Selecting previously unselected package libplexus-utils2-java. #11 32.91 Preparing to unpack .../32-libplexus-utils2-java_3.3.0-1_all.deb ... #11 32.92 Unpacking libplexus-utils2-java (3.3.0-1) ... #11 33.02 Selecting previously unselected package libwagon-provider-api-java. #11 33.02 Preparing to unpack .../33-libwagon-provider-api-java_3.3.4-1_all.deb ... #11 33.03 Unpacking libwagon-provider-api-java (3.3.4-1) ... #11 33.10 Selecting previously unselected package libmaven-resolver-java. #11 33.11 Preparing to unpack .../34-libmaven-resolver-java_1.4.1-1_all.deb ... #11 33.12 Unpacking libmaven-resolver-java (1.4.1-1) ... #11 33.26 Selecting previously unselected package libmaven-shared-utils-java. #11 33.27 Preparing to unpack .../35-libmaven-shared-utils-java_3.3.0-1ubuntu0.20.04.1_all.deb ... #11 33.27 Unpacking libmaven-shared-utils-java (3.3.0-1ubuntu0.20.04.1) ... #11 33.36 Selecting previously unselected package libplexus-cipher-java. #11 33.37 Preparing to unpack .../36-libplexus-cipher-java_1.7-3_all.deb ... #11 33.37 Unpacking libplexus-cipher-java (1.7-3) ... #11 33.44 Selecting previously unselected package libplexus-classworlds-java. #11 33.45 Preparing to unpack .../37-libplexus-classworlds-java_2.6.0-1_all.deb ... #11 33.45 Unpacking libplexus-classworlds-java (2.6.0-1) ... #11 33.53 Selecting previously unselected package libplexus-component-annotations-java. #11 33.53 Preparing to unpack .../38-libplexus-component-annotations-java_2.1.0-1_all.deb ... #11 33.54 Unpacking libplexus-component-annotations-java (2.1.0-1) ... #11 33.60 Selecting previously unselected package libplexus-interpolation-java. #11 33.60 Preparing to unpack .../39-libplexus-interpolation-java_1.26-1_all.deb ... #11 33.61 Unpacking libplexus-interpolation-java (1.26-1) ... #11 33.69 Selecting previously unselected package libplexus-sec-dispatcher-java. #11 33.70 Preparing to unpack .../40-libplexus-sec-dispatcher-java_1.4-4_all.deb ... #11 33.70 Unpacking libplexus-sec-dispatcher-java (1.4-4) ... #11 33.77 Selecting previously unselected package libslf4j-java. #11 33.77 Preparing to unpack .../41-libslf4j-java_1.7.25-3_all.deb ... #11 33.78 Unpacking libslf4j-java (1.7.25-3) ... #11 33.89 Selecting previously unselected package libsisu-inject-java. #11 33.89 Preparing to unpack .../42-libsisu-inject-java_0.3.3-1_all.deb ... #11 33.90 Unpacking libsisu-inject-java (0.3.3-1) ... #11 34.01 Selecting previously unselected package libsisu-plexus-java. #11 34.02 Preparing to unpack .../43-libsisu-plexus-java_0.3.3-3_all.deb ... #11 34.02 Unpacking libsisu-plexus-java (0.3.3-3) ... #11 34.12 Selecting previously unselected package 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(11.0.22+7-0ubuntu2~20.04.1) ... #11 34.88 Selecting previously unselected package openjdk-11-jdk-headless:amd64. #11 34.89 Preparing to unpack .../49-openjdk-11-jdk-headless_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 34.89 Unpacking openjdk-11-jdk-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 39.97 Selecting previously unselected package openjdk-11-jdk:amd64. #11 39.97 Preparing to unpack .../50-openjdk-11-jdk_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 39.98 Unpacking openjdk-11-jdk:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 40.14 Setting up libgraphite2-3:amd64 (1.3.13-11build1) ... #11 40.16 Setting up liblcms2-2:amd64 (2.9-4) ... #11 40.18 Setting up libslf4j-java (1.7.25-3) ... #11 40.20 Setting up libplexus-utils2-java (3.3.0-1) ... #11 40.22 Setting up libplexus-classworlds-java (2.6.0-1) ... #11 40.24 Setting up libjsr305-java (0.1~+svn49-11) ... #11 40.26 Setting up java-common (0.72) ... #11 40.30 Setting up libaopalliance-java (20070526-6) ... #11 40.32 Setting up libcommons-cli-java (1.4-1) ... #11 40.34 Setting up libplexus-component-annotations-java (2.1.0-1) ... #11 40.38 Setting up libplexus-cipher-java (1.7-3) ... #11 40.40 Setting up libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 40.42 Setting up libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 40.44 Setting up libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 40.46 Setting up libapache-pom-java (18-1) ... #11 40.48 Setting up libavahi-common-data:amd64 (0.7-4ubuntu7.3) ... #11 40.50 Setting up libatinject-jsr330-api-java (1.0+ds1-5) ... #11 40.53 Setting up libdbus-1-3:amd64 (1.12.16-2ubuntu2.3) ... #11 40.55 Setting up libplexus-interpolation-java (1.26-1) ... #11 40.57 Setting up libpcsclite1:amd64 (1.8.26-3) ... #11 40.59 Setting up libgif7:amd64 (5.1.9-1) ... #11 40.61 Setting up libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 40.63 Setting up libplexus-sec-dispatcher-java (1.4-4) ... #11 40.65 Setting up libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 40.67 Setting up libwagon-http-shaded-java (3.3.4-1) ... #11 40.69 Setting up libcdi-api-java (1.2-2) ... #11 40.71 Setting up libhawtjni-runtime-java (1.17-1) ... #11 40.73 Setting up libwagon-provider-api-java (3.3.4-1) ... #11 40.75 Setting up libmaven-parent-java (31-2) ... #11 40.77 Setting up libcommons-parent-java (43-1) ... #11 40.79 Setting up libavahi-common3:amd64 (0.7-4ubuntu7.3) ... #11 40.81 Setting up libmaven-resolver-java (1.4.1-1) ... #11 40.84 Setting up libguava-java (19.0-1) ... #11 40.86 Setting up libcommons-lang3-java (3.8-2) ... #11 40.88 Setting up libjansi-native-java (1.8-1) ... #11 40.90 Setting up libwagon-file-java (3.3.4-1) ... #11 40.92 Setting up libavahi-client3:amd64 (0.7-4ubuntu7.3) ... #11 40.94 Setting up libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 40.96 Setting up libguice-java (4.2.1-1ubuntu0.2) ... #11 40.98 Setting up libjansi-java (1.18-1) ... #11 41.00 Setting up libcups2:amd64 (2.3.1-9ubuntu1.6) ... #11 41.02 Setting up libmaven-shared-utils-java (3.3.0-1ubuntu0.20.04.1) ... #11 41.04 Setting up libsisu-inject-java (0.3.3-1) ... #11 41.06 Setting up libsisu-plexus-java (0.3.3-3) ... #11 41.08 Setting up libmaven3-core-java (3.6.3-1ubuntu0.1) ... #11 41.11 Setting up openjdk-11-jre-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.31 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/java to provide /usr/bin/java (java) in auto mode #11 41.32 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jjs to provide /usr/bin/jjs (jjs) in auto mode #11 41.33 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/keytool to provide /usr/bin/keytool (keytool) in auto mode #11 41.34 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmid to provide /usr/bin/rmid (rmid) in auto mode #11 41.34 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmiregistry to provide /usr/bin/rmiregistry (rmiregistry) in auto mode #11 41.35 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/pack200 to provide /usr/bin/pack200 (pack200) in auto mode #11 41.36 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/unpack200 to provide /usr/bin/unpack200 (unpack200) in auto mode #11 41.37 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/lib/jexec to provide /usr/bin/jexec (jexec) in auto mode #11 41.92 Setting up openjdk-11-jre:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.95 Setting up openjdk-11-jdk-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.98 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jar to provide /usr/bin/jar (jar) in auto mode #11 41.99 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jarsigner to provide /usr/bin/jarsigner (jarsigner) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javac to provide /usr/bin/javac (javac) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javadoc to provide /usr/bin/javadoc (javadoc) in auto mode #11 42.01 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javap to provide /usr/bin/javap (javap) in auto mode #11 42.02 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jcmd to provide /usr/bin/jcmd (jcmd) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdb to provide /usr/bin/jdb (jdb) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeprscan to provide /usr/bin/jdeprscan (jdeprscan) in auto mode #11 42.04 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeps to provide /usr/bin/jdeps (jdeps) in auto mode #11 42.05 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jfr to provide /usr/bin/jfr (jfr) in auto mode #11 42.06 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jimage to provide /usr/bin/jimage (jimage) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jinfo to provide /usr/bin/jinfo (jinfo) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jlink to provide /usr/bin/jlink (jlink) in auto mode #11 42.08 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmap to provide /usr/bin/jmap (jmap) in auto mode #11 42.09 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmod to provide /usr/bin/jmod (jmod) in auto mode #11 42.10 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jps to provide /usr/bin/jps (jps) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jrunscript to provide /usr/bin/jrunscript (jrunscript) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jshell to provide /usr/bin/jshell (jshell) in auto mode #11 42.12 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstack to provide /usr/bin/jstack (jstack) in auto mode #11 42.13 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstat to provide /usr/bin/jstat (jstat) in auto mode #11 42.14 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstatd to provide /usr/bin/jstatd (jstatd) in auto mode #11 42.15 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmic to provide /usr/bin/rmic (rmic) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/serialver to provide /usr/bin/serialver (serialver) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jaotc to provide /usr/bin/jaotc (jaotc) in auto mode #11 42.17 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jhsdb to provide /usr/bin/jhsdb (jhsdb) in auto mode #11 42.19 Setting up maven (3.6.3-1ubuntu0.1) ... #11 42.23 update-alternatives: using /usr/share/maven/bin/mvn to provide /usr/bin/mvn (mvn) in auto mode #11 42.25 Setting up openjdk-11-jdk:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 42.28 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jconsole to provide /usr/bin/jconsole (jconsole) in auto mode #11 42.29 Setting up ca-certificates-java (20190405ubuntu1.1) ... #11 42.35 head: cannot open '/etc/ssl/certs/java/cacerts' for reading: No such file or directory #11 42.65 Adding debian:COMODO_RSA_Certification_Authority.pem #11 42.66 Adding debian:Hongkong_Post_Root_CA_1.pem #11 42.67 Adding debian:Entrust_Root_Certification_Authority_-_EC1.pem #11 42.67 Adding debian:Certainly_Root_R1.pem #11 42.68 Adding debian:Amazon_Root_CA_2.pem #11 42.68 Adding debian:Certainly_Root_E1.pem #11 42.68 Adding debian:AffirmTrust_Premium_ECC.pem #11 42.68 Adding debian:ANF_Secure_Server_Root_CA.pem #11 42.68 Adding debian:GlobalSign_Root_R46.pem #11 42.69 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068.pem #11 42.69 Adding debian:Certum_Trusted_Root_CA.pem #11 42.69 Adding debian:Go_Daddy_Root_Certificate_Authority_-_G2.pem #11 42.69 Adding debian:SecureSign_RootCA11.pem #11 42.69 Adding debian:Amazon_Root_CA_4.pem #11 42.70 Adding debian:GDCA_TrustAUTH_R5_ROOT.pem #11 42.70 Adding debian:T-TeleSec_GlobalRoot_Class_2.pem #11 42.70 Adding debian:Trustwave_Global_ECC_P256_Certification_Authority.pem #11 42.70 Adding debian:GTS_Root_R1.pem #11 42.71 Adding debian:Izenpe.com.pem #11 42.71 Adding debian:CFCA_EV_ROOT.pem #11 42.71 Adding debian:DigiCert_Global_Root_G2.pem #11 42.71 Adding debian:XRamp_Global_CA_Root.pem #11 42.72 Adding debian:SSL.com_EV_Root_Certification_Authority_RSA_R2.pem #11 42.72 Adding debian:Security_Communication_RootCA3.pem #11 42.72 Adding debian:Starfield_Services_Root_Certificate_Authority_-_G2.pem #11 42.72 Adding debian:Amazon_Root_CA_1.pem #11 42.72 Adding debian:DigiCert_Assured_ID_Root_CA.pem #11 42.72 Adding debian:SSL.com_Root_Certification_Authority_RSA.pem #11 42.73 Adding debian:Telia_Root_CA_v2.pem #11 42.73 Adding debian:SwissSign_Gold_CA_-_G2.pem #11 42.73 Adding debian:GLOBALTRUST_2020.pem #11 42.73 Adding debian:emSign_ECC_Root_CA_-_G3.pem #11 42.73 Adding debian:Microsoft_RSA_Root_Certificate_Authority_2017.pem #11 42.74 Adding debian:TunTrust_Root_CA.pem #11 42.74 Adding debian:Security_Communication_ECC_RootCA1.pem #11 42.74 Adding debian:DigiCert_TLS_ECC_P384_Root_G5.pem #11 42.74 Adding debian:vTrus_Root_CA.pem #11 42.75 Adding debian:Certum_EC-384_CA.pem #11 42.76 Adding debian:CA_Disig_Root_R2.pem #11 42.76 Adding debian:emSign_Root_CA_-_C1.pem #11 42.77 Adding debian:AffirmTrust_Commercial.pem #11 42.77 Adding debian:Entrust_Root_Certification_Authority_-_G4.pem #11 42.78 Adding debian:Starfield_Class_2_CA.pem #11 42.78 Adding debian:Baltimore_CyberTrust_Root.pem #11 42.79 Adding debian:GTS_Root_R4.pem #11 42.79 Adding debian:Starfield_Root_Certificate_Authority_-_G2.pem #11 42.80 Adding debian:QuoVadis_Root_CA_1_G3.pem #11 42.81 Adding debian:DigiCert_Trusted_Root_G4.pem #11 42.81 Adding debian:DigiCert_High_Assurance_EV_Root_CA.pem #11 42.82 Adding debian:AC_RAIZ_FNMT-RCM.pem #11 42.82 Adding debian:Comodo_AAA_Services_root.pem #11 42.82 Adding debian:emSign_ECC_Root_CA_-_C3.pem #11 42.83 Adding debian:Entrust_Root_Certification_Authority.pem #11 42.84 Adding debian:D-TRUST_EV_Root_CA_1_2020.pem #11 42.85 Adding debian:Certigna_Root_CA.pem #11 42.85 Adding debian:TeliaSonera_Root_CA_v1.pem #11 42.86 Adding debian:Certum_Trusted_Network_CA.pem #11 42.86 Adding debian:HiPKI_Root_CA_-_G1.pem #11 42.87 Adding debian:SZAFIR_ROOT_CA2.pem #11 42.87 Adding debian:USERTrust_RSA_Certification_Authority.pem #11 42.88 Adding debian:OISTE_WISeKey_Global_Root_GC_CA.pem #11 42.88 Adding debian:Buypass_Class_2_Root_CA.pem #11 42.89 Adding debian:AC_RAIZ_FNMT-RCM_SERVIDORES_SEGUROS.pem #11 42.89 Adding debian:e-Szigno_Root_CA_2017.pem #11 42.90 Adding debian:Microsoft_ECC_Root_Certificate_Authority_2017.pem #11 42.91 Adding debian:Security_Communication_Root_CA.pem #11 42.91 Adding debian:GlobalSign_Root_CA.pem #11 42.92 Adding debian:E-Tugra_Certification_Authority.pem #11 42.93 Adding debian:Trustwave_Global_Certification_Authority.pem #11 42.93 Adding debian:QuoVadis_Root_CA_2_G3.pem #11 42.93 Adding debian:SSL.com_EV_Root_Certification_Authority_ECC.pem #11 42.94 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068_2.pem #11 42.95 Adding debian:E-Tugra_Global_Root_CA_ECC_v3.pem #11 42.95 Adding debian:Entrust.net_Premium_2048_Secure_Server_CA.pem #11 42.96 Adding debian:AffirmTrust_Premium.pem #11 42.96 Adding debian:Amazon_Root_CA_3.pem #11 42.97 Adding debian:AffirmTrust_Networking.pem #11 42.98 Adding debian:IdenTrust_Public_Sector_Root_CA_1.pem #11 42.98 Adding debian:GlobalSign_ECC_Root_CA_-_R4.pem #11 42.99 Adding debian:QuoVadis_Root_CA_3_G3.pem #11 42.99 Adding debian:certSIGN_ROOT_CA.pem #11 43.00 Adding debian:IdenTrust_Commercial_Root_CA_1.pem #11 43.00 Adding debian:SecureTrust_CA.pem #11 43.01 Adding debian:DigiCert_Global_Root_G3.pem #11 43.01 Adding debian:GlobalSign_Root_E46.pem #11 43.02 Adding debian:SwissSign_Silver_CA_-_G2.pem #11 43.02 Adding debian:vTrus_ECC_Root_CA.pem #11 43.03 Adding debian:GlobalSign_ECC_Root_CA_-_R5.pem #11 43.04 Adding debian:OISTE_WISeKey_Global_Root_GB_CA.pem #11 43.04 Adding debian:DigiCert_Assured_ID_Root_G3.pem #11 43.05 Adding debian:emSign_Root_CA_-_G1.pem #11 43.05 Adding debian:DigiCert_TLS_RSA4096_Root_G5.pem #11 43.06 Adding debian:COMODO_Certification_Authority.pem #11 43.06 Adding debian:DigiCert_Global_Root_CA.pem #11 43.07 Adding debian:QuoVadis_Root_CA_2.pem #11 43.08 Adding debian:TWCA_Global_Root_CA.pem #11 43.09 Adding debian:ACCVRAIZ1.pem #11 43.09 Adding debian:GTS_Root_R2.pem #11 43.10 Adding debian:HARICA_TLS_RSA_Root_CA_2021.pem #11 43.10 Adding debian:Actalis_Authentication_Root_CA.pem #11 43.11 Adding debian:Certum_Trusted_Network_CA_2.pem #11 43.11 Adding debian:Atos_TrustedRoot_2011.pem #11 43.11 Adding debian:T-TeleSec_GlobalRoot_Class_3.pem #11 43.13 Adding debian:NetLock_Arany_=Class_Gold=_F\u0151tan\u00fas\u00edtv\u00e1ny.pem #11 43.13 Adding debian:Certigna.pem #11 43.14 Adding debian:Microsec_e-Szigno_Root_CA_2009.pem #11 43.14 Adding debian:UCA_Extended_Validation_Root.pem #11 43.14 Adding debian:DigiCert_Assured_ID_Root_G2.pem #11 43.15 Adding debian:QuoVadis_Root_CA_3.pem #11 43.15 Adding debian:NAVER_Global_Root_Certification_Authority.pem #11 43.16 Adding debian:GlobalSign_Root_CA_-_R6.pem #11 43.17 Adding debian:TUBITAK_Kamu_SM_SSL_Kok_Sertifikasi_-_Surum_1.pem #11 43.17 Adding debian:Trustwave_Global_ECC_P384_Certification_Authority.pem #11 43.18 Adding debian:Go_Daddy_Class_2_CA.pem #11 43.18 Adding debian:TWCA_Root_Certification_Authority.pem #11 43.19 Adding debian:certSIGN_Root_CA_G2.pem #11 43.19 Adding debian:COMODO_ECC_Certification_Authority.pem #11 43.20 Adding debian:ISRG_Root_X2.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_EV_2009.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_2009.pem #11 43.22 Adding debian:UCA_Global_G2_Root.pem #11 43.22 Adding debian:D-TRUST_BR_Root_CA_1_2020.pem #11 43.23 Adding debian:Secure_Global_CA.pem #11 43.24 Adding debian:Hongkong_Post_Root_CA_3.pem #11 43.24 Adding debian:Buypass_Class_3_Root_CA.pem #11 43.25 Adding debian:Entrust_Root_Certification_Authority_-_G2.pem #11 43.25 Adding debian:SSL.com_Root_Certification_Authority_ECC.pem #11 43.26 Adding debian:Hellenic_Academic_and_Research_Institutions_RootCA_2015.pem #11 43.27 Adding debian:E-Tugra_Global_Root_CA_RSA_v3.pem #11 43.27 Adding debian:USERTrust_ECC_Certification_Authority.pem #11 43.28 Adding debian:ePKI_Root_Certification_Authority.pem #11 43.28 Adding debian:ISRG_Root_X1.pem #11 43.29 Adding debian:HARICA_TLS_ECC_Root_CA_2021.pem #11 43.29 Adding debian:GTS_Root_R3.pem #11 43.30 Adding debian:Hellenic_Academic_and_Research_Institutions_ECC_RootCA_2015.pem #11 43.30 Adding debian:Security_Communication_RootCA2.pem #11 43.31 Adding debian:GlobalSign_Root_CA_-_R3.pem #11 43.35 done. #11 43.36 Processing triggers for mime-support (3.64ubuntu1) ... #11 43.40 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #11 43.45 Processing triggers for ca-certificates (20230311ubuntu0.20.04.1) ... #11 43.47 Updating certificates in /etc/ssl/certs... #11 44.82 0 added, 0 removed; done. #11 44.82 Running hooks in /etc/ca-certificates/update.d... #11 44.83 #11 45.16 done. #11 45.16 done. #11 DONE 45.9s #12 [ 8/17] WORKDIR /opt/mlflow #12 DONE 0.1s #13 [ 9/17] RUN pip install mlflow==2.12.2 #13 1.009 Collecting mlflow==2.12.2 #13 1.075 Downloading mlflow-2.12.2-py3-none-any.whl.metadata (29 kB) #13 1.207 Collecting Flask < 4 (from mlflow==2.12.2) #13 1.222 Downloading flask-3.0.3-py3-none-any.whl.metadata (3.2 kB) #13 1.309 Collecting alembic!=1.10.0, < 2 (from mlflow==2.12.2) #13 1.324 Downloading alembic-1.13.1-py3-none-any.whl.metadata (7.4 kB) #13 1.395 Collecting click < 9,>=7.0 (from mlflow==2.12.2) #13 1.409 Downloading click-8.1.7-py3-none-any.whl.metadata (3.0 kB) #13 1.467 Collecting cloudpickle < 4 (from mlflow==2.12.2) #13 1.483 Downloading cloudpickle-3.0.0-py3-none-any.whl.metadata (7.0 kB) #13 1.564 Collecting docker < 8,>=4.0.0 (from mlflow==2.12.2) #13 1.579 Downloading docker-7.0.0-py3-none-any.whl.metadata (3.5 kB) #13 1.635 Collecting entrypoints < 1 (from mlflow==2.12.2) #13 1.651 Downloading entrypoints-0.4-py3-none-any.whl.metadata (2.6 kB) #13 1.742 Collecting gitpython < 4,>=3.1.9 (from mlflow==2.12.2) #13 1.757 Downloading GitPython-3.1.43-py3-none-any.whl.metadata (13 kB) #13 1.822 Collecting graphene < 4 (from mlflow==2.12.2) #13 1.837 Downloading graphene-3.3-py2.py3-none-any.whl.metadata (7.7 kB) #13 1.946 Collecting importlib-metadata!=4.7.0, < 8,>=3.7.0 (from mlflow==2.12.2) #13 1.960 Downloading importlib_metadata-7.1.0-py3-none-any.whl.metadata (4.7 kB) #13 2.027 Collecting markdown < 4,>=3.3 (from mlflow==2.12.2) #13 2.079 Downloading Markdown-3.6-py3-none-any.whl.metadata (7.0 kB) #13 2.299 Collecting matplotlib < 4 (from mlflow==2.12.2) #13 2.314 Downloading matplotlib-3.7.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (5.7 kB) #13 2.593 Collecting numpy < 2 (from mlflow==2.12.2) #13 2.612 Downloading numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB) #13 2.685 Collecting packaging < 25 (from mlflow==2.12.2) #13 2.698 Downloading packaging-24.0-py3-none-any.whl.metadata (3.2 kB) #13 2.902 Collecting pandas < 3 (from mlflow==2.12.2) #13 2.916 Downloading pandas-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB) #13 3.172 Collecting protobuf < 5,>=3.12.0 (from mlflow==2.12.2) #13 3.187 Downloading protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes) #13 3.323 Collecting pyarrow < 16,>=4.0.0 (from mlflow==2.12.2) #13 3.338 Downloading pyarrow-15.0.2-cp38-cp38-manylinux_2_28_x86_64.whl.metadata (3.0 kB) #13 3.456 Collecting pytz < 2025 (from mlflow==2.12.2) #13 3.471 Downloading pytz-2024.1-py2.py3-none-any.whl.metadata (22 kB) #13 3.482 Requirement already satisfied: pyyaml < 7,>=5.1 in /usr/lib/python3/dist-packages (from mlflow==2.12.2) (5.3.1) #13 3.672 Collecting querystring-parser < 2 (from mlflow==2.12.2) #13 3.689 Downloading querystring_parser-1.2.4-py2.py3-none-any.whl.metadata (559 bytes) #13 3.780 Collecting requests < 3,>=2.17.3 (from mlflow==2.12.2) #13 3.794 Downloading requests-2.31.0-py3-none-any.whl.metadata (4.6 kB) #13 3.969 Collecting scikit-learn < 2 (from mlflow==2.12.2) #13 3.985 Downloading scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB) #13 4.159 Collecting scipy < 2 (from mlflow==2.12.2) #13 4.175 Downloading scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (58 kB) #13 4.199 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fonttools, entrypoints, cycler, cloudpickle, click, charset-normalizer, certifi, blinker, Werkzeug, sqlalchemy, scipy, requests, querystring-parser, python-dateutil, pyarrow, Mako, Jinja2, importlib-resources, importlib-metadata, gunicorn, graphql-relay, gitdb, contourpy, scikit-learn, pandas, matplotlib, markdown, graphene, gitpython, Flask, docker, alembic, mlflow #13 34.72 Successfully installed Flask-3.0.3 Jinja2-3.1.4 Mako-1.3.5 MarkupSafe-2.1.5 Werkzeug-3.0.3 alembic-1.13.1 aniso8601-9.0.1 blinker-1.8.2 certifi-2024.2.2 charset-normalizer-3.3.2 click-8.1.7 cloudpickle-3.0.0 contourpy-1.1.1 cycler-0.12.1 docker-7.0.0 entrypoints-0.4 fonttools-4.51.0 gitdb-4.0.11 gitpython-3.1.43 graphene-3.3 graphql-core-3.2.3 graphql-relay-3.2.0 greenlet-3.0.3 gunicorn-22.0.0 idna-3.7 importlib-metadata-7.1.0 importlib-resources-6.4.0 itsdangerous-2.2.0 joblib-1.4.2 kiwisolver-1.4.5 markdown-3.6 matplotlib-3.7.5 mlflow-2.12.2 numpy-1.24.4 packaging-24.0 pandas-2.0.3 pillow-10.3.0 protobuf-4.25.3 pyarrow-15.0.2 pyparsing-3.1.2 python-dateutil-2.9.0.post0 pytz-2024.1 querystring-parser-1.2.4 requests-2.31.0 scikit-learn-1.3.2 scipy-1.10.1 six-1.16.0 smmap-5.0.1 sqlalchemy-2.0.30 sqlparse-0.5.0 threadpoolctl-3.5.0 typing-extensions-4.11.0 tzdata-2024.1 urllib3-2.2.1 zipp-3.18.2 #13 34.72 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #13 DONE 36.4s #14 [10/17] RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.12.2:pom -DoutputDirectory=/opt/java #14 1.558 [INFO] Scanning for projects... #14 1.845 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-clean-plugin/2.5/maven-clean-plugin-2.5.pom #14 2.423 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-clean-plugin/2.5/maven-clean-plugin-2.5.pom (3.9 kB at 6.7 kB/s) #14 2.448 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-plugins/22/maven-plugins-22.pom #14 2.499 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-plugins/22/maven-plugins-22.pom (13 kB at 256 kB/s) #14 2.511 [INFO] Downloading from central: 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12.86 [INFO] Finished at: 2024-05-16T20:25:32Z #14 12.86 [INFO] ------------------------------------------------------------------------ #14 DONE 13.0s #15 [11/17] RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.12.2:jar -DoutputDirectory=/opt/java/jars #15 1.696 [INFO] Scanning for projects... #15 1.897 [INFO] #15 1.897 [INFO] ------------------ < org.apache.maven:standalone-pom >------------------- #15 1.898 [INFO] Building Maven Stub Project (No POM) 1 #15 1.898 [INFO] --------------------------------[ pom ]--------------------------------- #15 1.905 [INFO] #15 1.905 [INFO] --- maven-dependency-plugin:2.8:copy (default-cli) @ standalone-pom --- #15 2.594 [INFO] Configured Artifact: org.mlflow:mlflow-scoring:2.12.2:jar #15 2.804 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/mlflow/mlflow-scoring/2.12.2/mlflow-scoring-2.12.2.jar #15 3.207 [INFO] Downloaded from central: 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/opt/java/jars/maven-plugin-parameter-documenter-2.2.1.jar #17 134.5 [INFO] Copying commons-math3-3.4.1.jar to /opt/java/jars/commons-math3-3.4.1.jar #17 134.5 [INFO] Copying junit-4.12.jar to /opt/java/jars/junit-4.12.jar #17 134.5 [INFO] Copying slf4j-jdk14-1.7.25.jar to /opt/java/jars/slf4j-jdk14-1.7.25.jar #17 134.5 [INFO] Copying slf4j-api-1.7.25.jar to /opt/java/jars/slf4j-api-1.7.25.jar #17 134.5 [INFO] Copying jetty-io-9.4.11.v20180605.jar to /opt/java/jars/jetty-io-9.4.11.v20180605.jar #17 134.5 [INFO] Copying httpclient-4.5.6.jar to /opt/java/jars/httpclient-4.5.6.jar #17 134.5 [INFO] Copying maven-model-2.2.1.jar to /opt/java/jars/maven-model-2.2.1.jar #17 134.5 [INFO] Copying jackson-dataformat-yaml-2.10.0.jar to /opt/java/jars/jackson-dataformat-yaml-2.10.0.jar #17 134.5 [INFO] Copying doxia-logging-api-1.1.jar to /opt/java/jars/doxia-logging-api-1.1.jar #17 134.5 [INFO] Copying surefire-api-2.22.0.jar to /opt/java/jars/surefire-api-2.22.0.jar #17 134.5 [INFO] Copying maven-plugin-registry-2.2.1.jar to /opt/java/jars/maven-plugin-registry-2.2.1.jar #17 134.5 [INFO] Copying backport-util-concurrent-3.1.jar to /opt/java/jars/backport-util-concurrent-3.1.jar #17 134.5 [INFO] Copying wagon-http-shared-1.0-beta-6.jar to /opt/java/jars/wagon-http-shared-1.0-beta-6.jar #17 134.5 [INFO] Copying commons-httpclient-3.1.jar to /opt/java/jars/commons-httpclient-3.1.jar #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] BUILD SUCCESS #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] Total time: 02:12 min #17 134.5 [INFO] Finished at: 2024-05-16T20:27:50Z #17 134.5 [INFO] ------------------------------------------------------------------------ #17 DONE 134.8s #18 [14/17] COPY model_dir/success6g_model /opt/ml/model #18 DONE 0.1s #19 [15/17] RUN python -c \"from mlflow.models import container as C; C._install_pyfunc_deps('/opt/ml/model', install_mlflow=False, enable_mlserver=True, env_manager='virtualenv');\" #19 1.285 2024/05/16 20:27:52 INFO mlflow.models.container: creating and activating custom environment #19 1.289 2024/05/16 20:27:52 INFO mlflow.utils.virtualenv: Installing python 3.10.11 if it does not exist #19 1.673 Downloading Python-3.10.11.tar.xz... #19 1.673 -> https://www.python.org/ftp/python/3.10.11/Python-3.10.11.tar.xz #19 4.792 Installing Python-3.10.11... #19 108.0 Installed Python-3.10.11 to /root/.pyenv/versions/3.10.11 #19 108.4 2024/05/16 20:29:39 INFO mlflow.utils.virtualenv: Creating a new environment in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b with /root/.pyenv/versions/3.10.11/bin/python #19 109.3 created virtual environment CPython3.10.11.final.0-64 in 719ms #19 109.3 creator CPython3Posix(dest=/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b, clear=False, no_vcs_ignore=False, global=False) #19 109.3 seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/.local/share/virtualenv) #19 109.3 added seed packages: pip==24.0, setuptools==69.5.1, wheel==0.43.0 #19 109.3 activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator #19 109.4 2024/05/16 20:29:40 INFO mlflow.utils.virtualenv: Installing dependencies #19 168.4 WARNING: There was an error checking the latest version of pip. #19 169.3 Collecting mlserver!=1.3.1, < 1.4.0,>=1.2.0 #19 169.4 Downloading mlserver-1.3.5-py3-none-any.whl (113 kB) #19 169.4 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 113.2/113.2 kB 3.4 MB/s eta 0:00:00 #19 169.6 Collecting mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0 #19 169.6 Downloading mlserver_mlflow-1.3.5-py3-none-any.whl (10 kB) #19 169.7 Requirement already satisfied: click in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) (8.1.3) #19 169.8 Collecting fastapi!=0.89.0, < =0.89.1,>=0.88.0 (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.8 Downloading fastapi-0.89.1-py3-none-any.whl (55 kB) #19 169.8 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 55.8/55.8 kB 7.4 MB/s eta 0:00:00 #19 169.9 Collecting python-dotenv (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.9 Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB) #19 171.0 Collecting grpcio (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.0 Downloading grpcio-1.63.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB) #19 171.3 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1.4.0,>=1.2.0) (4.25.3) #19 171.5 Collecting uvicorn (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.5 Downloading uvicorn-0.29.0-py3-none-any.whl (60 kB) #19 171.6 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 60.8/60.8 kB 8.7 MB/s eta 0:00:00 #19 171.7 Collecting starlette-exporter (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.7 Downloading starlette_exporter-0.21.0-py3-none-any.whl (14 kB) #19 171.9 Collecting py-grpc-prometheus (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.9 Downloading py_grpc_prometheus-0.8.0-py3-none-any.whl (12 kB) #19 172.1 Collecting aiokafka (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 172.1 Downloading aiokafka-0.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB) #19 172.2 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orjson-3.10.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (142 kB) #19 173.6 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 142.5/142.5 kB 19.4 MB/s eta 0:00:00 #19 173.7 Collecting uvloop (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 173.7 Downloading uvloop-0.19.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.4 MB) #19 173.8 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.4/3.4 MB 19.8 MB/s eta 0:00:00 #19 173.9 Requirement already satisfied: mlflow in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (2.12.2) #19 174.2 Collecting pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1, < 2.0.0,>=1.6.2 (from fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 174.2 Downloading pydantic-1.10.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB) #19 174.4 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.1/3.1 MB 19.8 MB/s eta 0:00:00 #19 174.5 Collecting starlette==0.22.0 (from fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 174.5 Downloading starlette-0.22.0-py3-none-any.whl (64 kB) #19 174.5 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0:00:00 #19 176.1 Collecting geventhttpclient < =2.0.2,>=1.4.4 (from tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.2 Downloading geventhttpclient-2.0.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (100 kB) #19 176.2 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.3/100.3 kB 10.5 MB/s eta 0:00:00 #19 176.3 Requirement already satisfied: async-timeout in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from aiokafka->mlserver!=1.3.1, < 1.4.0,>=1.2.0) (4.0.3) #19 176.3 Requirement already satisfied: packaging in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from aiokafka->mlserver!=1.3.1, < 1.4.0,>=1.2.0) (23.1) #19 176.4 Requirement already satisfied: Flask < 4 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.0.3) #19 176.4 Requirement already satisfied: alembic!=1.10.0, < 2 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (1.13.1) #19 176.4 Requirement already satisfied: cloudpickle < 4 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (2.2.1) #19 176.4 Requirement already satisfied: docker < 8,>=4.0.0 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (7.0.0) #19 176.4 Requirement already satisfied: entrypoints < 1 in 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requirements. This could take a while. #19 176.6 Collecting starlette-exporter (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.7 Downloading starlette_exporter-0.20.0-py3-none-any.whl (14 kB) #19 176.7 Downloading starlette_exporter-0.19.0-py3-none-any.whl (14 kB) #19 176.7 Downloading starlette_exporter-0.18.0-py3-none-any.whl (14 kB) #19 176.8 Downloading starlette_exporter-0.17.1-py3-none-any.whl (14 kB) #19 176.8 Collecting h11>=0.8 (from uvicorn->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.8 Downloading h11-0.14.0-py3-none-any.whl (58 kB) #19 176.9 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 58.3/58.3 kB 8.0 MB/s eta 0:00:00 #19 176.9 Requirement already satisfied: typing-extensions>=4.0 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from 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/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from sqlalchemy < 3,>=1.4.0->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.0.3) #19 179.6 Collecting sniffio>=1.1 (from anyio < 5,>=3.4.0->starlette==0.22.0->fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 179.6 Downloading sniffio-1.3.1-py3-none-any.whl (10 kB) #19 179.7 Collecting exceptiongroup>=1.0.2 (from anyio < 5,>=3.4.0->starlette==0.22.0->fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 179.7 Downloading exceptiongroup-1.2.1-py3-none-any.whl (16 kB) #19 179.9 Collecting zope.event (from gevent>=0.13->geventhttpclient < =2.0.2,>=1.4.4->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 180.0 Downloading zope.event-5.0-py3-none-any.whl (6.8 kB) #19 180.2 Collecting zope.interface (from gevent>=0.13->geventhttpclient < =2.0.2,>=1.4.4->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 180.2 Downloading zope.interface-6.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (247 kB) #19 180.2 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 247.5/247.5 kB 31.3 MB/s eta 0:00:00 #19 180.2 Requirement already satisfied: smmap < 6,>=3.0.1 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from gitdb < 5,>=4.0.1->gitpython < 4,>=3.1.9->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (5.0.1) #19 181.2 Installing collected packages: brotli, zope.interface, zope.event, uvloop, sniffio, python-rapidjson, python-dotenv, pydantic, prometheus-client, orjson, multidict, importlib-resources, h11, grpcio, frozenlist, exceptiongroup, attrs, aiokafka, aiofiles, yarl, uvicorn, tritonclient, py-grpc-prometheus, gevent, anyio, aiosignal, starlette, geventhttpclient, aiohttp, starlette-exporter, fastapi, mlserver, mlserver-mlflow #19 184.4 Successfully installed aiofiles-23.2.1 aiohttp-3.9.5 aiokafka-0.10.0 aiosignal-1.3.1 anyio-4.3.0 attrs-23.2.0 brotli-1.1.0 exceptiongroup-1.2.1 fastapi-0.89.1 frozenlist-1.4.1 gevent-24.2.1 geventhttpclient-2.0.2 grpcio-1.63.0 h11-0.14.0 importlib-resources-6.4.0 mlserver-1.3.5 mlserver-mlflow-1.3.5 multidict-6.0.5 orjson-3.10.3 prometheus-client-0.20.0 py-grpc-prometheus-0.8.0 pydantic-1.10.15 python-dotenv-1.0.1 python-rapidjson-1.16 sniffio-1.3.1 starlette-0.22.0 starlette-exporter-0.17.1 tritonclient-2.45.0 uvicorn-0.29.0 uvloop-0.19.0 yarl-1.9.4 zope.event-5.0 zope.interface-6.4 #19 184.4 WARNING: There was an error checking the latest version of pip. #19 DONE 186.7s #20 [16/17] RUN chmod o+rwX /opt/mlflow/ #20 DONE 0.4s #21 [17/17] RUN rm -rf /var/lib/apt/lists/* #21 DONE 0.6s #22 exporting to image #22 exporting layers #22 exporting layers 12.0s done #22 writing image sha256:29875270957bd037ee2b833cf9201a001d353e4fef34b1d8c4923b278ef8ac02 done #22 naming to docker.io/5uperpalo/mlflow-success6g done #22 DONE 12.1s ```","title":"Appendix"},{"location":"notebooks/05_model_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = 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event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { 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.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ 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In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Model analysis \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [3]: Copied! from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast In [4]: Copied! trainer = dill_load ( \"lgbm_trainer.dill\" ) trainer = dill_load(\"lgbm_trainer.dill\") In [5]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ trainer . target_col ], random_state = random_state , ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ trainer . target_col ], random_state = random_state , ) # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[trainer.target_col], random_state=random_state, ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[trainer.target_col], random_state=random_state, ) In [6]: Copied! df_predicted_proba = trainer . predict_proba ( df = df_test . drop ( columns = trainer . target_col ), binary2d = False ) df_predicted_cls = trainer . predict_cls ( df = df_test . drop ( columns = trainer . target_col )) df_predicted_proba = trainer.predict_proba( df=df_test.drop(columns=trainer.target_col), binary2d=False ) df_predicted_cls = trainer.predict_cls(df=df_test.drop(columns=trainer.target_col)) Model feature importance and \"certainty\" of the model in predicting classes - probability density per class \u00b6 In [7]: Copied! lgb . plot_importance ( trainer . model , importance_type = \"split\" ) # lgb.plot_importance(trainer.model, importance_type=\"gain\") lgb.plot_importance(trainer.model, importance_type=\"split\") # lgb.plot_importance(trainer.model, importance_type=\"gain\") Out[7]: <Axes: title={'center': 'Feature importance'}, xlabel='Feature importance', ylabel='Features'> In [8]: Copied! fig = prob_distrib_per_class ( predicted_probs = df_predicted_proba . values . flatten (), actual = df_test [ trainer . target_col ] . values . flatten (), task = \"binary\" , ) fig = prob_distrib_per_class( predicted_probs=df_predicted_proba.values.flatten(), actual=df_test[trainer.target_col].values.flatten(), task=\"binary\", ) SHAP analysis \u00b6 In [9]: Copied! df_test_prep = trainer . preprocessors [ 0 ] . transform ( df_test . drop ( columns = [ trainer . target_col ]) ) df_test_prep [ trainer . target_col ] = df_test [ trainer . target_col ] . astype ( int ) lgb_test , _ = to_lgbdataset ( train = df_test_prep , cat_cols = trainer . cat_cols , target_col = trainer . target_col , id_cols = trainer . id_cols , ) df_test_prep = trainer.preprocessors[0].transform( df_test.drop(columns=[trainer.target_col]) ) df_test_prep[trainer.target_col] = df_test[trainer.target_col].astype(int) lgb_test, _ = to_lgbdataset( train=df_test_prep, cat_cols=trainer.cat_cols, target_col=trainer.target_col, id_cols=trainer.id_cols, ) In [10]: Copied! base_value = trainer . model . predict ( data = lgb_test . data , pred_contrib = True ) shap_values = base_value [:, : - 1 ] base_value = np . mean ( base_value [:, - 1 ]) base_value = trainer.model.predict(data=lgb_test.data, pred_contrib=True) shap_values = base_value[:, :-1] base_value = np.mean(base_value[:, -1]) In [11]: Copied! shap . summary_plot ( shap_values , features = lgb_test . data , plot_type = \"dot\" , sort = False , show = True ) shap.summary_plot( shap_values, features=lgb_test.data, plot_type=\"dot\", sort=False, show=True ) In [19]: Copied! df_predicted_cls [ 10 : 12 ] df_predicted_cls[10:12] Out[19]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 10 0 11 1 In [20]: Copied! df_test . iloc [ 10 : 12 ] df_test.iloc[10:12] Out[20]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 579 38.04 89.0 895.5 0.0 22.0 9.77 83.14 34050.0 101.0 14.76 0 123abc 23 100.00 22.0 926.0 0.0 11.0 12.66 83.53 32160.0 101.0 14.72 1 123abc In [21]: Copied! shap_decision_plot = shap . decision_plot ( base_value = base_value , shap_values = shap_values [ 10 : 12 ], features = trainer . model . feature_name (), feature_names = trainer . model . feature_name (), # legend_labels=legend_labels, link = \"logit\" , # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), ) shap_decision_plot = shap.decision_plot( base_value=base_value, shap_values=shap_values[10:12], features=trainer.model.feature_name(), feature_names=trainer.model.feature_name(), # legend_labels=legend_labels, link=\"logit\", # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), )","title":"05_model_analysis"},{"location":"notebooks/05_model_analysis.html#model-analysis","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [3]: Copied! from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast In [4]: Copied! trainer = dill_load ( \"lgbm_trainer.dill\" ) trainer = dill_load(\"lgbm_trainer.dill\") In [5]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ trainer . target_col ], random_state = random_state , ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ trainer . target_col ], random_state = random_state , ) # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[trainer.target_col], random_state=random_state, ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[trainer.target_col], random_state=random_state, ) In [6]: Copied! df_predicted_proba = trainer . predict_proba ( df = df_test . drop ( columns = trainer . target_col ), binary2d = False ) df_predicted_cls = trainer . predict_cls ( df = df_test . drop ( columns = trainer . target_col )) df_predicted_proba = trainer.predict_proba( df=df_test.drop(columns=trainer.target_col), binary2d=False ) df_predicted_cls = trainer.predict_cls(df=df_test.drop(columns=trainer.target_col))","title":"Model analysis"},{"location":"notebooks/05_model_analysis.html#model-feature-importance-and-certainty-of-the-model-in-predicting-classes-probability-density-per-class","text":"In [7]: Copied! lgb . plot_importance ( trainer . model , importance_type = \"split\" ) # lgb.plot_importance(trainer.model, importance_type=\"gain\") lgb.plot_importance(trainer.model, importance_type=\"split\") # lgb.plot_importance(trainer.model, importance_type=\"gain\") Out[7]: <Axes: title={'center': 'Feature importance'}, xlabel='Feature importance', ylabel='Features'> In [8]: Copied! fig = prob_distrib_per_class ( predicted_probs = df_predicted_proba . values . flatten (), actual = df_test [ trainer . target_col ] . values . flatten (), task = \"binary\" , ) fig = prob_distrib_per_class( predicted_probs=df_predicted_proba.values.flatten(), actual=df_test[trainer.target_col].values.flatten(), task=\"binary\", )","title":"Model feature importance and \"certainty\" of the model in predicting classes - probability density per class"},{"location":"notebooks/05_model_analysis.html#shap-analysis","text":"In [9]: Copied! df_test_prep = trainer . preprocessors [ 0 ] . transform ( df_test . drop ( columns = [ trainer . target_col ]) ) df_test_prep [ trainer . target_col ] = df_test [ trainer . target_col ] . astype ( int ) lgb_test , _ = to_lgbdataset ( train = df_test_prep , cat_cols = trainer . cat_cols , target_col = trainer . target_col , id_cols = trainer . id_cols , ) df_test_prep = trainer.preprocessors[0].transform( df_test.drop(columns=[trainer.target_col]) ) df_test_prep[trainer.target_col] = df_test[trainer.target_col].astype(int) lgb_test, _ = to_lgbdataset( train=df_test_prep, cat_cols=trainer.cat_cols, target_col=trainer.target_col, id_cols=trainer.id_cols, ) In [10]: Copied! base_value = trainer . model . predict ( data = lgb_test . data , pred_contrib = True ) shap_values = base_value [:, : - 1 ] base_value = np . mean ( base_value [:, - 1 ]) base_value = trainer.model.predict(data=lgb_test.data, pred_contrib=True) shap_values = base_value[:, :-1] base_value = np.mean(base_value[:, -1]) In [11]: Copied! shap . summary_plot ( shap_values , features = lgb_test . data , plot_type = \"dot\" , sort = False , show = True ) shap.summary_plot( shap_values, features=lgb_test.data, plot_type=\"dot\", sort=False, show=True ) In [19]: Copied! df_predicted_cls [ 10 : 12 ] df_predicted_cls[10:12] Out[19]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 10 0 11 1 In [20]: Copied! df_test . iloc [ 10 : 12 ] df_test.iloc[10:12] Out[20]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 579 38.04 89.0 895.5 0.0 22.0 9.77 83.14 34050.0 101.0 14.76 0 123abc 23 100.00 22.0 926.0 0.0 11.0 12.66 83.53 32160.0 101.0 14.72 1 123abc In [21]: Copied! shap_decision_plot = shap . decision_plot ( base_value = base_value , shap_values = shap_values [ 10 : 12 ], features = trainer . model . feature_name (), feature_names = trainer . model . feature_name (), # legend_labels=legend_labels, link = \"logit\" , # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), ) shap_decision_plot = shap.decision_plot( base_value=base_value, shap_values=shap_values[10:12], features=trainer.model.feature_name(), feature_names=trainer.model.feature_name(), # legend_labels=legend_labels, link=\"logit\", # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), )","title":"SHAP analysis"},{"location":"notebooks/success6g-edge.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); In [1]: Copied! import pandas as pd import ast import os import redis os . chdir ( \"..\" ) import pandas as pd import ast import os import redis os.chdir(\"..\") Get the data into pandas \u00b6 In [2]: Copied! with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df = pd . concat ([ df , temp_df ], axis = 1 ) with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df = pd.concat([df, temp_df], axis=1) In [3]: Copied! df df Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage 0 37.25 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 37.25 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 2 36.86 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 3 36.86 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 4 36.86 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 ... ... ... ... ... ... ... ... ... ... ... 32740 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32741 NaN NaN NaN NaN NaN 9.44 NaN NaN NaN NaN 32742 NaN NaN NaN NaN NaN 9.49 NaN NaN NaN NaN 32743 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32744 NaN NaN NaN NaN NaN 9.52 NaN NaN NaN NaN 32745 rows \u00d7 10 columns In [4]: Copied! df . dropna ( inplace = True ) df.dropna(inplace=True) In [5]: Copied! df [ \"class\" ] = 0 df . loc [: 100 , [ \"class\" ]] = 1 df . loc [: 100 , [ \"engine_load\" ]] = 100 df[\"class\"] = 0 df.loc[:100, [\"class\"]] = 1 df.loc[:100, [\"engine_load\"]] = 100 In [6]: Copied! from lightgbm import LGBMClassifier model = LGBMClassifier () model . fit ( df . drop ( columns = [ \"class\" ]), df [ \"class\" ]) from lightgbm import LGBMClassifier model = LGBMClassifier() model.fit(df.drop(columns=[\"class\"]), df[\"class\"]) [LightGBM] [Info] Number of positive: 101, number of negative: 934 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 508 [LightGBM] [Info] Number of data points in the train set: 1035, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097585 -> initscore=-2.224356 [LightGBM] [Info] Start training from score -2.224356 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf Out[6]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} LGBMClassifier() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. LGBMClassifier LGBMClassifier() Redis \u00b6 Install and start the redis database \u00b6 pip install prometheus-client sudo apt install lsb-release curl gpg curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list sudo apt-get update sudo apt-get install redis sudo service redis-server start # sudo service redis-server stop sudo service redis-server status redis-cli ping Fill the Redis database \u00b6 send data to redis redis-cli -h localhost -p 6379 redis-cli -h 10.152.183.169 -a redis -p 6379 XADD v2x * car_id 123 engine_load 37.25 engine_coolant_temp 17.00 engine_speed 904.50 vehicle_speed 0.00 intake_air_temp 10.00 maf 12.55 throttle_position 83.14 fuel_rg_pressure 37270.00 barometric_pressure 101.00 control_voltage 0.0666 In [7]: Copied! # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis . Redis ( host = redis_host , port = 6379 , password = \"redis\" ) # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis.Redis(host=redis_host, port=6379, password=\"redis\") # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) In [8]: Copied! entry_data = { \"field1\" : \"value1\" , \"field2\" : \"value2\" } for index , row in df . iterrows (): redis_conn . xadd ( \"v2x\" , dict ( row )) entry_data = {\"field1\": \"value1\", \"field2\": \"value2\"} for index, row in df.iterrows(): redis_conn.xadd(\"v2x\", dict(row)) Retrieve data from redis \u00b6 In [13]: Copied! while True : # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[ stream , [[ number , parts ]]]] = redis_conn . xread ( streams = { \"v2x\" : \"$\" }, block = 0 ) keys = [ p . decode ( \"utf-8\" ) for p in parts . keys ()] vals = [ float ( p . decode ( \"utf-8\" )) for p in parts . values ()] d = pd . DataFrame ( dict ( zip ( keys , vals )), index = [ 0 ]) d [ \"predicted_class\" ] = model . predict ( d . drop ( columns = [ \"car_id\" ]))[ 0 ] print ( d ) while True: # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[stream, [[number, parts]]]] = redis_conn.xread(streams={\"v2x\": \"$\"}, block=0) keys = [p.decode(\"utf-8\") for p in parts.keys()] vals = [float(p.decode(\"utf-8\")) for p in parts.values()] d = pd.DataFrame(dict(zip(keys, vals)), index=[0]) d[\"predicted_class\"] = model.predict(d.drop(columns=[\"car_id\"]))[0] print(d)","title":"success6g-edge"},{"location":"notebooks/success6g-edge.html#get-the-data-into-pandas","text":"In [2]: Copied! with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df = pd . concat ([ df , temp_df ], axis = 1 ) with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df = pd.concat([df, temp_df], axis=1) In [3]: Copied! df df Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage 0 37.25 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 37.25 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 2 36.86 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 3 36.86 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 4 36.86 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 ... ... ... ... ... ... ... ... ... ... ... 32740 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32741 NaN NaN NaN NaN NaN 9.44 NaN NaN NaN NaN 32742 NaN NaN NaN NaN NaN 9.49 NaN NaN NaN NaN 32743 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32744 NaN NaN NaN NaN NaN 9.52 NaN NaN NaN NaN 32745 rows \u00d7 10 columns In [4]: Copied! df . dropna ( inplace = True ) df.dropna(inplace=True) In [5]: Copied! df [ \"class\" ] = 0 df . loc [: 100 , [ \"class\" ]] = 1 df . loc [: 100 , [ \"engine_load\" ]] = 100 df[\"class\"] = 0 df.loc[:100, [\"class\"]] = 1 df.loc[:100, [\"engine_load\"]] = 100 In [6]: Copied! from lightgbm import LGBMClassifier model = LGBMClassifier () model . fit ( df . drop ( columns = [ \"class\" ]), df [ \"class\" ]) from lightgbm import LGBMClassifier model = LGBMClassifier() model.fit(df.drop(columns=[\"class\"]), df[\"class\"]) [LightGBM] [Info] Number of positive: 101, number of negative: 934 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 508 [LightGBM] [Info] Number of data points in the train set: 1035, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097585 -> initscore=-2.224356 [LightGBM] [Info] Start training from score -2.224356 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf Out[6]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: 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div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} LGBMClassifier() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. LGBMClassifier LGBMClassifier()","title":"Get the data into pandas"},{"location":"notebooks/success6g-edge.html#redis","text":"","title":"Redis"},{"location":"notebooks/success6g-edge.html#install-and-start-the-redis-database","text":"pip install prometheus-client sudo apt install lsb-release curl gpg curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list sudo apt-get update sudo apt-get install redis sudo service redis-server start # sudo service redis-server stop sudo service redis-server status redis-cli ping","title":"Install and start the redis database"},{"location":"notebooks/success6g-edge.html#fill-the-redis-database","text":"send data to redis redis-cli -h localhost -p 6379 redis-cli -h 10.152.183.169 -a redis -p 6379 XADD v2x * car_id 123 engine_load 37.25 engine_coolant_temp 17.00 engine_speed 904.50 vehicle_speed 0.00 intake_air_temp 10.00 maf 12.55 throttle_position 83.14 fuel_rg_pressure 37270.00 barometric_pressure 101.00 control_voltage 0.0666 In [7]: Copied! # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis . Redis ( host = redis_host , port = 6379 , password = \"redis\" ) # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis.Redis(host=redis_host, port=6379, password=\"redis\") # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) In [8]: Copied! entry_data = { \"field1\" : \"value1\" , \"field2\" : \"value2\" } for index , row in df . iterrows (): redis_conn . xadd ( \"v2x\" , dict ( row )) entry_data = {\"field1\": \"value1\", \"field2\": \"value2\"} for index, row in df.iterrows(): redis_conn.xadd(\"v2x\", dict(row))","title":"Fill the Redis database"},{"location":"notebooks/success6g-edge.html#retrieve-data-from-redis","text":"In [13]: Copied! while True : # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[ stream , [[ number , parts ]]]] = redis_conn . xread ( streams = { \"v2x\" : \"$\" }, block = 0 ) keys = [ p . decode ( \"utf-8\" ) for p in parts . keys ()] vals = [ float ( p . decode ( \"utf-8\" )) for p in parts . values ()] d = pd . DataFrame ( dict ( zip ( keys , vals )), index = [ 0 ]) d [ \"predicted_class\" ] = model . predict ( d . drop ( columns = [ \"car_id\" ]))[ 0 ] print ( d ) while True: # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[stream, [[number, parts]]]] = redis_conn.xread(streams={\"v2x\": \"$\"}, block=0) keys = [p.decode(\"utf-8\") for p in parts.keys()] vals = [float(p.decode(\"utf-8\")) for p in parts.values()] d = pd.DataFrame(dict(zip(keys, vals)), index=[0]) d[\"predicted_class\"] = model.predict(d.drop(columns=[\"car_id\"]))[0] print(d)","title":"Retrieve data from redis"}]}
\ No newline at end of file
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Description of the components: Grafana - dashboards Ingress - expose services to the operator Prometheus - gather pod metrics InfluxDB - gather vehicular measurements and predictions MinIO - store models and training/testing data JupyterHub - develop new models MLflow - MLops, experiment and model tracking Kserve - serve inference models to predefined pods Istio - to ensure optimal traffic flow between microservices Knative - to ensure autoscaling of inference service pods Kepler - gather energy consumption data Redis - API for transfer of OBU measurements to Kubernetes Additional ideas \u00b6 KubeEdge deployment - same as Microk8s except with KubeEdge and Kubeflow/Kserver is swapped for Sedna implement multimodel pods e.g. by ModelMesh , or alpha feature of Kserve use Rancher to manage multi cluster Kubernetes","title":"home"},{"location":"index.html#_1","text":"Basic Architecture Overview This repository is for the edge pod implementation, monitoring, and analysis in SUCCESS6G project.","title":""},{"location":"index.html#solution-overview","text":"Solution is deployed in Microk8s. Description of the components: Grafana - dashboards Ingress - expose services to the operator Prometheus - gather pod metrics InfluxDB - gather vehicular measurements and predictions MinIO - store models and training/testing data JupyterHub - develop new models MLflow - MLops, experiment and model tracking Kserve - serve inference models to predefined pods Istio - to ensure optimal traffic flow between microservices Knative - to ensure autoscaling of inference service pods Kepler - gather energy consumption data Redis - API for transfer of OBU measurements to Kubernetes","title":"Solution overview"},{"location":"index.html#additional-ideas","text":"KubeEdge deployment - same as Microk8s except with KubeEdge and Kubeflow/Kserver is swapped for Sedna implement multimodel pods e.g. by ModelMesh , or alpha feature of Kserve use Rancher to manage multi cluster Kubernetes","title":"Additional ideas"},{"location":"architecture_overview.html","text":"Architecture \u00b6 Basic Architecture \u00b6 Basic Architecture Single cluster architecture \u00b6 Single cluster architecture Advantages: Simplified Management: Easier to manage and maintain as there's only one cluster. Simplified network configuration and service discovery within the cluster. Resource Efficiency: Better utilization of resources since there is no need to allocate separate resources for multiple clusters. Cost-Effective: Lower operational costs due to reduced overhead in managing multiple clusters. Reduced complexity in monitoring and logging, leading to potential cost savings. Unified Security and Policy Management: Simplified implementation of security policies, access controls, and compliance measures. Consistent Environment: Ensures a consistent environment across all nodes, simplifying development and deployment processes. Disadvantages: Scalability Limitations: May face scalability issues as the number of edge devices or workloads increases. Single point of failure; if the cluster goes down, all connected devices and services are affected. Performance Bottlenecks: Potential for performance bottlenecks if the cluster becomes overloaded. Limited fault isolation; issues in one part of the cluster can impact the entire system. Geographical Constraints: Less optimal for geographically distributed edge nodes as latency might increase. May not efficiently handle diverse network conditions across different locations. Single cluster architecture (advanced) \u00b6 Single cluster architecture(advanced) Architecture improved by involving service mesh for optimal micro service traffic routing. For more info see istio documentation Multi cluster architecture \u00b6 Multi cluster architecture Multi cluster deployment that can utilize dUPF metrics (e.g. number of connected users) to decide if the application should be deployed in a neighboring micro cluster Advantages: Scalability and Fault Isolation: Better scalability by distributing workloads across multiple clusters. Improved fault isolation; problems in one cluster do not affect others. Geographical Distribution: Optimized for geographically distributed deployments, reducing latency by placing clusters closer to the edge devices. Can handle diverse network conditions more effectively. Performance Optimization: Easier to optimize performance by dedicating clusters to specific workloads or regions. Enhanced load balancing and resource distribution. Resilience and High Availability: Higher resilience and availability; failure in one cluster can be mitigated by other clusters. Enhanced disaster recovery capabilities. Disadvantages: Increased Complexity: More complex to manage and maintain multiple clusters. Higher administrative overhead in coordinating and synchronizing clusters. Resource Overhead: Requires more resources for cluster management (e.g., control planes for each cluster). Potentially higher infrastructure costs due to multiple clusters. Security and Policy Management: More complex to implement and manage security policies and compliance across multiple clusters. Potential challenges in maintaining consistent security postures across clusters. Inter-Cluster Communication: Requires robust inter-cluster communication mechanisms, which can add to the complexity. More complicated service discovery and networking between clusters. Conclusion \u00b6 The choice between single-cluster and multi-cluster Kubernetes deployments for IoT on the edge depends on factors such as scale, geographic distribution, resource availability, and management complexity. Single-cluster deployments are simpler and more cost-effective for smaller, localized environments, whereas multi-cluster deployments offer better scalability, fault isolation, and performance optimization for larger, distributed environments.","title":"architecture overview"},{"location":"architecture_overview.html#architecture","text":"","title":"Architecture"},{"location":"architecture_overview.html#basic-architecture","text":"Basic Architecture","title":"Basic Architecture"},{"location":"architecture_overview.html#single-cluster-architecture","text":"Single cluster architecture Advantages: Simplified Management: Easier to manage and maintain as there's only one cluster. Simplified network configuration and service discovery within the cluster. Resource Efficiency: Better utilization of resources since there is no need to allocate separate resources for multiple clusters. Cost-Effective: Lower operational costs due to reduced overhead in managing multiple clusters. Reduced complexity in monitoring and logging, leading to potential cost savings. Unified Security and Policy Management: Simplified implementation of security policies, access controls, and compliance measures. Consistent Environment: Ensures a consistent environment across all nodes, simplifying development and deployment processes. Disadvantages: Scalability Limitations: May face scalability issues as the number of edge devices or workloads increases. Single point of failure; if the cluster goes down, all connected devices and services are affected. Performance Bottlenecks: Potential for performance bottlenecks if the cluster becomes overloaded. Limited fault isolation; issues in one part of the cluster can impact the entire system. Geographical Constraints: Less optimal for geographically distributed edge nodes as latency might increase. May not efficiently handle diverse network conditions across different locations.","title":"Single cluster architecture"},{"location":"architecture_overview.html#single-cluster-architecture-advanced","text":"Single cluster architecture(advanced) Architecture improved by involving service mesh for optimal micro service traffic routing. For more info see istio documentation","title":"Single cluster architecture (advanced)"},{"location":"architecture_overview.html#multi-cluster-architecture","text":"Multi cluster architecture Multi cluster deployment that can utilize dUPF metrics (e.g. number of connected users) to decide if the application should be deployed in a neighboring micro cluster Advantages: Scalability and Fault Isolation: Better scalability by distributing workloads across multiple clusters. Improved fault isolation; problems in one cluster do not affect others. Geographical Distribution: Optimized for geographically distributed deployments, reducing latency by placing clusters closer to the edge devices. Can handle diverse network conditions more effectively. Performance Optimization: Easier to optimize performance by dedicating clusters to specific workloads or regions. Enhanced load balancing and resource distribution. Resilience and High Availability: Higher resilience and availability; failure in one cluster can be mitigated by other clusters. Enhanced disaster recovery capabilities. Disadvantages: Increased Complexity: More complex to manage and maintain multiple clusters. Higher administrative overhead in coordinating and synchronizing clusters. Resource Overhead: Requires more resources for cluster management (e.g., control planes for each cluster). Potentially higher infrastructure costs due to multiple clusters. Security and Policy Management: More complex to implement and manage security policies and compliance across multiple clusters. Potential challenges in maintaining consistent security postures across clusters. Inter-Cluster Communication: Requires robust inter-cluster communication mechanisms, which can add to the complexity. More complicated service discovery and networking between clusters.","title":"Multi cluster architecture"},{"location":"code_profiling.html","text":"Code profiling [5 min read] \u00b6 JupyterHub(any Kubernetes) pod \u00b6 Max RAM/CPU \u00b6 Using PromQL to query Prometheus metrics of the pod. Max RAM/CPU/GPU from prometheus_api_client import PrometheusConnect from math import ceil def get_pod_max_cpu_memory_in_prometheus( prometheus_url: str = \"http://prometheus-kube-prometheus-prometheus.monitoring:9090\", container_namespace: str = \"jupyterhub\", pod_name: str = \"jupyter-5uperpalo\", ) -> dict: \"\"\" Function to query max CPU and memory consumption of a specific pod in Kubernetes in the past 10 days. container_memory_working_set_bytes metric returns value per spawned pod id (different pods might have been spawned with the same name in the past 10 days) - only the last is checked For the specific query details see the link below: https://stackoverflow.com/questions/58747562/how-to-get-max-cpu-useage-of-a-pod-in-kubernetes-over-a-time-interval-say-30-da https://stackoverflow.com/a/66778814 Args: prometheus_url (str): Prometheus service URL container_namespace (str): Kubernetes pod namespace name pod_name (str): Kubernetes namespace name Returns: metrics (dict): dictionary with values \"\"\" def round_up(n, decimals=0): multiplier = 10**decimals return math.ceil(n * multiplier) / multiplier prom = PrometheusConnect(url=prometheus_url, disable_ssl=True) pod_name = f\"'{pod_name}'\" container_namespace = f\"'{container_namespace}'\" container_cpu_max_query = f\"max_over_time(sum(rate(container_cpu_usage_seconds_total{{namespace={container_namespace}, pod={pod_name}, container!=''}}[1m]))[10d:1m])\" container_memory_max_query = f\"max_over_time(container_memory_working_set_bytes{{namespace={container_namespace}, pod={pod_name}, container!=''}}[10d])\" cpu_max = prom.custom_query(query=container_cpu_max_query) memory_max = prom.custom_query(query=container_memory_max_query) metrics = { \"cpu\": round_up(float(cpu_max[-1][\"value\"][1]), 2), \"memory\": round_up(float(memory_max[-1][\"value\"][1])/1024**3, 2), } return metrics besteffort_pods_list = [ (\"kepler-7dfh2\", \"kepler\"), (\"nginx-ingress-microk8s-controller-h5kfj\", \"ingress\"), ] for pod, namespace in besteffort_pods_list: metrics = get_pod_max_cpu_memory_in_prometheus( container_namespace=namespace, pod_name=pod, ) print(f\"{pod} ({namespace}), {metrics}\") >>> kepler-7dfh2 (kepler), {'cpu': 0.19, 'memory': 0.07} >>> nginx-ingress-microk8s-controller-h5kfj (ingress), {'cpu': 0.12, 'memory': 0.45} Max GPU \u00b6 In case it is necessary a the Prometheus metrics can be extended by including NVIDIA DCGM exporter in exported metrics and then querying the metrics of interest using similar function to get_pod_max_cpu_memory_in_prometheus . Energy measurements \u00b6 Using Kubernetes Efficient Power Level Exporter (Kepler) . Core/Uncore measurements might not be available(only PKG) depending on Intel architecture. See RAPL section in Kepler metrics explanation. Kepler metrics from datetime import datetime from prometheus_api_client import PrometheusConnect def get_kepler_pod_stats( to_timestamp: float, from_timestamp: float, prometheus_url: str = \"http://prometheus-kube-prometheus-prometheus.monitoring:9090\", container_namespace: str = \"jupyterhub\", pod_name: str = \"jupyter-5uperpalo\", ) -> dict: \"\"\"Function to query Kepler power consumption data of specific pod in Kubernetes. # https://sustainable-computing.io/design/kepler-energy-sources/ # https://github.com/sustainable-computing-io/kepler/blob/1c397ff00b72b5cb1585d0de2cd495c73d88f07a/grafana-dashboards/Kepler-Exporter.json#L299 # https://prometheus.io/docs/prometheus/latest/querying/basics/#time-durations # [metric for metric in prom.all_metrics() if \"kepler\" in metric] Args: to_timestamp (list): 'to' timestamp from_timestamp (list): 'from' timestamp prometheus_url (str): Prometheus service url container_namespace (str): Kubernetes pod namespace name pod_name (str): Kubernetes namespace name Returns: metrics (dict): Kepler metrics of the power consumption of pod in Kubernetes \"\"\" prom = PrometheusConnect(url=prometheus_url, disable_ssl=True) pod_name = f\"'{pod_name}'\" container_namespace = f\"'{container_namespace}'\" time_range_sec = str(int(to_timestamp - from_timestamp)) container_sum_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_core_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_core_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_uncore_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_uncore_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_pkg_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_package_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_dram_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_dram_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_other_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_other_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" container_gpu_query = f\"sum by (pod_name, container_namespace) (irate(kepler_container_gpu_joules_total{{container_namespace={container_namespace}, pod_name={pod_name}}}[{time_range_sec}s] @ {str(to_timestamp)}))\" sum_data = prom.custom_query(query=container_sum_query) core_data = prom.custom_query(query=container_core_query) uncore_data = prom.custom_query(query=container_uncore_query) pkg_data = prom.custom_query(query=container_pkg_query) dram_data = prom.custom_query(query=container_dram_query) other_data = prom.custom_query(query=container_other_query) gpu_data = prom.custom_query(query=container_gpu_query) metrics = { \"from\": datetime.fromtimestamp(from_timestamp).strftime(\"%m/%d/%Y, %H:%M:%S\"), \"to\": datetime.fromtimestamp(to_timestamp).strftime(\"%m/%d/%Y, %H:%M:%S\"), \"sum\": float(sum_data[0][\"value\"][1]), \"core\": float(core_data[0][\"value\"][1]), \"uncore\": float(uncore_data[0][\"value\"][1]), \"pkg\": float(pkg_data[0][\"value\"][1]), \"dram\": float(dram_data[0][\"value\"][1]), \"other\": float(other_data[0][\"value\"][1]), \"gpu\": float(gpu_data[0][\"value\"][1]), } return metrics >>> # prometheus has UTC 00:00 as opposed to Barcelona UTC +02:00 >>> to_timestamp = datetime(2024, 6, 11, 8, 31).timestamp() >>> from_timestamp = datetime(2024, 6, 11, 8, 25).timestamp() >>> >>> get_kepler_pod_stats(to_timestamp=to_timestamp, from_timestamp=from_timestamp) >>> >>> {'from': '06/11/2024, 08:25:00', >>> 'to': '06/11/2024, 08:31:00', >>> 'sum': 45.333333333333336, >>> 'core': 0.0, >>> 'uncore': 0.0, >>> 'pkg': 44.48569999999987, >>> 'dram': 0.8289999999998447, >>> 'other': 0.0, >>> 'gpu': 0.0} Carbon emission monitoring \u00b6 Grafana dashboard provided by Kepler uses predefined natural gas/coal/petroleum conversions from US . See simple implemented formula here with coefficient defined here . The Energy measurements can be easily transformed to Carbon emissions using CodeCarbon methodology . Code \u00b6 RAM/CPU/GPU \u00b6 All-in-one solution by Scalene: https://github.com/plasma-umass/scalene Carbon emission monitoring: \u00b6 https://mlco2.github.io/codecarbon/usage.html# https://github.com/sb-ai-lab/Eco2AI https://github.com/lfwa/carbontracker Code Execution timing \u00b6 Timer decorator Import and prepend the time decorator to log time(into specified log file) that it takes to execute analyzed function/class, e.g.: import logging from functools import wraps from time import time def timer(func): \"\"\"Wrapper to time and log the function execution. Parameters: func: function \"\"\" @wraps(func) def timer_func(*args, **kwargs): start_time = time() value = func(*args, **kwargs) end_time = time() logging.info( f\"Finished {func.__name__} in {(end_time - start_time):.4f} seconds.\" # noqa ) return value return timer_func @timer def training(config: TrainingConfig, custom_params: CustomParameters): [Legacy] RAM \u00b6 Max RAM To get max RAM used during executition of the function import the decorator and put it above it function to log max RAM used into specified logging file, e.g.: NOTE : this applies only to functions and NOT classes or class methods. import logging from functools import wraps from memory_profiler import memory_usage def ram_usage(func): \"\"\"Wrapper to monitor and log RAM usage during the function execution. NOTE: can be applied to function but not to method of a class. Parameters: func: function \"\"\" @wraps(func) def ram_usage_func(*args, **kwargs): ram, value = memory_usage( (func, args, *kwargs), interval=1.0, retval=True, max_usage=True ) logging.info( f\"Finished {func.__name__,}. Max RAM used {(ram / 1000):.4f} GB.\" ) # noqa return value return ram_usage_func from churn_pred.code_profiling import ram_usage @ram_usage def training(config: TrainingConfig, custom_params: CustomParameters): Per line RAM usage analysis To analyze RAM usage per line of the code import profile decorator, put it above the function you want to analyze: from memory_profiler import profile @profile def training(config: TrainingConfig, custom_params: CustomParameters): , run the code using memory_profile command line util, and analyze the RAM usage, e.g.: $ python -m memory_profiler local_run.py Filename: /XXX/main.py Line # Mem usage Increment Occurrences Line Contents ============================================================= 53 230.8 MiB 230.8 MiB 1 @ram_usage 54 @profile 55 def training(config: TrainingConfig, custom_params: CustomParameters): 56 230.9 MiB 0.1 MiB 1 data_config = DataConfig.load(config.data_config) 57 230.9 MiB 0.0 MiB 1 hyperparameters = Hyperparameters.parse_obj(config.hyperparameters) 58 59 687.5 MiB 456.6 MiB 1 queries, df_data = get_data(config, custom_params) 60 687.5 MiB 0.0 MiB 1 (","title":"code profiling"},{"location":"code_profiling.html#code-profiling-5-min-read","text":"","title":"Code profiling [5 min read]"},{"location":"code_profiling.html#jupyterhubany-kubernetes-pod","text":"","title":"JupyterHub(any Kubernetes) pod"},{"location":"code_profiling.html#code","text":"","title":"Code"},{"location":"contributing.html","text":"Code \u00b6 Before making a pushing a code and making a pull request please run codestyle checks and tests ./code_style.sh pytest --doctest-modules churn_pred --cov-report xml --cov-report term --disable-pytest-warnings --cov = eda tests/ Documentation \u00b6 Documentation is created using github pages and mkdocs, see # to build locally cd docs pip install -r requirements.txt mkdocs build # to push to github pages mkdocs gh-deploy # if you want to run webserver locally mkdocs serve","title":"contributing"},{"location":"contributing.html#code","text":"Before making a pushing a code and making a pull request please run codestyle checks and tests ./code_style.sh pytest --doctest-modules churn_pred --cov-report xml --cov-report term --disable-pytest-warnings --cov = eda tests/","title":"Code"},{"location":"contributing.html#documentation","text":"Documentation is created using github pages and mkdocs, see # to build locally cd docs pip install -r requirements.txt mkdocs build # to push to github pages mkdocs gh-deploy # if you want to run webserver locally mkdocs serve","title":"Documentation"},{"location":"data_management.html","text":"Data management \u00b6 The choice of data storage and management options was influenced by the following factors: * time series data (measurements and predictions) -> time series database * easy to administer, popular solution with helm chart available by developers and Python API support -> InfluxDB * responsibility ownership of communication between vehicle and endpoint (v2x communication) on v2x measurement provider, i.e. Idneo -> Idneo choice - Redis * monitoring of energy consumption(carbonn footprint) + CPU/GPU/RAM utilization in Kubernetes -> Prometheus + Kepler + Grafana Local/edge data management \u00b6 Both the vehicle On Board Unit (OBU) and endpoint API are administered by Idneo . V2X vehicle measurements refer to the data that the vehicle uses to communicate its presence on the road. This data is essential in Intelligent Transportation Systems (ITS) to complement and provide the Cooperative Awareness (CA) service, which generates Cooperative Awareness Messages (CAM) according to the European standard ETSI, equivalent to the Basic Safety Message (BSM) of the American standard (SAE). The data shared through V2X in ITS services represents only a small portion of the data available in the vehicle's internal network Notice that generating simple V2X messages with their mandatory data, only requires the public vehicle data. However, a V2X stack has an interface where car manufacturers can insert some private data to enrich the V2X messages (these are optional fields) The data is sampled through the OBDII diagnostic port by constant polling. This data is uploaded to a transactional database Each variable has its operating range and units. For more details, see On-board diagnostics parameter definitions . For a detailed communication description, i.e. how the communication is enforced to stay in an edge node, please see networking section single cluster Kubernetes the Redis database is deployed on every edge node by use of Kubernetes pod assigning options and defining DaemonSets that will run a pod on every selected node multi-cluster Kubernetes multi-cluster environment suggests the use of a small cluster per edge location, in this case, a separate Redis database deployment in each cluster is sufficient Predictions \u00b6 If there is no responsibility ownership division an Inference Service provided by Kserve would be an endpoint for OBU. Compatibility with this scenario is assured by model predict methods . As the Redis database is an endpoint for OBU we use the Redis client channel subscription method to get the latest vehicle measurements and forward the predictions to the central Influx database. Additional notes to \"Local/edge data management\" and \"Predictions\" \u00b6 channel subscription scenario is not \"usual\" as inference service is used indirectly keeping the v2x data on the edge reduces communication overhead and if necessary for analysis it can be (i) queried from the Redis database or (ii) aggregated to InfluxDB by use of Telegram keeping the v2x data on the edge also reduces the possible security/privacy concerns caused by transferring all the data to the central location we do not use Prometheus for predictions storing as Prometheus is designed for scraping metrics and not storing data; storing functionality can improved by Thanos , but the solution is primarily designed for metrics not measurements and predictions we do not use Grafana for visualization of predictions as InfluxDB by default includes User Interface(UI) for visualization and forwarding data to Grafana would cause additional unnecessary data communication System Metrics \u00b6 We use combination of Prometheus + Kepler + Grafana to monitor resource utilization inside the cluster. This data can be later used for downscaling the solution and reducing the carbon footprint in production.","title":"data management"},{"location":"data_management.html#data-management","text":"The choice of data storage and management options was influenced by the following factors: * time series data (measurements and predictions) -> time series database * easy to administer, popular solution with helm chart available by developers and Python API support -> InfluxDB * responsibility ownership of communication between vehicle and endpoint (v2x communication) on v2x measurement provider, i.e. Idneo -> Idneo choice - Redis * monitoring of energy consumption(carbonn footprint) + CPU/GPU/RAM utilization in Kubernetes -> Prometheus + Kepler + Grafana","title":"Data management"},{"location":"data_management.html#localedge-data-management","text":"Both the vehicle On Board Unit (OBU) and endpoint API are administered by Idneo . V2X vehicle measurements refer to the data that the vehicle uses to communicate its presence on the road. This data is essential in Intelligent Transportation Systems (ITS) to complement and provide the Cooperative Awareness (CA) service, which generates Cooperative Awareness Messages (CAM) according to the European standard ETSI, equivalent to the Basic Safety Message (BSM) of the American standard (SAE). The data shared through V2X in ITS services represents only a small portion of the data available in the vehicle's internal network Notice that generating simple V2X messages with their mandatory data, only requires the public vehicle data. However, a V2X stack has an interface where car manufacturers can insert some private data to enrich the V2X messages (these are optional fields) The data is sampled through the OBDII diagnostic port by constant polling. This data is uploaded to a transactional database Each variable has its operating range and units. For more details, see On-board diagnostics parameter definitions . For a detailed communication description, i.e. how the communication is enforced to stay in an edge node, please see networking section single cluster Kubernetes the Redis database is deployed on every edge node by use of Kubernetes pod assigning options and defining DaemonSets that will run a pod on every selected node multi-cluster Kubernetes multi-cluster environment suggests the use of a small cluster per edge location, in this case, a separate Redis database deployment in each cluster is sufficient","title":"Local/edge data management"},{"location":"data_management.html#predictions","text":"If there is no responsibility ownership division an Inference Service provided by Kserve would be an endpoint for OBU. Compatibility with this scenario is assured by model predict methods . As the Redis database is an endpoint for OBU we use the Redis client channel subscription method to get the latest vehicle measurements and forward the predictions to the central Influx database.","title":"Predictions"},{"location":"data_management.html#additional-notes-to-localedge-data-management-and-predictions","text":"channel subscription scenario is not \"usual\" as inference service is used indirectly keeping the v2x data on the edge reduces communication overhead and if necessary for analysis it can be (i) queried from the Redis database or (ii) aggregated to InfluxDB by use of Telegram keeping the v2x data on the edge also reduces the possible security/privacy concerns caused by transferring all the data to the central location we do not use Prometheus for predictions storing as Prometheus is designed for scraping metrics and not storing data; storing functionality can improved by Thanos , but the solution is primarily designed for metrics not measurements and predictions we do not use Grafana for visualization of predictions as InfluxDB by default includes User Interface(UI) for visualization and forwarding data to Grafana would cause additional unnecessary data communication","title":"Additional notes to \"Local/edge data management\" and \"Predictions\""},{"location":"data_management.html#system-metrics","text":"We use combination of Prometheus + Kepler + Grafana to monitor resource utilization inside the cluster. This data can be later used for downscaling the solution and reducing the carbon footprint in production.","title":"System Metrics"},{"location":"development.html","text":"\u00b6 SUstainable and high PErformance COMputing platform Research & Development testbed. R&D Architecture steps: setup full testbed use RaspberryPis to emulate edge nodes send gathered data using Python script and target specific edge nodes (emulation of vehicle moving between base stations) test centralized vs edge computation vs federated learning query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, Testbed environment make pictures of used HW (RaspberyPis, workstation, SUPERCOM servers rack?)","title":"development"},{"location":"development.html#_1","text":"SUstainable and high PErformance COMputing platform Research & Development testbed. R&D Architecture steps: setup full testbed use RaspberryPis to emulate edge nodes send gathered data using Python script and target specific edge nodes (emulation of vehicle moving between base stations) test centralized vs edge computation vs federated learning query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, Testbed environment make pictures of used HW (RaspberyPis, workstation, SUPERCOM servers rack?)","title":""},{"location":"ml_model_development.html","text":"Machine learning model development \u00b6 Centralized training \u00b6 The project is focused on developing a production-ready solution. At the time of writing the documentation, we do not have vehicular measurements that would include labels with vehicle faults. We focus on exploring the best possible overall/engineering solutions as opposed to finding the best-performing model. Centralized training is therefore first choice for implementation. advantages: simplified data management - having all data in one place can simplify data management, preprocessing, and cleaning disadvantages: privacy concerns - ggregating data from multiple sources can lead to significant privacy issues, especially with sensitive information Online/stream vs batch \u00b6 V2X measurements form a stream of data from a vehicle to AI/ML on the edge. Unfortunately, this causes issues with the possibility of a high number(100-10,000?) streams per edge location. Until such a system is implemented we do not know the probable number of streams per edge location and we are unable to define requirements of the system that would be able to process X streams with Y data points per second. Such a system has to be able to process streams either by multi-threading or parallel processing. Also, methods like ADWIN as the window size is reset to the initial value each time vehicle changes edge device as the state of the ML model can't be migrated between locations per vehicle as of the number of vehicles and their traveling speed VS migration time of the model to a new location. For now, we pick the \"easier\" option of batch processing where (i) every sample is treated individually, or (ii) samples from the same vehicle are processed in fixed batch sizes Deep vs \"Traditional\" \u00b6 As we are dealing with strictly tabular data we focus on the tree-based model solution implementation. For detailed answer, we refer a reader to e.g. paper \"Why do tree-based models still outperform deep learning on tabular data?\" [TODO] Federated learning/training \u00b6 We implement a federated learning model to address concerns of privacy and data management in case of multi-cluster solution. advantages: enhanced privacy - raw data never leaves the local devices, reducing privacy risks reduced data transfer - only model updates (rather than raw data) are transmitted, which can be more bandwidth-efficient compliance - easier to comply with data sovereignty and privacy regulations since data remains local fault tolerance - the system can be more resilient to individual server failures since the data and computation are distributed disadvantages: complexity - implementing federated learning is more complex due to the need to manage distributed training, aggregation, and synchronization heterogeneity - variability in data quality and distribution across devices can impact model performance limited computational resources - Local devices may have limited computational power compared to centralized data centers communication overhead - while raw data isn't transferred, frequent model updates can still generate significant communication overhead, especially in large-scale systems graph TB subgraph Cluster1 direction TB Worker1[PySyft Worker Node 1] end subgraph Cluster2 direction TB Worker2[PySyft Worker Node 2] end subgraph Cluster3 direction TB Worker3[PySyft Worker Node 3] end subgraph CentralCluster direction TB CentralServer[PySyft Central Server] end Worker1 <--> CentralServer Worker2 <--> CentralServer Worker3 <--> CentralServer PySyft [PREFERRED] focuses on strong privacy-preserving techniques, offering advanced security features for sensitive data easy to deploy workers/clients Flower emphasizes flexibility and ease of integration with existing machine learning frameworks, suitable for rapid prototyping and deployment more focused on iOS and Android deployment KubeFATE - focused towards secure, scalable enterprise applications with advanced privacy-preserving techniques","title":"ML model dev"},{"location":"ml_model_development.html#machine-learning-model-development","text":"","title":"Machine learning model development"},{"location":"ml_model_development.html#centralized-training","text":"The project is focused on developing a production-ready solution. At the time of writing the documentation, we do not have vehicular measurements that would include labels with vehicle faults. We focus on exploring the best possible overall/engineering solutions as opposed to finding the best-performing model. Centralized training is therefore first choice for implementation. advantages: simplified data management - having all data in one place can simplify data management, preprocessing, and cleaning disadvantages: privacy concerns - ggregating data from multiple sources can lead to significant privacy issues, especially with sensitive information","title":"Centralized training"},{"location":"ml_model_development.html#todo-federated-learningtraining","text":"We implement a federated learning model to address concerns of privacy and data management in case of multi-cluster solution. advantages: enhanced privacy - raw data never leaves the local devices, reducing privacy risks reduced data transfer - only model updates (rather than raw data) are transmitted, which can be more bandwidth-efficient compliance - easier to comply with data sovereignty and privacy regulations since data remains local fault tolerance - the system can be more resilient to individual server failures since the data and computation are distributed disadvantages: complexity - implementing federated learning is more complex due to the need to manage distributed training, aggregation, and synchronization heterogeneity - variability in data quality and distribution across devices can impact model performance limited computational resources - Local devices may have limited computational power compared to centralized data centers communication overhead - while raw data isn't transferred, frequent model updates can still generate significant communication overhead, especially in large-scale systems graph TB subgraph Cluster1 direction TB Worker1[PySyft Worker Node 1] end subgraph Cluster2 direction TB Worker2[PySyft Worker Node 2] end subgraph Cluster3 direction TB Worker3[PySyft Worker Node 3] end subgraph CentralCluster direction TB CentralServer[PySyft Central Server] end Worker1 <--> CentralServer Worker2 <--> CentralServer Worker3 <--> CentralServer PySyft [PREFERRED] focuses on strong privacy-preserving techniques, offering advanced security features for sensitive data easy to deploy workers/clients Flower emphasizes flexibility and ease of integration with existing machine learning frameworks, suitable for rapid prototyping and deployment more focused on iOS and Android deployment KubeFATE - focused towards secure, scalable enterprise applications with advanced privacy-preserving techniques","title":"[TODO] Federated learning/training"},{"location":"networking.html","text":"Networking \u00b6 Description of traffic flow for simple(no Istio) Single cluster traffic flow. Vehicle data processing \u00b6 Vehicle data processing Traffic flow: Vehicle sends measurements to Vehicle API service (provided by Idneo \u2013 data preprocessing +Redis DB) transmission is received by antenna Transmission enters data(IPv4/IPv6) network Data is forwarded to the closest Kubernetes Edge node using (i) Anycast IP address, shared by all Edge Nodes, and (ii) nodePort defined in NodePort service Edge Node receives the data, checks the IP table in kube-proxy, and forwards it to local Pod A as the service is configured with externalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the packet is dropped Vehicle API service forwards the data to the ML prediction service(Kserve inference) Data is forwarded to Pod B as Kserve inference service is configured with internalTrafficPolicy: Local If there is no local Pod for Kserve inference service, the packet is dropped or ML prediction service pod periodically queries local Redis DB in Vehicle API service \u2013 as Vehicle API service is also configured with internalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the queries/requests are dropped Kserve inference service pushes the data into InfluxDB Grafana periodically queries data from InfluxDB Energy consumption monitoring \u00b6 Energy consumption monitoring Traffic flow: Kepler exporter uses eBPF to probe CPU, GPU, RAM performance counters and exposes them over time via HTTP Prometheus periodically queries Kepler exporter for a new data Grafana periodically queries Prometheus for anew data Vehicle data and energy consumption monitoring \u00b6 Vehicle data and energy consumption monitoring Traffic flow: User enters Grafana FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it Grafana GUI comes up in the Users web browser ML model development/update \u00b6 ML model development/update Traffic flow: User enters MLflow FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it to Mlflow GUI where user can check current models User updates the model locally or in cloud and uploads it to MLflow API Mlflow API forwards the model to the backend database - Minio ML model deployment \u00b6 ML model deployment Traffic flow: Admin user instructs Kserve to deploy a model Kserve queries the model from Minio storage Kserve updates/\u201dserves\u201d the model in all Kserver inference Pods","title":"networking"},{"location":"networking.html#networking","text":"Description of traffic flow for simple(no Istio) Single cluster traffic flow.","title":"Networking"},{"location":"networking.html#vehicle-data-processing","text":"Vehicle data processing Traffic flow: Vehicle sends measurements to Vehicle API service (provided by Idneo \u2013 data preprocessing +Redis DB) transmission is received by antenna Transmission enters data(IPv4/IPv6) network Data is forwarded to the closest Kubernetes Edge node using (i) Anycast IP address, shared by all Edge Nodes, and (ii) nodePort defined in NodePort service Edge Node receives the data, checks the IP table in kube-proxy, and forwards it to local Pod A as the service is configured with externalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the packet is dropped Vehicle API service forwards the data to the ML prediction service(Kserve inference) Data is forwarded to Pod B as Kserve inference service is configured with internalTrafficPolicy: Local If there is no local Pod for Kserve inference service, the packet is dropped or ML prediction service pod periodically queries local Redis DB in Vehicle API service \u2013 as Vehicle API service is also configured with internalTrafficPolicy: Local If there is no local Pod for Vehicle API service, the queries/requests are dropped Kserve inference service pushes the data into InfluxDB Grafana periodically queries data from InfluxDB","title":"Vehicle data processing"},{"location":"networking.html#energy-consumption-monitoring","text":"Energy consumption monitoring Traffic flow: Kepler exporter uses eBPF to probe CPU, GPU, RAM performance counters and exposes them over time via HTTP Prometheus periodically queries Kepler exporter for a new data Grafana periodically queries Prometheus for anew data","title":"Energy consumption monitoring"},{"location":"networking.html#vehicle-data-and-energy-consumption-monitoring","text":"Vehicle data and energy consumption monitoring Traffic flow: User enters Grafana FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it Grafana GUI comes up in the Users web browser","title":"Vehicle data and energy consumption monitoring"},{"location":"networking.html#ml-model-developmentupdate","text":"ML model development/update Traffic flow: User enters MLflow FQDN in the web browser and is forwarded to the Ingress controller Ingress controller translates the FQDN to the appropriate service and forwards it to Mlflow GUI where user can check current models User updates the model locally or in cloud and uploads it to MLflow API Mlflow API forwards the model to the backend database - Minio","title":"ML model development/update"},{"location":"networking.html#ml-model-deployment","text":"ML model deployment Traffic flow: Admin user instructs Kserve to deploy a model Kserve queries the model from Minio storage Kserve updates/\u201dserves\u201d the model in all Kserver inference Pods","title":"ML model deployment"},{"location":"testing.html","text":"Real-world testing and data gathering - Parcmotor Castellol\u00ed administered by Cellnex \u00b6 Castellol\u00ed testing architecture Castellol\u00ed testing architecture legend: white - mandatory grey pattern - optional steps: prepare the Kserve model and helm charts make testing requests before the day of testing run the real-world test with vehicles in Castelloli query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, NBC environment make vehicle pictures","title":"testing"},{"location":"testing.html#real-world-testing-and-data-gathering-parcmotor-castelloli-administered-by-cellnex","text":"Castellol\u00ed testing architecture Castellol\u00ed testing architecture legend: white - mandatory grey pattern - optional steps: prepare the Kserve model and helm charts make testing requests before the day of testing run the real-world test with vehicles in Castelloli query and save Kepler stats (CPU,RAM usage + CO2 estimation) from Prometheus query and save measurements and predictions from Prometheus make screenshots: Grafana, NBC environment make vehicle pictures","title":"Real-world testing and data gathering - Parcmotor Castellol\u00ed administered by Cellnex"},{"location":"use_cases.html","text":"Vehicular condition monitoring with robust V2X connectivity - EXTEND \u00b6 Use Case Extend Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. However, wireless transmission of in-vehicle monitoring data comes inadvertently with challenges which involve distortions and missing data owing to the inherently shared wireless medium. Incomplete vehicular data attributed to either increased interference levels, channel attenuation phenomena or sensor malfunctioning may lead to incorrect decision-making and failure to predict maintenance actions. Addressing the degradation of transmitted V2X data is thus essential for high-fidelity estimation of the system network state and informed actions. In this context, machine learning (ML)-empowered signal reconstruction and imputation techniques are expected to obtain accurate estimates of incomplete sensor trajectories by leveraging the intrinsic spatiotemporal synergies of captured information. By deeply investigating the interplay between vehicle dynamics and fault provisioning, faulty vehicular equipment will be timely detected/predicted, improving road safety by preventing critical failures. Vehicular condition monitoring with robust V2X connectivity - DEVISE \u00b6 Use Case Devise Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. Wireless transmission of in-vehicle monitoring data is susceptible to various security threats which may destabilize system operation, degrade network performance, and potentially lead to incorrect decision-making regarding the vehicle condition status. At the same time, the pervasive integration of data-driven techniques expands the attack surface, giving rise to finely targeted, stealthier, and scalable attacks targeting both phases (training, inference) of ML techniques. The mitigation of such threats becomes imperative for the correct estimation of vehicle condition. Condition monitoring data used for subsequent predictive diagnostics should be properly secured such that they do not contain falsified information, while abnormal traffic should be detected and isolated in its entirety. In this context, appropriate security countermeasures are expected to accurately detect malicious content in the transmitted information and ensure the semantic correctness of the aggregated data for trustworthy decision-making. Vehicular condition monitoring with robust V2X connectivity - VERIFY \u00b6 Use Case Extend 01 Use Case Extend 02 Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. The limited computational capabilities of edge nodes pose significant challenges for the realization of computationally efficient V2X communication aimed for condition monitoring. Aiming to achieve dramatically reduced data processing times, over-the-air computing methods and decentralized deployments of AI models aim at rapid knowledge extraction from the vehicular measurement streams and reduction of computational overhead. In this context, over-the-air computing methods will be explored to address the computational limitations and stringent latency requirements in V2X systems by means of over-the-air aggregation. In addition, decentralized methods for predictive diagnosis of vehicles condition will be investigated to leverage the benefits of edge-computing capabilities for efficient inference, aiming at minimizing the vehicle downtime incidents. The integration of both approaches holds the promise of distributed learning parallelization over the wireless compute medium and the realization of data intensive V2X services with stringent performance requirements.","title":"use cases"},{"location":"use_cases.html#vehicular-condition-monitoring-with-robust-v2x-connectivity-extend","text":"Use Case Extend Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. However, wireless transmission of in-vehicle monitoring data comes inadvertently with challenges which involve distortions and missing data owing to the inherently shared wireless medium. Incomplete vehicular data attributed to either increased interference levels, channel attenuation phenomena or sensor malfunctioning may lead to incorrect decision-making and failure to predict maintenance actions. Addressing the degradation of transmitted V2X data is thus essential for high-fidelity estimation of the system network state and informed actions. In this context, machine learning (ML)-empowered signal reconstruction and imputation techniques are expected to obtain accurate estimates of incomplete sensor trajectories by leveraging the intrinsic spatiotemporal synergies of captured information. By deeply investigating the interplay between vehicle dynamics and fault provisioning, faulty vehicular equipment will be timely detected/predicted, improving road safety by preventing critical failures.","title":"Vehicular condition monitoring with robust V2X connectivity - EXTEND"},{"location":"use_cases.html#vehicular-condition-monitoring-with-robust-v2x-connectivity-devise","text":"Use Case Devise Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. Wireless transmission of in-vehicle monitoring data is susceptible to various security threats which may destabilize system operation, degrade network performance, and potentially lead to incorrect decision-making regarding the vehicle condition status. At the same time, the pervasive integration of data-driven techniques expands the attack surface, giving rise to finely targeted, stealthier, and scalable attacks targeting both phases (training, inference) of ML techniques. The mitigation of such threats becomes imperative for the correct estimation of vehicle condition. Condition monitoring data used for subsequent predictive diagnostics should be properly secured such that they do not contain falsified information, while abnormal traffic should be detected and isolated in its entirety. In this context, appropriate security countermeasures are expected to accurately detect malicious content in the transmitted information and ensure the semantic correctness of the aggregated data for trustworthy decision-making.","title":"Vehicular condition monitoring with robust V2X connectivity - DEVISE"},{"location":"use_cases.html#vehicular-condition-monitoring-with-robust-v2x-connectivity-verify","text":"Use Case Extend 01 Use Case Extend 02 Description: A C-V2X infrastructure provides coverage to the connected vehicle throughout its trajectory for monitoring purposes. The limited computational capabilities of edge nodes pose significant challenges for the realization of computationally efficient V2X communication aimed for condition monitoring. Aiming to achieve dramatically reduced data processing times, over-the-air computing methods and decentralized deployments of AI models aim at rapid knowledge extraction from the vehicular measurement streams and reduction of computational overhead. In this context, over-the-air computing methods will be explored to address the computational limitations and stringent latency requirements in V2X systems by means of over-the-air aggregation. In addition, decentralized methods for predictive diagnosis of vehicles condition will be investigated to leverage the benefits of edge-computing capabilities for efficient inference, aiming at minimizing the vehicle downtime incidents. The integration of both approaches holds the promise of distributed learning parallelization over the wireless compute medium and the realization of data intensive V2X services with stringent performance requirements.","title":"Vehicular condition monitoring with robust V2X connectivity - VERIFY"},{"location":"idneo/datasets.html","text":"Datasets \u00b6 DS1 : Ignition on but stopped DS2 : national road. ~ 90km/h DS3 : highway. ~ 120km/h Data structure \u00b6 STAMP : Time delay in seconds after starting the log. STAMP Sensor 1.02 value 2.48 value 3.95 value","title":"datasets"},{"location":"idneo/datasets.html#datasets","text":"DS1 : Ignition on but stopped DS2 : national road. ~ 90km/h DS3 : highway. ~ 120km/h","title":"Datasets"},{"location":"idneo/datasets.html#data-structure","text":"STAMP : Time delay in seconds after starting the log. STAMP Sensor 1.02 value 2.48 value 3.95 value","title":"Data structure"},{"location":"idneo/vehicle_injector_tool.html","text":"Vehicle Injector Tool \u00b6 This tool facilitates the incorporation of real data from a vehicle's sensors, stored in csv Datasets. Its purpose is to facilitate the development of algorithms responsible for processing the information that is collected and stored in this REDIS database within the Success6G project. graph LR Client(VEHICLE <br> INJECTOR) -- TCP/IP <br> (63790 / 127.0.0.1)--> Database(REDIS <br> DATABASE); click Client \"https://github.com/5uperpalo/success6g-edge/blob/main/tools/vehicle/vehicle_injector.py\" click Database \"https://github.com/5uperpalo/success6g-edge/blob/main/configs/edge_redis.yaml\" Dataset[Dataset File] --> Client classDef orange fill:#f96,stroke:#333,stroke-width:2px class Dataset orange Usage \u00b6 Install the Redis database in your local development setup. docker_compose -d configs/edge_redis.yaml Verify docker container is running docker ps -a Run \u201cVehicle Injector\u201c script with dataset and station-id script: python3 vehicle_injector.py -f","title":"vehicle injector tool"},{"location":"idneo/vehicle_injector_tool.html#vehicle-injector-tool","text":"This tool facilitates the incorporation of real data from a vehicle's sensors, stored in csv Datasets. Its purpose is to facilitate the development of algorithms responsible for processing the information that is collected and stored in this REDIS database within the Success6G project. graph LR Client(VEHICLE <br> INJECTOR) -- TCP/IP <br> (63790 / 127.0.0.1)--> Database(REDIS <br> DATABASE); click Client \"https://github.com/5uperpalo/success6g-edge/blob/main/tools/vehicle/vehicle_injector.py\" click Database \"https://github.com/5uperpalo/success6g-edge/blob/main/configs/edge_redis.yaml\" Dataset[Dataset File] --> Client classDef orange fill:#f96,stroke:#333,stroke-width:2px class Dataset orange","title":"Vehicle Injector Tool"},{"location":"idneo/vehicle_injector_tool.html#usage","text":"Install the Redis database in your local development setup. docker_compose -d configs/edge_redis.yaml Verify docker container is running docker ps -a Run \u201cVehicle Injector\u201c script with dataset and station-id script: python3 vehicle_injector.py -f","title":"Usage"},{"location":"inference_model/eda.html","text":"Preprocess data \u00b6 analysis \u00b6 init_check \u00b6 init_check ( df , identifier = None , cat_cols = None , cont_cols = None , verbose = False , ) Procedure to check: * duplicated rows in teh dataset * general stats of numerical features * general stats of categorical features Parameters: df ( DataFrame ) \u2013 pandas dataframe identifier ( str ) \u2013 column which identifies unique user IDs cat_cols ( list ) \u2013 categorical features in the dataset cont_cols ( list ) \u2013 numerical features in the dataset Returns: duplicated_ids ( int ) \u2013 cont_cols_desc ( pd . DataFrame ) \u2013 cat_cols_desc ( pd . DataFrame ) \u2013 Source code in inference_model/eda/features/analysis.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 def init_check ( df : pd . DataFrame , identifier : Optional [ str ] = None , cat_cols : Optional [ list ] = None , cont_cols : Optional [ list ] = None , verbose : bool = False , ) -> Tuple [ int , pd . DataFrame , pd . DataFrame ]: \"\"\"Procedure to check: * duplicated rows in teh dataset * general stats of numerical features * general stats of categorical features Args: df (DataFrame): pandas dataframe identifier (str): column which identifies unique user IDs cat_cols (list): categorical features in the dataset cont_cols (list): numerical features in the dataset Returns: duplicated_ids (int): cont_cols_desc (pd.DataFrame): cat_cols_desc (pd.DataFrame): \"\"\" data = df . copy () if identifier : duplicated_ids = data [ identifier ] . duplicated () . sum () if verbose : print ( \"[CHECK] Number of duplicated ids: {} \" . format ( duplicated_ids )) else : duplicated_ids = None if cont_cols : cont_cols_f = intsec ( data . columns . values , cont_cols ) cont_cols_desc = data [ cont_cols_f ] . describe () . transpose () if verbose : print ( \"[CHECK] Numerical columns\" ) with pd . option_context ( \"display.precision\" , 2 ): print ( cont_cols_desc ) else : cont_cols_desc = None if cat_cols : cat_cols_f = intsec ( data . columns . values , cat_cols ) cat_cols_desc = data [ cat_cols_f ] . describe () . transpose () if verbose : print ( \"[CHECK] Categorical columns\" ) with pd . option_context ( \"display.precision\" , 2 ): print ( cat_cols_desc ) else : cat_cols_desc = None return duplicated_ids , cont_cols_desc , cat_cols_desc missing \u00b6 missing ( df , scale = 'linear' , plot = False ) Procedure to check fraction of missing values in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: missing_val_frac ( DataFrame ) \u2013 sorted dataframe with fraction of missing values per feature fig ( Figure ) \u2013 plot with sorted fractions of missing values in each column Source code in inference_model/eda/features/analysis.py 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 def missing ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to check fraction of missing values in the dataset. Args: df (DataFrame): pandas dataframe scale (str): y scale of the plot plot (bool): whether to output the plot Returns: missing_val_frac (DataFrame): sorted dataframe with fraction of missing values per feature fig (Figure): plot with sorted fractions of missing values in each column \"\"\" data = df . copy () missing_val_frac = data . isna () . sum () / len ( data ) missing_val_frac . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( missing_val_frac , ax , title = \"Fraction of missing values in the dataset\" , ) else : fig = None ax . set_yscale ( scale ) return missing_val_frac , fig zero \u00b6 zero ( df , scale = 'linear' , plot = False ) Procedure to check fraction of zero values in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: zero_val_frac ( DataFrame ) \u2013 sorted dataframe with fraction of '0' values per feature fig ( Figure ) \u2013 plot with sorted fractions of '0' values in each column Source code in inference_model/eda/features/analysis.py 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 def zero ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to check fraction of zero values in the dataset. Args: df (DataFrame): pandas dataframe scale (str): y scale of the plot plot (bool): whether to output the plot Returns: zero_val_frac (DataFrame): sorted dataframe with fraction of '0' values per feature fig (Figure): plot with sorted fractions of '0' values in each column \"\"\" data = df . copy () zero_val_frac = data . isin ([ 0 ]) . sum () / len ( data ) zero_val_frac . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( zero_val_frac , ax , title = \"Fraction of zero values in the dataset\" ) else : fig = None ax . set_yscale ( scale ) return zero_val_frac , fig nunique \u00b6 nunique ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their number of unique values. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted number of unique values in features Source code in inference_model/eda/features/analysis.py 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 def nunique ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their number of unique values. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted number of unique values in features \"\"\" data = df . copy () data_nunique = data . nunique () data_nunique . sort_values ( ascending = True , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( data_nunique . dropna (), ax , title = \"Features number of unique values (NA are dropped)\" , ) else : fig = None ax . set_yscale ( scale ) return data_nunique , fig std \u00b6 std ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their variance. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted standard deviation of continuous features Source code in inference_model/eda/features/analysis.py 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def std ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their variance. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted standard deviation of continuous features \"\"\" data = df . copy () data_std = data . std () data_std . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( data_std . dropna (), ax , title = \"Features variance (NA are dropped)\" ) else : fig = None ax . set_yscale ( scale ) return data_std , fig entropy \u00b6 entropy ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their entropy. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted entropy of the features Source code in inference_model/eda/features/analysis.py 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 def entropy ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their entropy. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted entropy of the features \"\"\" data = df . copy () data = data . astype ( str ) col_entropies = data . apply ( entropy_calc , axis = 0 ) col_entropies . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( col_entropies . dropna (), ax , title = \"Features entropies (NA are dropped)\" ) else : fig = None ax . set_yscale ( scale ) return col_entropies , fig plotting \u00b6 cross_correlation \u00b6 cross_correlation ( df , n = 10 , verbose = False ) Procedure to calculate and plot cross-correlation of features in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe verbose ( bool ) \u2013 show n most correlated features n ( int ) \u2013 number correlated features in verbose output Returns: fig ( Figure ) \u2013 heatmap of continuous features cross correlations Source code in inference_model/eda/features/plotting.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def cross_correlation ( df : pd . DataFrame , n : int = 10 , verbose : bool = False , ) -> Figure : \"\"\"Procedure to calculate and plot cross-correlation of features in the dataset. Args: df (DataFrame): pandas dataframe verbose (bool): show n most correlated features n (int): number correlated features in verbose output Returns: fig (Figure): heatmap of continuous features cross correlations \"\"\" data = df . copy () # corrwith uses numpy which does not like pandas Float dtypes corr_matrix = data . astype ( float ) . corr () upper_stacked_sorted = ( corr_matrix . abs () . where ( np . triu ( np . ones ( corr_matrix . shape ), k = 1 ) . astype ( \"bool\" )) . stack () . sort_values ( ascending = False ) ) fig , ax = plt . subplots ( figsize = ( 10 , 10 )) ax . set_title ( \"Cross-correlation matrix\" , size = 20 ) sns . set ( font_scale = 1.0 ) sns . heatmap ( corr_matrix , ax = ax , cbar = True , square = True , # linewidths=0.5, fmt = \".2f\" , ) if verbose : print ( \"Top {} absolute correlations\" . format ( n )) print ( upper_stacked_sorted [: n ]) return fig distributions \u00b6 distributions ( df , low_per_cut = 0 , high_per_cut = 1 , type = \"box\" ) Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Parameters: df ( DataFrame ) \u2013 pandas dataframe low_per_cut ( float ) \u2013 lower percentile where to cut the plot for better readability high_per_cut ( float ) \u2013 higher percentile where to cut the plot for better readability type ( str ) \u2013 type of distribution plot Returns: fig ( Figure ) \u2013 ditribution plot per each feature Source code in inference_model/eda/features/plotting.py 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 def distributions ( df : pd . DataFrame , low_per_cut : float = 0 , high_per_cut : float = 1 , type : Literal [ \"box\" , \"violin\" ] = \"box\" , ) -> Figure : \"\"\"Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Args: df (DataFrame): pandas dataframe low_per_cut (float): lower percentile where to cut the plot for better readability high_per_cut (float): higher percentile where to cut the plot for better readability type (str): type of distribution plot Returns: fig (Figure): ditribution plot per each feature \"\"\" data = df . copy () cols = data . columns . values data = data . fillna ( 0 ) . astype ( float ) fig , ax = plt . subplots ( len ( cols ), 1 , figsize = ( 16 , len ( cols ) * 2 )) fig . suptitle ( \"Distribution of feature values\" , size = 20 ) for col , i in zip ( cols , range ( len ( cols ))): if type == \"violin\" : sns . violinplot ( ax = ax [ i ], x = col , orient = \"h\" , # cut=0, # showextrem=False, data = data , ) if type == \"box\" : sns . boxplot ( ax = ax [ i ], x = col , orient = \"h\" , data = data , ) ax [ i ] . set_xlim ( data [ col ] . quantile ( low_per_cut ), data [ col ] . quantile ( high_per_cut ) ) ax [ i ] . get_yaxis () . set_visible ( False ) plt . subplots_adjust ( hspace = 0.9 ) return fig analysis \u00b6 correlation \u00b6 correlation ( df , target , scale = 'linear' , plot = False ) Procedure to plot most correlated numerical features with target column. Parameters: df ( pd . DataFrame ) \u2013 pandas dataframe target ( pd . Series ) \u2013 target values scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: sorted_corr_cols ( pd . DataFrame ) \u2013 sorted dataframe with feature and target value correlation fig ( Figure ) \u2013 sorted bar plot with feature and target value correlation Source code in inference_model/eda/target/analysis.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 def correlation ( df : pd . DataFrame , target : pd . Series , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot most correlated numerical features with target column. Args: df (pd.DataFrame): pandas dataframe target (pd.Series): target values scale (str): y scale of the plot plot (bool): whether to output the plot Returns: sorted_corr_cols (pd.DataFrame): sorted dataframe with feature and target value correlation fig (Figure): sorted bar plot with feature and target value correlation \"\"\" data = df . copy () target_col = target . name correlation_df = pd . concat ([ data , target ], axis = 1 ) . corr () sorted_corr_cols = correlation_df [ target_col ] . drop ( index = [ target_col ]) sorted_corr_cols . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( sorted_corr_cols , ax , title = f \"Sorted features by their correlation with { target_col } \" , ) else : fig = None ax . set_yscale ( scale ) return sorted_corr_cols , fig plotting \u00b6 prob_distrib_per_class \u00b6 prob_distrib_per_class ( predicted_probs , actual , task ) Procedure to plot probability density distributions per class from LightGBM predictions. Parameters: predicted_probs ( ndarray ) \u2013 predicted probs actual ( ndarray ) \u2013 ground truth classes task ( str ) \u2013 type of task Returns: fig ( Figure ) \u2013 probability density ditributions plot per each class Source code in inference_model/eda/target/plotting.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def prob_distrib_per_class ( predicted_probs : np . ndarray , actual : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ], ) -> Figure : \"\"\"Procedure to plot probability density distributions per class from LightGBM predictions. Args: predicted_probs (ndarray): predicted probs actual (ndarray): ground truth classes task (str): type of task Returns: fig (Figure): probability density ditributions plot per each class \"\"\" if task == \"binary\" : temp = pd . DataFrame ({ \"predicted_proba\" : predicted_probs , \"actual\" : actual }) fig , ax = plt . subplots ( figsize = ( 3 , 2 )) fig . suptitle ( \"Predicted probability density per class\" ) for label , alpha , color in zip ([ 0 , 1 ], [ 1 , 0.7 ], [ \"black\" , \"red\" ]): ax . hist ( temp [ temp [ \"actual\" ] == label ][ \"predicted_proba\" ], density = True , bins = np . linspace ( 0 , 1 , 10 ), alpha = alpha , color = color , ec = \"k\" , ) ax . set_xlim ([ 0 , 1 ]) ax . set_xlabel ( \"probability\" ) ax . set_ylabel ( \"probability_density\" ) elif task == \"multiclass\" : temp = pd . DataFrame ( { \"predicted_proba\" : np . take_along_axis ( predicted_probs , np . vstack ( actual ), axis = 1 # type: ignore ) . flatten (), \"actual\" : actual , } ) # Dictionary of color for each label color_d = dict ( zip_longest ( temp [ \"actual\" ] . unique (), plt . rcParams [ \"axes.prop_cycle\" ] . by_key ()[ \"color\" ], ) ) n_classes = temp [ \"actual\" ] . nunique () fig , ax = plt . subplots ( ncols = n_classes , figsize = ( n_classes * 3 , 2 ), sharex = True , sharey = True ) fig . suptitle ( \"Predicted probability density per class\" ) plt . subplots_adjust ( wspace = 0.3 , top = 0.75 ) for i , ( label , gp ) in enumerate ( temp . groupby ( \"actual\" )): ax [ i ] . hist ( gp [ \"predicted_proba\" ], density = True , bins = np . linspace ( 0 , 1 , 10 ), color = color_d [ label ], ec = \"k\" , ) ax [ i ] . set_title ( label ) ax [ i ] . set_xlim ([ 0 , 1 ]) ax [ i ] . set_xlabel ( \"probability\" ) ax [ i ] . set_ylabel ( \"probability_density\" ) return fig distributions_in_binary_cls \u00b6 distributions_in_binary_cls ( df , target , low_per_cut = 0 , high_per_cut = 1 ) Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Parameters: df ( DataFrame ) \u2013 pandas dataframe cont_cols ( list ) \u2013 numerical features in the dataset target ( pd . Series ) \u2013 target values, i.e. binary classes Returns: fig ( Figure ) \u2013 ditribution plot per each feature Source code in inference_model/eda/target/plotting.py 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 def distributions_in_binary_cls ( df : pd . DataFrame , target : pd . Series , low_per_cut : float = 0 , high_per_cut : float = 1 , ) -> Figure : \"\"\"Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Args: df (DataFrame): pandas dataframe cont_cols (list): numerical features in the dataset target (pd.Series): target values, i.e. binary classes Returns: fig (Figure): ditribution plot per each feature \"\"\" data = pd . concat ([ df , target ], axis = 1 ) cols = data . columns . values data [ \"dummy\" ] = 0 data = data . fillna ( 0 ) . astype ( float ) fig , ax = plt . subplots ( len ( cols ), 1 , figsize = ( 16 , len ( cols ) * 2 )) fig . suptitle ( f \"Distribution of feature values splitted by { target . name } \" , size = 20 ) for col , i in zip ( cols , range ( len ( cols ))): if col != target . name : sns . violinplot ( ax = ax [ i ], hue = target . name , x = col , y = \"dummy\" , orient = \"h\" , cut = 0 , showextrem = False , split = True , data = data , ) ax [ i ] . get_legend () . remove () ax [ i ] . set_xlim ( data [ col ] . quantile ( low_per_cut ), data [ col ] . quantile ( high_per_cut ) ) ax [ i ] . get_yaxis () . set_visible ( False ) plt . subplots_adjust ( hspace = 0.9 , top = 0.97 ) return fig plotting \u00b6 bar_plot \u00b6 bar_plot ( df , ax , title ) Helper method for unified bar plot Parameters: df ( DataFrame ) \u2013 dataframe to plot ax ( Axes ) \u2013 axes defining where to plot it Returns: adjusted_plot ( Axes ) \u2013 adjusted axes Source code in inference_model/eda/plotting.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 def bar_plot ( df : pd . DataFrame , ax : Axes , title : str ) -> Axes : \"\"\"Helper method for unified bar plot Args: df (DataFrame): dataframe to plot ax (Axes): axes defining where to plot it Returns: adjusted_plot (Axes): adjusted axes \"\"\" data = df . copy () ax . set_title ( title , size = 20 ) ax . tick_params ( labelsize = 8 ) ax . set_xlabel ( \"sorted features\" , size = 12 ) return data . plot . bar ( ax = ax ) general_utils \u00b6 intsec \u00b6 intsec ( list1 , list2 ) Simple intesection of two lists. Parameters: list1 ( list ) \u2013 list1 list2 ( list ) \u2013 list2 Returns: list ( list ) \u2013 intersection of lists Source code in inference_model/eda/general_utils.py 6 7 8 9 10 11 12 13 14 def intsec ( list1 : list , list2 : list ) -> list : \"\"\"Simple intesection of two lists. Args: list1 (list): list1 list2 (list): list2 Returns: list (list): intersection of lists \"\"\" return list ( set . intersection ( set ( list1 ), set ( list2 ))) entropy_calc \u00b6 entropy_calc ( labels , base = np . e ) Computes entropy of both continuous and categorical features. Shamelessly stolen from : https://stackoverflow.com/a/45091961 Parameters: labels ( list , ndarray , Series ) \u2013 list of values Returns: ent ( float ) \u2013 entropy of the list of values Source code in inference_model/eda/general_utils.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def entropy_calc ( labels : list , base : float = np . e ) -> float : \"\"\"Computes entropy of both continuous and categorical features. Shamelessly stolen from : https://stackoverflow.com/a/45091961 Args: labels (list, ndarray, Series): list of values Returns: ent (float): entropy of the list of values \"\"\" n_labels = len ( labels ) value , counts = np . unique ( labels , return_counts = True ) probs = counts / n_labels n_classes = np . count_nonzero ( probs ) ent = float ( 0 ) if n_classes > 1 : for i in probs : ent -= i * math . log ( i , base ) return ent","title":"eda"},{"location":"inference_model/eda.html#preprocess-data","text":"","title":"Preprocess data"},{"location":"inference_model/eda.html#inference_model.eda.features.analysis","text":"","title":"analysis"},{"location":"inference_model/eda.html#inference_model.eda.features.plotting","text":"","title":"plotting"},{"location":"inference_model/eda.html#inference_model.eda.target.analysis","text":"","title":"analysis"},{"location":"inference_model/eda.html#inference_model.eda.target.plotting","text":"","title":"plotting"},{"location":"inference_model/eda.html#inference_model.eda.plotting","text":"","title":"plotting"},{"location":"inference_model/eda.html#inference_model.eda.general_utils","text":"","title":"general_utils"},{"location":"inference_model/preprocessing.html","text":"Preprocess data \u00b6 label_encoder \u00b6 LabelEncoder \u00b6 LabelEncoder ( columns_to_encode = None ) Bases: object Label Encode categorical values for multiple columns at once NOTE : Shamlessly copied from https://github.com/jrzaurin/pytorch-widedeep NOTE : LabelEncoder reserves 0 for unseen new categories. This is convenient when defining the embedding layers, since we can just set padding idx to 0. Parameters: columns_to_encode ( list, Optional, default = None ) \u2013 List of strings containing the names of the columns to encode. If None all columns of type object in the dataframe will be label encoded. Attributes: encoding_dict ( Dict ) \u2013 Dictionary containing the encoding mappings in the format, e.g. : {'colname1': {'cat1': 1, 'cat2': 2, ...}, 'colname2': {'cat1': 1, 'cat2': 2, ...}, ...} # noqa inverse_encoding_dict(Dict) ( Dict ) \u2013 Dictionary containing the inverse encoding mappings in the format, e.g. : {'colname1': {1: 'cat1', 2: 'cat2', ...}, 'colname2': {1: 'cat1', 2: 'cat2', ...}, ...} # noqa Source code in inference_model/preprocessing/label_encoder.py 29 30 31 32 33 def __init__ ( self , columns_to_encode : Optional [ List [ str ]] = None , ): self . columns_to_encode = columns_to_encode fit \u00b6 fit ( df ) Creates encoding attributes Returns: LabelEncoder ( LabelEncoder ) \u2013 LabelEncoder fitted object Source code in inference_model/preprocessing/label_encoder.py 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 def fit ( self , df : pd . DataFrame ) -> \"LabelEncoder\" : \"\"\"Creates encoding attributes Returns: LabelEncoder: `LabelEncoder` fitted object \"\"\" df_inp = df . copy () if self . columns_to_encode is None : self . columns_to_encode = list ( df_inp . select_dtypes ( include = [ \"object\" ]) . columns ) else : # sanity check to make sure all categorical columns are in an adequate # format for col in self . columns_to_encode : df_inp [ col ] = df_inp [ col ] . astype ( \"O\" ) unique_column_vals = dict () for c in self . columns_to_encode : unique_column_vals [ c ] = df_inp [ c ] . unique () self . encoding_dict = dict () # leave 0 for padding/\"unseen\" categories idx = 1 for k , v in unique_column_vals . items (): self . encoding_dict [ k ] = { o : i + idx for i , o in enumerate ( unique_column_vals [ k ]) } idx = 1 self . inverse_encoding_dict = dict () for c in self . encoding_dict : self . inverse_encoding_dict [ c ] = { v : k for k , v in self . encoding_dict [ c ] . items () } self . inverse_encoding_dict [ c ][ 0 ] = \"unseen\" return self transform \u00b6 transform ( df ) Label Encoded the categories in columns_to_encode Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Source code in inference_model/preprocessing/label_encoder.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 def transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Label Encoded the categories in `columns_to_encode` Returns: pd.DataFrame: label-encoded dataframe \"\"\" try : self . encoding_dict except AttributeError : raise NotFittedError ( \"This LabelEncoder instance is not fitted yet. \" \"Call 'fit' with appropriate arguments before using this LabelEncoder.\" ) df_inp = df . copy () # sanity check to make sure all categorical columns are in an adequate # format for col in self . columns_to_encode : # type: ignore df_inp [ col ] = df_inp [ col ] . astype ( \"O\" ) for k , v in self . encoding_dict . items (): df_inp [ k ] = df_inp [ k ] . apply ( lambda x : v [ x ] if x in v . keys () else 0 ) return df_inp fit_transform \u00b6 fit_transform ( df ) Combines fit and transform Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd . DataFrame ({ 'col1' : [ 1 , 2 , 3 ], 'col2' : [ 'me' , 'you' , 'him' ]}) >>> columns_to_encode = [ 'col2' ] >>> encoder = LabelEncoder ( columns_to_encode ) >>> encoder . fit_transform ( df ) col1 col2 0 1 1 1 2 2 2 3 3 >>> encoder . encoding_dict {'col2': {'me': 1, 'you': 2, 'him': 3}} Source code in inference_model/preprocessing/label_encoder.py 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 def fit_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Combines `fit` and `transform` Returns: pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']}) >>> columns_to_encode = ['col2'] >>> encoder = LabelEncoder(columns_to_encode) >>> encoder.fit_transform(df) col1 col2 0 1 1 1 2 2 2 3 3 >>> encoder.encoding_dict {'col2': {'me': 1, 'you': 2, 'him': 3}} \"\"\" return self . fit ( df ) . transform ( df ) inverse_transform \u00b6 inverse_transform ( df ) Returns the original categories Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd . DataFrame ({ 'col1' : [ 1 , 2 , 3 ], 'col2' : [ 'me' , 'you' , 'him' ]}) >>> columns_to_encode = [ 'col2' ] >>> encoder = LabelEncoder ( columns_to_encode ) >>> df_enc = encoder . fit_transform ( df ) >>> encoder . inverse_transform ( df_enc ) col1 col2 0 1 me 1 2 you 2 3 him Source code in inference_model/preprocessing/label_encoder.py 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def inverse_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Returns the original categories Returns: pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from data_preparation.label_encoder import LabelEncoder >>> df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']}) >>> columns_to_encode = ['col2'] >>> encoder = LabelEncoder(columns_to_encode) >>> df_enc = encoder.fit_transform(df) >>> encoder.inverse_transform(df_enc) col1 col2 0 1 me 1 2 you 2 3 him \"\"\" for k , v in self . inverse_encoding_dict . items (): df [ k ] = df [ k ] . apply ( lambda x : v [ x ]) return df preprocess_data \u00b6 drop_high_nan_cols \u00b6 drop_high_nan_cols ( df , threshold = 0.8 , verbose = False ) Returns dataframe without columns that have ratio of missingness above threshold. Parameters: df ( pd . DataFrame ) \u2013 input dataframe threshold ( float = 0.8 ) \u2013 ratio of missingness applied per column verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 def drop_high_nan_cols ( df : pd . DataFrame , threshold : float = 0.8 , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without columns that have ratio of missingness above threshold. Args: df (pd.DataFrame): input dataframe threshold (float = 0.8): ratio of missingness applied per column verbose (bool): whether the output should be verbose \"\"\" n_rows = df . shape [ 0 ] nan_fraction_per_col = df . apply ( lambda x : x . isna () . sum () / n_rows , axis = 0 ) high_nan_percentage_cols = nan_fraction_per_col [ nan_fraction_per_col > threshold ] . index . values df = df . drop ( high_nan_percentage_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} columns with fraction of NaN above threshold = {}. Affected columns: {} \"\"\" . format ( len ( high_nan_percentage_cols ), threshold , high_nan_percentage_cols ) ) return df drop_constant_cols \u00b6 drop_constant_cols ( df , verbose = False ) Returns dataframe without constant columns, i.e. those with just 1 unique value for all rows. Source code in inference_model/preprocessing/preprocess_data.py 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 def drop_constant_cols ( df : pd . DataFrame , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without constant columns, i.e. those with just 1 unique value for all rows.\"\"\" nunique_per_col = df . apply ( lambda x : x . nunique (), axis = 0 ) const_cols = nunique_per_col [ nunique_per_col == 1 ] . index . values df = df . drop ( const_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} constant columns. Affected columns: {} \"\"\" . format ( len ( const_cols ), const_cols ) ) return df drop_high_uq_cat_cols \u00b6 drop_high_uq_cat_cols ( df , cat_cols , uq_val_count , verbose = False ) Returns dataframe without categorical columns that have too many unique values Parameters: df ( pd . DataFrame ) \u2013 input dataframe cat_cols ( list ) \u2013 list of categorical columns uq_val_count ( int ) \u2013 unique value count verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 def drop_high_uq_cat_cols ( df : pd . DataFrame , cat_cols : list , uq_val_count : int , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without categorical columns that have too many unique values Args: df (pd.DataFrame): input dataframe cat_cols (list): list of categorical columns uq_val_count (int): unique value count verbose (bool): whether the output should be verbose \"\"\" nunique_per_col = df [ cat_cols ] . apply ( lambda x : x . nunique (), axis = 0 ) high_uq_value_cat_cols = nunique_per_col [ nunique_per_col > uq_val_count ] . index . values df = df . drop ( high_uq_value_cat_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} high unique value columns. Affected columns: {} \"\"\" . format ( len ( high_uq_value_cat_cols ), high_uq_value_cat_cols ) ) return df drop_highly_correlated_columns \u00b6 drop_highly_correlated_columns ( df , cont_cols , crosscorr_val = 0.95 , verbose = False ) Returns dataframe without highly correlated columns, cross correlation is evaluated with crosscorr_val. Parameters: df ( pd . DataFrame ) \u2013 input dataframe cont_cols ( list ) \u2013 list of columns to evaluate correlation for crosscorr_val ( float = 0.95 ) \u2013 threshold value of correlation verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 def drop_highly_correlated_columns ( df : pd . DataFrame , cont_cols : list , crosscorr_val : float = 0.95 , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Returns dataframe without highly correlated columns, cross correlation is evaluated with crosscorr_val. Args: df (pd.DataFrame): input dataframe cont_cols (list): list of columns to evaluate correlation for crosscorr_val (float = 0.95): threshold value of correlation verbose (bool): whether the output should be verbose \"\"\" corr_matrix = df [ cont_cols ] . corr () . abs () upper = corr_matrix . where ( np . triu ( np . ones ( corr_matrix . shape ), k = 1 ) . astype ( \"bool\" )) upper_above = upper . apply ( lambda x : any ( x > crosscorr_val ), axis = 1 ) to_drop = upper_above [ upper_above ] . index . values df = df . drop ( to_drop , axis = 1 ) if verbose : print ( \"\"\" Dropped {} highly correlated columns. Affected columns: {} \"\"\" . format ( len ( to_drop ), to_drop ) ) return df nan_with_unknown_imputer \u00b6 nan_with_unknown_imputer ( df , columns , fill_token = \"unknown\" , verbose = False ) Fills NAs with surrogate string, 'unknown' is default value used, it can be customized. Parameters: df ( pd . DataFrame ) \u2013 input dataframe columns ( List [ str ] ) \u2013 ist of columns that will be filled fillna_token ( str = \"unknown\" ) \u2013 string used to replace NAs verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 def nan_with_unknown_imputer ( df : pd . DataFrame , columns : List [ str ], fill_token : str = \"unknown\" , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Fills NAs with surrogate string, 'unknown' is default value used, it can be customized. Args: df (pd.DataFrame): input dataframe columns (List[str]): ist of columns that will be filled fillna_token (str = \"unknown\"): string used to replace NAs verbose (bool): whether the output should be verbose \"\"\" df = df . copy () if verbose : sum_nan_vals = df [ columns ] . isna () . sum () nans_cols = [] for c in columns : nans_cols . append (( c , df [ c ] . isna () . sum ())) for c in columns : df [ c ] = df [ c ] . astype ( object ) . fillna ( fill_token ) dfc = df . copy () dfc [ columns ] = dfc [ columns ] . apply ( lambda x : x . astype ( object ) . fillna ( fill_token )) if verbose : print ( \"\"\" Imputed {} NaN values with {}. Affected columns (col, num_NaNs): {} \"\"\" . format ( sum_nan_vals , fill_token , nans_cols ) ) return df nan_with_number_imputer \u00b6 nan_with_number_imputer ( df , columns , fill_number =- 1.0 , verbose = False ) Fills NAs with surrogate float, -1 is default value used, it can be customized. Parameters: df ( pd . DataFrame ) \u2013 input dataframe columns ( List [ str ] ) \u2013 list of columns that will be filled fill_number ( float = -1 ) \u2013 number used to replace NAs. Defaults to verbose ( bool ) \u2013 whether the output should be verbose Source code in inference_model/preprocessing/preprocess_data.py 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 def nan_with_number_imputer ( df : pd . DataFrame , columns : List [ str ], fill_number : float = - 1.0 , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Fills NAs with surrogate float, -1 is default value used, it can be customized. Args: df (pd.DataFrame): input dataframe columns (List[str]): list of columns that will be filled fill_number (float = -1): number used to replace NAs. Defaults to verbose (bool): whether the output should be verbose \"\"\" dfc = df . copy () if verbose : sum_nan_vals = df [ columns ] . isna () . sum () nans_cols = [] for c in columns : nans_cols . append (( c , df [ c ] . isna () . sum ())) dfc = df . copy () dfc [ columns ] = dfc [ columns ] . apply ( lambda x : x . astype ( float ) . fillna ( fill_number )) if verbose : print ( \"\"\" Imputed {} NaN values with {}. Affected columns (col, num_NaNs): {} \"\"\" . format ( sum_nan_vals , fill_number , nans_cols ) ) return dfc nuq_in_list_col \u00b6 nuq_in_list_col ( dfs ) Returns pd.Series with the number of unique values in the lists. Parameters: dfs ( pd . Series ) \u2013 input pandas series containing string list values Source code in inference_model/preprocessing/preprocess_data.py 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 def nuq_in_list_col ( dfs : pd . Series ): \"\"\"Returns pd.Series with the number of unique values in the lists. Args: dfs (pd.Series): input pandas series containing string list values \"\"\" dfsc = dfs . copy () dfsc . apply ( lambda string : _nunique ( list ( string . replace ( \"[\" , \"\" ) . replace ( \"]\" , \"\" ) . split ( \", \" )) ) if isinstance ( string , str ) else string ) return dfsc most_frequent_in_list_col \u00b6 most_frequent_in_list_col ( dfs ) Returns pd.Series with the most frequent values in the lists. Parameters: dfs ( pd . Series ) \u2013 input pandas series containing string list values Source code in inference_model/preprocessing/preprocess_data.py 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 def most_frequent_in_list_col ( dfs : pd . Series ): \"\"\"Returns pd.Series with the most frequent values in the lists. Args: dfs (pd.Series): input pandas series containing string list values \"\"\" dfsc = dfs . copy () dfsc . apply ( lambda string : _most_frequent ( list ( string . replace ( \"[\" , \"\" ) . replace ( \"]\" , \"\" ) . split ( \", \" )) ) if isinstance ( string , str ) else string ) return dfsc replace_rare_categories_with_str_other \u00b6 replace_rare_categories_with_str_other ( df , categorical_cols , quantile = 0.05 , surrogate_value = \"other\" , verbose = False , ) Replaces rare category value with surrogate string. Parameters: df ( pd . DataFrame ) \u2013 input dataframe categorical_cols ( List [ str ] ) \u2013 list of columns in dataframe to process. quantile ( float = 0.05 ) \u2013 determines what values are considered as rare surrogate_value ( str = \"other\" ) \u2013 string used to replace rare values Returns: Tuple [ pd . DataFrame , Dict ] \u2013 Tuple[pd.DataFrame, Dict]: New dataframe and a dict. with mapping between orig. and surrogate values. Source code in inference_model/preprocessing/preprocess_data.py 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 def replace_rare_categories_with_str_other ( df : pd . DataFrame , categorical_cols : List [ str ], quantile : float = 0.05 , surrogate_value : str = \"other\" , verbose : bool = False , ) -> Tuple [ pd . DataFrame , Dict ]: \"\"\"Replaces rare category value with surrogate string. Args: df (pd.DataFrame): input dataframe categorical_cols (List[str]): list of columns in dataframe to process. quantile (float = 0.05): determines what values are considered as rare surrogate_value (str = \"other\"): string used to replace rare values Returns: Tuple[pd.DataFrame, Dict]: New dataframe and a dict. with mapping between orig. and surrogate values. \"\"\" if surrogate_value in df [ categorical_cols ] . values : raise ValueError ( \"Surrogate string - \" + surrogate_value + \" - is already present, choose another one.\" ) dfc = df . copy () replace_dict_per_col = ( df [ categorical_cols ] . apply ( _replace_rare_dict , args = [ surrogate_value , quantile ]) . to_dict () ) dfc = dfc . replace ( replace_dict_per_col ) if verbose : sum_rare_cats = sum ( [ len ( replace_dict_per_col [ col ]) for col in replace_dict_per_col ] ) rare_cats_per_col = [ ( col , list ( replace_dict_per_col [ col ] . keys ())) for col in replace_dict_per_col ] print ( \"\"\" Replaced {} rare categories (val_count < {}) with {}. Affected columns (List[(col, rare_cats)]): {} \"\"\" . format ( sum_rare_cats , quantile , surrogate_value , rare_cats_per_col , ) ) return dfc , replace_dict_per_col preprocess \u00b6 PreprocessData \u00b6 PreprocessData ( target_col , id_cols , cat_cols = None , cont_cols = None ) Object to preprocess the dataset. Parameters: target_col ( str ) \u2013 target column name id_cols ( List [ str ] ) \u2013 id columns cat_cols ( Optional [ List [ str ]] ) \u2013 list of categorical column names cont_cols ( Optional [ List [ str ]] ) \u2013 list of continuous column names Source code in inference_model/preprocessing/preprocess.py 28 29 30 31 32 33 34 35 36 37 38 39 40 def __init__ ( self , target_col : str , id_cols : str , cat_cols : Optional [ List [ str ]] = None , cont_cols : Optional [ List [ str ]] = None , ): self . target_col = target_col self . id_cols = id_cols self . cat_cols = cat_cols self . cont_cols = cont_cols self . is_fitted = False fit_transform \u00b6 fit_transform ( df ) Fit peprocessor and transform dataset in training step. Source code in inference_model/preprocessing/preprocess.py 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 def fit_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Fit peprocessor and transform dataset in training step.\"\"\" dfc = df . drop ( columns = self . id_cols ) . copy () dfc = dfc . pipe ( drop_constant_cols ) self . cat_cols = intsec ( list ( dfc ), self . cat_cols ) if self . cat_cols is None : self . cat_cols = self . _infere_cat_cols ( dfc ) dfc_cat_cols = drop_high_nan_cols ( dfc [ self . cat_cols ]) dfc = dfc . drop ( columns = self . cat_cols ) dfc = pd . concat ([ dfc , dfc_cat_cols ], axis = 1 ) self . cat_cols = dfc_cat_cols . columns . values . tolist () if self . cont_cols is None : self . init_cont_cols = self . _infere_cont_cols ( dfc , self . cat_cols ) dfc_init_cont_cols = drop_high_nan_cols ( dfc [ self . init_cont_cols ]) dfc = dfc . drop ( columns = self . init_cont_cols ) dfc = pd . concat ([ dfc , dfc_init_cont_cols ], axis = 1 ) self . init_cont_cols = dfc_init_cont_cols . columns . values . tolist () dfc = nan_with_number_imputer ( dfc , self . init_cont_cols , - 9999.0 ) ( dfc , self . final_cont_cols , ) = self . _drop_highly_corr_cols_and_get_final_cont_cols ( dfc , self . init_cont_cols ) else : self . final_cont_cols = list ( set ( self . cont_cols ) . intersection ( set ( dfc . columns . values )) ) dfc = nan_with_number_imputer ( dfc , self . final_cont_cols , - 9999.0 ) dfc = nan_with_unknown_imputer ( dfc , self . cat_cols ) dfc = dfc [ self . cat_cols + self . final_cont_cols + [ self . target_col ]] self . label_encoder = LabelEncoder ( self . cat_cols ) dfc_le = self . label_encoder . fit_transform ( dfc ) dfc_le = self . _change_int_float_types ( dfc_le ) dfc_le [ self . id_cols ] = df [ self . id_cols ] self . is_fitted = True return dfc_le transform \u00b6 transform ( df ) Transform dataset in inference step. Source code in inference_model/preprocessing/preprocess.py 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 def transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Transform dataset in inference step.\"\"\" if not self . is_fitted : raise NotFittedError ( \"\"\"This instance of 'PreprocessData' has not been fitted yet. Please, run 'fit' first\"\"\" ) dfc = df . drop ( columns = self . id_cols ) . copy () # added as mlflow inference is receiving all the data as objects dfc [ self . cat_cols ] = dfc [ self . cat_cols ] . astype ( str ) dfc [ self . final_cont_cols ] = dfc [ self . final_cont_cols ] . astype ( float ) try : dfc = dfc [ self . cat_cols + self . final_cont_cols + [ self . target_col ]] except KeyError : dfc = dfc [ self . cat_cols + self . final_cont_cols ] # dfc = dfc.replace(self.replace_rare_categories_dict) dfc = nan_with_unknown_imputer ( dfc , self . cat_cols ) dfc = nan_with_number_imputer ( dfc , list ( set ( self . final_cont_cols )), - 9999.0 , # noqa ) dfc_le = self . label_encoder . transform ( dfc ) dfc_le = self . _change_int_float_types ( dfc_le ) dfc_le [ self . id_cols ] = df [ self . id_cols ] return dfc_le fit \u00b6 fit ( df ) Just to keep familiar naming convention with sklearn. Source code in inference_model/preprocessing/preprocess.py 134 135 136 def fit ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Just to keep familiar naming convention with sklearn.\"\"\" return self . fit_transform ( df ) scaler \u00b6 scaler_mapper \u00b6 scaler_mapper ( cont_cols , cat_cols , id_cols , scaler_mapper_def = None ) Function that maps scaler functions to appropriate columns. By default does not assign any scaler to continuous, categorical or identifier columns. The scalers must be set in scaler_mapper_def. Use sklearn scalers. Only columns defined in mapper object will be present in the transformed dataset. Parameters: cont_cols ( list ) \u2013 list of continuous feature columns in the dataset cat_cols ( list ) \u2013 list of categorical feature columns in the dataset id_cols ( list ) \u2013 identifier columns scaler_mapper_def ( dict ) \u2013 optional dictionary that contains keys ['cont_cols', 'cat_cols', 'id_cols'] with their corresponding scalers (defined by names, not instantiated) from sklearn library Returns: scaler ( DataFrameMapper ) \u2013 scaler object mapping sklearn scalers to columns in pandas dataframe Source code in inference_model/preprocessing/scaler.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 def scaler_mapper ( cont_cols : List [ str ], cat_cols : List [ str ], id_cols : List [ str ], scaler_mapper_def : Optional [ dict ] = None , ) -> DataFrameMapper : \"\"\"Function that maps scaler functions to appropriate columns. By default does not assign any scaler to continuous, categorical or identifier columns. The scalers must be set in scaler_mapper_def. Use sklearn scalers. Only columns defined in mapper object will be present in the transformed dataset. Args: cont_cols (list): list of continuous feature columns in the dataset cat_cols (list): list of categorical feature columns in the dataset id_cols (list): identifier columns scaler_mapper_def (dict): optional dictionary that contains keys ['cont_cols', 'cat_cols', 'id_cols'] with their corresponding scalers (defined by names, not instantiated) from sklearn library Returns: scaler (DataFrameMapper): scaler object mapping sklearn scalers to columns in pandas dataframe \"\"\" if scaler_mapper_def : cont_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cont_cols )), classes = [ scaler_mapper_def [ \"cont_cols\" ]], ) cat_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cat_cols )), classes = [ scaler_mapper_def [ \"cat_cols\" ]], ) id_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], id_cols )), classes = [ scaler_mapper_def [ \"id_cols\" ]], ) else : cont_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cont_cols )), classes = [ None ] ) cat_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cat_cols )), classes = [ None ] ) id_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], id_cols )), classes = [ None ] ) scaler = DataFrameMapper ( cont_cols_def + cat_cols_def + id_cols_def , df_out = True ) return scaler","title":"preprocessing"},{"location":"inference_model/preprocessing.html#preprocess-data","text":"","title":"Preprocess data"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.label_encoder","text":"","title":"label_encoder"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.preprocess_data","text":"","title":"preprocess_data"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.preprocess","text":"","title":"preprocess"},{"location":"inference_model/preprocessing.html#inference_model.preprocessing.scaler","text":"","title":"scaler"},{"location":"inference_model/training.html","text":"Training \u00b6 metrics \u00b6 smape \u00b6 smape ( actual , predicted ) Symmetric Mean Absolute Percentage Error https://vedexcel.com/how-to-calculate-smape-in-python/ Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Returns: smape ( float ) \u2013 symmetric mean absolute percentage error Source code in inference_model/training/metrics.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 def smape ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Symmetric Mean Absolute Percentage Error https://vedexcel.com/how-to-calculate-smape-in-python/ Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Returns: smape (float): symmetric mean absolute percentage error \"\"\" return ( 100 / len ( actual ) * np . sum ( 2 * np . abs ( predicted - actual ) / ( np . abs ( actual ) + np . abs ( predicted ))) ) ndcg \u00b6 ndcg ( actual , predicted ) Normalized Discounted Cumulative Gain Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Return ndcg (float): normalized discounted cumulative gain Source code in inference_model/training/metrics.py 32 33 34 35 36 37 38 39 40 41 42 43 44 def ndcg ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Normalized Discounted Cumulative Gain Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Return: ndcg (float): normalized discounted cumulative gain \"\"\" return ndcg_score ( [ rankdata ( actual , method = \"ordinal\" )], [ rankdata ( predicted , method = \"ordinal\" )], ) rmse \u00b6 rmse ( actual , predicted ) Root Mean Squared Error Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Returns: rmse ( float ) \u2013 root mean square error Source code in inference_model/training/metrics.py 47 48 49 50 51 52 53 54 55 56 def rmse ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Root Mean Squared Error Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Returns: rmse (float): root mean square error \"\"\" return mean_squared_error ( actual , predicted , squared = False ) nacil \u00b6 nacil ( actual , high_quantile_predicted , low_quantile_predicted ) Normalized Average Confidence Interval Length for Quantile Regression. The value is normalized to actual(expected) value. Parameters: actual ( np . ndarray ) \u2013 actual values high_quantile_predicted ( np . ndarray ) \u2013 high quantile predicted values low_quantile_predicted ( np . ndarray ) \u2013 low quantile predicted values Returns: nacil ( float ) \u2013 normalized average confidence interval length Source code in inference_model/training/metrics.py 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def nacil ( actual : np . ndarray , high_quantile_predicted : np . ndarray , low_quantile_predicted : np . ndarray , ): \"\"\"Normalized Average Confidence Interval Length for Quantile Regression. The value is normalized to actual(expected) value. Args: actual (np.ndarray): actual values high_quantile_predicted (np.ndarray): high quantile predicted values low_quantile_predicted (np.ndarray): low quantile predicted values Returns: nacil (float): normalized average confidence interval length \"\"\" return np . mean (( high_quantile_predicted - low_quantile_predicted ) / actual ) aiqc \u00b6 aiqc ( actual , high_quantile , low_quantile ) Average InterQuantile Coverage for Quantile Regression. Check if the interquantile coverage is close to the coverage of in the test dataset. Parameters: actual ( np . ndarray ) \u2013 actual values high_quantile_predicted ( np . ndarray ) \u2013 high quantile predicted values low_quantile_predicted ( np . ndarray ) \u2013 low quantile predicted values Returns: aiqc ( float ) \u2013 average interquantile coverage Source code in inference_model/training/metrics.py 77 78 79 80 81 82 83 84 85 86 87 88 def aiqc ( actual : np . ndarray , high_quantile : np . ndarray , low_quantile : np . ndarray ): \"\"\"Average InterQuantile Coverage for Quantile Regression. Check if the interquantile coverage is close to the coverage of in the test dataset. Args: actual (np.ndarray): actual values high_quantile_predicted (np.ndarray): high quantile predicted values low_quantile_predicted (np.ndarray): low quantile predicted values Returns: aiqc (float): average interquantile coverage \"\"\" return np . sum (( actual < high_quantile ) & ( actual > low_quantile )) / len ( actual ) optuna_optimizer \u00b6 LGBOptunaOptimizer \u00b6 LGBOptunaOptimizer ( objective , n_class = None ) Bases: BaseOptimizer Fallback/backup Optuna optimizer. Development is focused on Raytune. Kepping this code as backup. Parameters: objective ( str ) \u2013 objective of the model n_class ( int ) \u2013 number of classes in the dataset Source code in inference_model/training/optuna_optimizer.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def __init__ ( self , objective : Literal [ \"binary\" , \"multiclass\" , \"regression\" ], n_class : Optional [ int ] = None , ): \"\"\"Fallback/backup Optuna optimizer. Development is focused on Raytune. Kepping this code as backup. Args: objective (str): objective of the model n_class (int): number of classes in the dataset \"\"\" # Optuna does not support original lighgbm implemenattion and with a workaroud it # is possible to get the binarry classfier with focal_loss(any custom loss) # running but the multiclass requires changes to Optuna code, eg. this post: # https://lightrun.com/answers/optuna-optuna-error-when-using-custom-metrics-in-optunaintegrationlightgbm # noqa # loss = None super ( LGBOptunaOptimizer , self ) . __init__ ( objective , n_class ) self . params = self . base_params optimize \u00b6 optimize ( dtrain , deval ) Optimize LGBM model on provided datasets. Parameters: dtrain ( lgbDataset ) \u2013 training lgb dataset deval ( lgbDataset ) \u2013 evaluation lgb dataset Source code in inference_model/training/optuna_optimizer.py 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 def optimize ( self , dtrain : lgbDataset , deval : lgbDataset ): \"\"\"Optimize LGBM model on provided datasets. Args: dtrain (lgbDataset): training lgb dataset deval (lgbDataset): evaluation lgb dataset \"\"\" dtrain_copy = deepcopy ( dtrain ) deval_copy = deepcopy ( deval ) if deval is not None else None study = create_study ( study_name = \"LightGBMTuner\" ) tuner = LightGBMTuner ( params = self . params , train_set = dtrain_copy , valid_sets = deval_copy , num_boost_round = 1000 , callbacks = [ lgb . early_stopping ( stopping_rounds = 50 )], feval = self . feval , study = study , # fobj=self.fobj, ) tuner . run () self . study = study self . best = tuner . best_params # since n_estimators is not among the params that Optuna optimizes we # need to add it manually. We add a high value since it will be used # with early_stopping_rounds self . best [ \"n_estimators\" ] = 1000 # type: ignore trainer \u00b6 Trainer \u00b6 Trainer ( cat_cols , target_col , id_cols , objective , optimizer = None , n_class = None , preprocessors = None , ) Bases: BaseTrainer Objects that governs training and parameter optimization of the lgbm model. Parameters: cat_cols ( list ) \u2013 list of categorical feature column names target_col ( str ) \u2013 column name that represents target id_cols ( list ) \u2013 identification column names objective ( str ) \u2013 type of the task/objective optimizer ( BaseOptimizer ) \u2013 parameter optimizer object Source code in inference_model/training/trainer.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 def __init__ ( self , cat_cols : List [ str ], target_col : str , id_cols : List [ str ], objective : Literal [ \"binary\" ], optimizer : Union [ Any , BaseOptimizer , None ] = None , n_class : Optional [ int ] = None , preprocessors : Optional [ List [ Union [ Any , PreprocessData ]]] = None , ): \"\"\"Objects that governs training and parameter optimization of the lgbm model. Args: cat_cols (list): list of categorical feature column names target_col (str): column name that represents target id_cols (list): identification column names objective (str): type of the task/objective optimizer (BaseOptimizer): parameter optimizer object \"\"\" super ( Trainer , self ) . __init__ ( cat_cols = cat_cols , target_col = target_col , id_cols = id_cols , objective = objective , n_class = n_class , preprocessors = preprocessors , ) if optimizer is not None : if not hasattr ( optimizer , \"optimize\" ): raise AttributeError ( \" {} optimizer must have {} method\" . format ( optimizer , \"optimize\" ) ) self . optimizer = optimizer train \u00b6 train ( df_train , params = None , df_valid = None ) Train the model with the parameters. Parameters: df_train ( pd . DataFrame ) \u2013 training dataset params ( dict ) \u2013 model paramaters df_valid ( pd . DataFrame ) \u2013 optional validation dataset Returns: model ( lgb . basic . Booster ) \u2013 trained model Source code in inference_model/training/trainer.py 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 def train ( self , df_train : pd . DataFrame , params : Optional [ Dict ] = None , df_valid : Optional [ pd . DataFrame ] = None , ): \"\"\"Train the model with the parameters. Args: df_train (pd.DataFrame): training dataset params (dict): model paramaters df_valid (pd.DataFrame): optional validation dataset Returns: model (lgb.basic.Booster): trained model \"\"\" if self . preprocessors : for prep in self . preprocessors : df_train_prep = prep . transform ( df_train . drop ( columns = [ self . target_col ])) df_valid_prep = ( prep . transform ( df_valid . drop ( columns = [ self . target_col ])) if df_valid is not None else None ) if self . objective in [ \"binary\" ]: df_train_prep [ self . target_col ] = df_train [ self . target_col ] . astype ( int ) if df_valid is not None : df_valid_prep [ self . target_col ] = df_valid [ self . target_col ] . astype ( int ) else : raise NotImplementedError else : df_train_prep = df_train . copy () df_valid_prep = df_valid . copy () if df_valid is not None else None cat_cols = self . cat_cols if params : config = params elif ( params is None ) and ( self . optimizer ): config = self . optimizer . best if hasattr ( self . optimizer , \"best_to_drop\" ): df_train_prep . drop ( columns = self . optimizer . best_to_drop , # type: ignore inplace = True , ) if df_valid is not None : df_valid_prep . drop ( columns = self . optimizer . best_to_drop , # type: ignore inplace = True , ) cat_cols = intsec ( self . cat_cols , df_train_prep . columns . values ) elif ( params is None ) and ( self . optimizer is None ): config = self . base_params lgb_train , lgb_valid = to_lgbdataset ( train = df_train_prep , cat_cols = cat_cols , target_col = self . target_col , id_cols = self . id_cols , valid = df_valid_prep , ) self . model = lgb_train_function ( config = config , lgbtrain = lgb_train , lgbeval = lgb_valid , feval = self . feval , ) fit \u00b6 fit ( df_train , df_valid , df_test , random_state = 1 ) Train the model and optimize the parameters. Parameters: df_train ( pd . DataFrame ) \u2013 training dataset df_valid ( pd . DataFrame ) \u2013 validation dataset df_test ( pd . DataFrame ) \u2013 testing dataset Returns: model ( lgb . basic . Booster ) \u2013 trained mdoel Source code in inference_model/training/trainer.py 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 def fit ( self , df_train : pd . DataFrame , df_valid : pd . DataFrame , df_test : pd . DataFrame , random_state : int = 1 , ) -> pd . DataFrame : \"\"\"Train the model and optimize the parameters. Args: df_train (pd.DataFrame): training dataset df_valid (pd.DataFrame): validation dataset df_test (pd.DataFrame): testing dataset Returns: model (lgb.basic.Booster): trained mdoel \"\"\" if self . preprocessors : # this should be # df_train_prep = prep.transform(df_train_prep.drop(columns=[self.target_col])) for prep in self . preprocessors : df_train_prep = prep . transform ( df_train . drop ( columns = [ self . target_col ])) df_valid_prep = prep . transform ( df_valid . drop ( columns = [ self . target_col ])) if self . objective in [ \"binary\" ]: df_train_prep [ self . target_col ] = df_train [ self . target_col ] . astype ( int ) df_valid_prep [ self . target_col ] = df_valid [ self . target_col ] . astype ( int ) else : raise NotImplementedError else : df_train_prep = df_train . copy () df_valid_prep = df_valid . copy () lgb_train , lgb_valid = to_lgbdataset ( train = df_train_prep , cat_cols = self . cat_cols , target_col = self . target_col , id_cols = self . id_cols , valid = df_valid_prep , ) self . optimizer . optimize ( dtrain = lgb_train , deval = lgb_valid ) df_train_valid = pd . concat ([ df_train , df_valid ], ignore_index = True ) df_train_valid = df_train_valid . reset_index ( drop = True ) self . train ( df_train = df_train_valid ) metrics_dict = self . compute_metrics ( df_test ) return metrics_dict utils \u00b6 get_or_create_experiment \u00b6 get_or_create_experiment ( experiment_name ) Retrieve the ID of an existing MLflow experiment or create a new one if it doesn't exist. This function checks if an experiment with the given name exists within MLflow. If it does, the function returns its ID. If not, it creates a new experiment with the provided name and returns its ID. experiment_name (str): Name of the MLflow experiment. str: ID of the existing or newly created MLflow experiment. Source code in inference_model/training/utils.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 def get_or_create_experiment ( experiment_name ): \"\"\" Retrieve the ID of an existing MLflow experiment or create a new one if it doesn't exist. This function checks if an experiment with the given name exists within MLflow. If it does, the function returns its ID. If not, it creates a new experiment with the provided name and returns its ID. Parameters: - experiment_name (str): Name of the MLflow experiment. Returns: - str: ID of the existing or newly created MLflow experiment. \"\"\" if experiment := mlflow . get_experiment_by_name ( experiment_name ): return experiment . experiment_id else : return mlflow . create_experiment ( experiment_name ) to_lgbdataset \u00b6 to_lgbdataset ( train , cat_cols , target_col , id_cols = [], valid = None ) Transform pandas dataframe to lgbm dataset, or datasets(eval). Parameters: train ( pd . DataFrame ) \u2013 training dataset cat_cols ( list ) \u2013 list of categorical columns target_col ( str ) \u2013 target column in the dataset id_cols ( list ) \u2013 list of identifier columns valid ( pd . DataFrame ) \u2013 validation dataset Returns: lgb_train ( lgbDataset ) \u2013 lgbm training dataset lgb_valid ( lgbDataset ) \u2013 lgbm valid dataset Source code in inference_model/training/utils.py 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 def to_lgbdataset ( train : pd . DataFrame , cat_cols : List [ str ], target_col : str , id_cols : List [ str ] = [], valid : Optional [ pd . DataFrame ] = None , ) -> Tuple [ lgbDataset , Optional [ lgbDataset ]]: \"\"\"Transform pandas dataframe to lgbm dataset, or datasets(eval). Args: train (pd.DataFrame): training dataset cat_cols (list): list of categorical columns target_col (str): target column in the dataset id_cols (list): list of identifier columns valid (pd.DataFrame): validation dataset Returns: lgb_train (lgbDataset): lgbm training dataset lgb_valid (lgbDataset): lgbm valid dataset \"\"\" lgb_train = lgbDataset ( train . drop ( columns = [ target_col ] + id_cols ), train [ target_col ], categorical_feature = cat_cols , free_raw_data = False , ) if valid is not None : lgb_valid = lgbDataset ( valid . drop ( columns = [ target_col ] + id_cols ), valid [ target_col ], reference = lgb_train , free_raw_data = False , ) else : lgb_valid = None return lgb_train , lgb_valid predict_proba_lgbm_from_raw \u00b6 predict_proba_lgbm_from_raw ( preds_raw , task , binary2d = False ) Apply softmax to array of arrays that is an output of lightgbm.predct(). This replaces predict_proba(). Parameters: predicted_raw ( ndarray ) \u2013 1D numpy array of arrays task ( str ) \u2013 type of task/objective binary2d ( boolean ) \u2013 wether the output of binary classification should be 1 or 2d vector Returns: predicted_probs ( ndarray ) \u2013 array with predicted probabilities Source code in inference_model/training/utils.py 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 def predict_proba_lgbm_from_raw ( preds_raw : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ], binary2d : Optional [ bool ] = False , ) -> np . ndarray : \"\"\"Apply softmax to array of arrays that is an output of lightgbm.predct(). This replaces predict_proba(). Args: predicted_raw (ndarray): 1D numpy array of arrays task (str): type of task/objective binary2d (boolean): wether the output of binary classification should be 1 or 2d vector Returns: predicted_probs (ndarray): array with predicted probabilities \"\"\" if task == \"binary\" : predicted_probs = _sigmoid ( preds_raw ) if binary2d : predicted_probs = np . apply_along_axis ( _binary_margin_to_prob , 1 , np . vstack ( tuple ( predicted_probs )) ) elif task == \"multiclass\" : predicted_probs = np . apply_along_axis ( _softmax , 1 , np . stack ( tuple ( preds_raw ))) return predicted_probs predict_cls_lgbm_from_raw \u00b6 predict_cls_lgbm_from_raw ( preds_raw , task ) Helper function to convert raw margin predictions through a sigmoid to represent a probability. Parameters: preds_raw ( ndarray ) \u2013 predictions lgbDataset ( lightgbm . Dataset ) \u2013 dataset, containing labels, used for prediction Returns: y_true , preds \u2013 tuple containg labels and predictions for further evaluation Source code in inference_model/training/utils.py 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 def predict_cls_lgbm_from_raw ( preds_raw : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ] ) -> np . ndarray : \"\"\"Helper function to convert raw margin predictions through a sigmoid to represent a probability. Args: preds_raw (ndarray): predictions lgbDataset (lightgbm.Dataset): dataset, containing labels, used for prediction Returns: (y_true, preds): tuple containg labels and predictions for further evaluation \"\"\" predicted_probs = predict_proba_lgbm_from_raw ( preds_raw = preds_raw , task = task ) if task == \"binary\" : pred_cls = np . array ([ int ( p > 0.5 ) for p in predicted_probs ]) elif task == \"multiclass\" : pred_cls = predicted_probs . argmax ( axis = 1 ) return pred_cls get_feature_importance \u00b6 get_feature_importance ( model ) Extract model feature importances and return sorted dataframe. Parameters: model ( lgb . basic . Booster ) \u2013 LightGBM model Returns: feature_imp ( pd . DataFrame ) \u2013 sorted dataframe with features and their importances Source code in inference_model/training/utils.py 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 def get_feature_importance ( model : lgb . basic . Booster ) -> pd . DataFrame : \"\"\"Extract model feature importances and return sorted dataframe. Args: model (lgb.basic.Booster): LightGBM model Returns: feature_imp (pd.DataFrame): sorted dataframe with features and their importances \"\"\" feature_imp = pd . DataFrame ( { \"value\" : model . feature_importance (), \"feature\" : model . feature_name ()} ) feature_imp . sort_values ( by = \"value\" , inplace = True ) feature_imp . reset_index ( drop = True , inplace = True ) return feature_imp flatten_dict \u00b6 flatten_dict ( d , parent_key = '' , sep = '_' ) fastest according to https://www.freecodecamp.org/news/how-to-flatten-a-dictionary-in-python-in-4-different-ways/ Source code in inference_model/training/utils.py 184 185 186 187 188 189 190 191 192 193 194 195 196 197 def flatten_dict ( d : MutableMapping , parent_key : str = \"\" , sep : str = \"_\" ) -> MutableMapping : \"\"\" fastest according to https://www.freecodecamp.org/news/how-to-flatten-a-dictionary-in-python-in-4-different-ways/ \"\"\" items : List [ Any ] = [] for k , v in d . items (): new_key = parent_key + sep + k if parent_key else k if isinstance ( v , MutableMapping ): items . extend ( flatten_dict ( v , new_key , sep = sep ) . items ()) else : items . append (( new_key , v )) return dict ( items )","title":"training"},{"location":"inference_model/training.html#training","text":"","title":"Training"},{"location":"inference_model/training.html#inference_model.training.metrics","text":"","title":"metrics"},{"location":"inference_model/training.html#inference_model.training.optuna_optimizer","text":"","title":"optuna_optimizer"},{"location":"inference_model/training.html#inference_model.training.trainer","text":"","title":"trainer"},{"location":"inference_model/training.html#inference_model.training.utils","text":"","title":"utils"},{"location":"inference_model/utils.html","text":"Utils \u00b6 utils \u00b6 intsec \u00b6 intsec ( list1 , list2 ) Simple intesection of two lists. Parameters: list1 ( list ) \u2013 list1 list2 ( list ) \u2013 list2 Returns: list ( list ) \u2013 intersection of lists Source code in inference_model/utils.py 7 8 9 10 11 12 13 14 15 def intsec ( list1 : list , list2 : list ) -> list : \"\"\"Simple intesection of two lists. Args: list1 (list): list1 list2 (list): list2 Returns: list (list): intersection of lists \"\"\" return list ( set . intersection ( set ( list1 ), set ( list2 ))) dill_load \u00b6 dill_load ( file_loc ) Helper function to open/close dill file, otherwise the python outputs warning that the file remains opened Parameters: file_loc ( str ) \u2013 location of the file Returns: content ( dict ) \u2013 content of dill file, usually dictionary Source code in inference_model/utils.py 18 19 20 21 22 23 24 25 26 27 28 29 def dill_load ( file_loc : Union [ str , Path ]) -> Any : \"\"\"Helper function to open/close dill file, otherwise the python outputs warning that the file remains opened Args: file_loc (str): location of the file Returns: content (dict): content of dill file, usually dictionary \"\"\" with open ( file_loc , \"rb\" ) as f : content = dill . load ( f ) return content dill_dump \u00b6 dill_dump ( file_loc , content ) Helper function to open/close dill file and dump content into it, otherwise the python outputs warning that the file remains opened Parameters: file_loc ( str ) \u2013 location of the file content ( object ) \u2013 data that will be saved to dill, usually dictionary Source code in inference_model/utils.py 32 33 34 35 36 37 38 39 40 41 def dill_dump ( file_loc : Union [ str , Path ], content : object ): \"\"\"Helper function to open/close dill file and dump content into it, otherwise the python outputs warning that the file remains opened Args: file_loc (str): location of the file content (object): data that will be saved to dill, usually dictionary \"\"\" with open ( file_loc , \"wb\" ) as f : dill . dump ( content , f )","title":"utils"},{"location":"inference_model/utils.html#utils","text":"","title":"Utils"},{"location":"inference_model/utils.html#inference_model.utils","text":"","title":"utils"},{"location":"notebooks/01_feature_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Feature analysis \u00b6 Purpose : analyse dataset features without inclusion of target value if it looks healthy duplicate values NA values zero/0 values if the values are \"too constant\", ie. most [robably bad features for classifcation/regression small std values entropy is too high/too low(not sure right now?) - \"Entropy is a measure of disorder or uncertainty\" number of unique values in categorical features is too high - most probably they are unable to identify any group of targets drop highly correlated values to improve performance of the model training to decrease dataset size for further exploration Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing , zero , nunique , init_check , std , entropy , nunique , ) pd . set_option ( \"display.max_columns\" , 100 ) pd . set_option ( \"display.max_rows\" , 300 ) from inference_model.eda.features.plotting import cross_correlation , distributions import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing, zero, nunique, init_check, std, entropy, nunique, ) pd.set_option(\"display.max_columns\", 100) pd.set_option(\"display.max_rows\", 300) from inference_model.eda.features.plotting import cross_correlation, distributions import ast Dataset \u00b6 In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None Features \u00b6 Analysis \u00b6 In [4]: Copied! duplicated_ids , cont_cols_desc , cat_cols_desc = init_check ( df = df_pd , identifier = id_cols , cat_cols = cat_cols , cont_cols = cont_cols , verbose = True , ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() duplicated_ids, cont_cols_desc, cat_cols_desc = init_check( df=df_pd, identifier=id_cols, cat_cols=cat_cols, cont_cols=cont_cols, verbose=True, ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() [CHECK] Number of duplicated ids: 1034 [CHECK] Numerical columns count mean std min 25% 50% \\ maf 1035.0 11.00 3.40 0.03 9.77 10.19 vehicle_speed 1035.0 0.95 3.70 0.00 0.00 0.00 engine_load 1035.0 46.29 21.59 30.59 35.69 37.25 barometric_pressure 1035.0 101.00 0.00 101.00 101.00 101.00 intake_air_temp 1035.0 28.56 18.16 -6.00 20.00 26.00 throttle_position 1035.0 83.21 0.15 83.14 83.14 83.14 engine_coolant_temp 1035.0 76.66 21.89 17.00 74.50 88.00 engine_speed 1035.0 929.39 270.78 3.20 893.00 896.50 fuel_rg_pressure 1035.0 35967.15 14377.15 110.00 31220.00 34050.00 control_voltage 1035.0 14.32 2.14 0.05 14.76 14.78 75% max maf 11.99 31.83 vehicle_speed 0.00 20.00 engine_load 38.82 100.00 barometric_pressure 101.00 101.00 intake_air_temp 32.00 145.00 throttle_position 83.14 83.53 engine_coolant_temp 89.00 91.00 engine_speed 899.50 2282.50 fuel_rg_pressure 35390.00 108770.00 control_voltage 14.80 14.84 In [5]: Copied! missing_val_frac , fig = missing ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) missing_val_frac . head () missing_val_frac, fig = missing( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) missing_val_frac.head() Out[5]: engine_load 0.0 engine_coolant_temp 0.0 engine_speed 0.0 vehicle_speed 0.0 intake_air_temp 0.0 dtype: float64 In [6]: Copied! zero_val_frac , fig = zero ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) zero_val_frac . head () zero_val_frac, fig = zero( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) zero_val_frac.head() Out[6]: vehicle_speed 0.927536 class 0.902415 engine_load 0.000000 engine_coolant_temp 0.000000 engine_speed 0.000000 dtype: float64 In [7]: Copied! data_nunique , fig = nunique ( df = df_pd . drop ( columns = id_cols ), scale = \"log\" , plot = True , ) data_nunique . head () data_nunique, fig = nunique( df=df_pd.drop(columns=id_cols), scale=\"log\", plot=True, ) data_nunique.head() Out[7]: barometric_pressure 1 throttle_position 2 class 2 vehicle_speed 21 control_voltage 48 dtype: int64 In [8]: Copied! data_std , fig = std ( df = df_pd [ cont_cols ], scale = \"log\" , plot = True , ) data_std . head () data_std, fig = std( df=df_pd[cont_cols], scale=\"log\", plot=True, ) data_std.head() Out[8]: fuel_rg_pressure 14377.152211 engine_speed 270.781548 engine_coolant_temp 21.888726 engine_load 21.587427 intake_air_temp 18.163539 dtype: float64 In [9]: Copied! col_entropies , fig = entropy ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) col_entropies . head () col_entropies, fig = entropy( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) col_entropies.head() Out[9]: maf 4.800777 engine_speed 3.981783 fuel_rg_pressure 3.797527 intake_air_temp 3.612889 engine_load 3.253640 dtype: float64 Plotting \u00b6 In [10]: Copied! fig = cross_correlation ( df = df_pd [ cont_cols ], n = 10 , verbose = True ) fig = cross_correlation(df=df_pd[cont_cols], n=10, verbose=True) Top 10 absolute correlations vehicle_speed fuel_rg_pressure 0.648543 maf 0.544904 engine_load engine_coolant_temp 0.531108 engine_speed vehicle_speed 0.525832 engine_coolant_temp throttle_position 0.491290 engine_speed maf 0.398281 maf fuel_rg_pressure 0.331396 engine_speed fuel_rg_pressure 0.329811 engine_coolant_temp intake_air_temp 0.305559 engine_load intake_air_temp 0.207530 dtype: float64 In [11]: Copied! fig = distributions ( df = df_pd [ cont_cols ], low_per_cut = 0 , high_per_cut = 1 , type = \"box\" , ) fig = distributions( df=df_pd[cont_cols], low_per_cut=0, high_per_cut=1, type=\"box\", ) /home/jovyan/success6g-edge/inference_model/eda/features/plotting.py:98: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding. ax[i].set_xlim(data[col].quantile(low_per_cut), data[col].quantile(high_per_cut))","title":"01_feature_analysis"},{"location":"notebooks/01_feature_analysis.html#feature-analysis","text":"Purpose : analyse dataset features without inclusion of target value if it looks healthy duplicate values NA values zero/0 values if the values are \"too constant\", ie. most [robably bad features for classifcation/regression small std values entropy is too high/too low(not sure right now?) - \"Entropy is a measure of disorder or uncertainty\" number of unique values in categorical features is too high - most probably they are unable to identify any group of targets drop highly correlated values to improve performance of the model training to decrease dataset size for further exploration","title":"Feature analysis"},{"location":"notebooks/01_feature_analysis.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing , zero , nunique , init_check , std , entropy , nunique , ) pd . set_option ( \"display.max_columns\" , 100 ) pd . set_option ( \"display.max_rows\" , 300 ) from inference_model.eda.features.plotting import cross_correlation , distributions import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd # import plotly.graph_objects as go from inference_model.eda.features.analysis import ( missing, zero, nunique, init_check, std, entropy, nunique, ) pd.set_option(\"display.max_columns\", 100) pd.set_option(\"display.max_rows\", 300) from inference_model.eda.features.plotting import cross_correlation, distributions import ast","title":"Imports"},{"location":"notebooks/01_feature_analysis.html#dataset","text":"In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None","title":"Dataset"},{"location":"notebooks/01_feature_analysis.html#features","text":"","title":"Features"},{"location":"notebooks/01_feature_analysis.html#analysis","text":"In [4]: Copied! duplicated_ids , cont_cols_desc , cat_cols_desc = init_check ( df = df_pd , identifier = id_cols , cat_cols = cat_cols , cont_cols = cont_cols , verbose = True , ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() duplicated_ids, cont_cols_desc, cat_cols_desc = init_check( df=df_pd, identifier=id_cols, cat_cols=cat_cols, cont_cols=cont_cols, verbose=True, ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() [CHECK] Number of duplicated ids: 1034 [CHECK] Numerical columns count mean std min 25% 50% \\ maf 1035.0 11.00 3.40 0.03 9.77 10.19 vehicle_speed 1035.0 0.95 3.70 0.00 0.00 0.00 engine_load 1035.0 46.29 21.59 30.59 35.69 37.25 barometric_pressure 1035.0 101.00 0.00 101.00 101.00 101.00 intake_air_temp 1035.0 28.56 18.16 -6.00 20.00 26.00 throttle_position 1035.0 83.21 0.15 83.14 83.14 83.14 engine_coolant_temp 1035.0 76.66 21.89 17.00 74.50 88.00 engine_speed 1035.0 929.39 270.78 3.20 893.00 896.50 fuel_rg_pressure 1035.0 35967.15 14377.15 110.00 31220.00 34050.00 control_voltage 1035.0 14.32 2.14 0.05 14.76 14.78 75% max maf 11.99 31.83 vehicle_speed 0.00 20.00 engine_load 38.82 100.00 barometric_pressure 101.00 101.00 intake_air_temp 32.00 145.00 throttle_position 83.14 83.53 engine_coolant_temp 89.00 91.00 engine_speed 899.50 2282.50 fuel_rg_pressure 35390.00 108770.00 control_voltage 14.80 14.84 In [5]: Copied! missing_val_frac , fig = missing ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) missing_val_frac . head () missing_val_frac, fig = missing( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) missing_val_frac.head() Out[5]: engine_load 0.0 engine_coolant_temp 0.0 engine_speed 0.0 vehicle_speed 0.0 intake_air_temp 0.0 dtype: float64 In [6]: Copied! zero_val_frac , fig = zero ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) zero_val_frac . head () zero_val_frac, fig = zero( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) zero_val_frac.head() Out[6]: vehicle_speed 0.927536 class 0.902415 engine_load 0.000000 engine_coolant_temp 0.000000 engine_speed 0.000000 dtype: float64 In [7]: Copied! data_nunique , fig = nunique ( df = df_pd . drop ( columns = id_cols ), scale = \"log\" , plot = True , ) data_nunique . head () data_nunique, fig = nunique( df=df_pd.drop(columns=id_cols), scale=\"log\", plot=True, ) data_nunique.head() Out[7]: barometric_pressure 1 throttle_position 2 class 2 vehicle_speed 21 control_voltage 48 dtype: int64 In [8]: Copied! data_std , fig = std ( df = df_pd [ cont_cols ], scale = \"log\" , plot = True , ) data_std . head () data_std, fig = std( df=df_pd[cont_cols], scale=\"log\", plot=True, ) data_std.head() Out[8]: fuel_rg_pressure 14377.152211 engine_speed 270.781548 engine_coolant_temp 21.888726 engine_load 21.587427 intake_air_temp 18.163539 dtype: float64 In [9]: Copied! col_entropies , fig = entropy ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) col_entropies . head () col_entropies, fig = entropy( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) col_entropies.head() Out[9]: maf 4.800777 engine_speed 3.981783 fuel_rg_pressure 3.797527 intake_air_temp 3.612889 engine_load 3.253640 dtype: float64","title":"Analysis"},{"location":"notebooks/01_feature_analysis.html#plotting","text":"In [10]: Copied! fig = cross_correlation ( df = df_pd [ cont_cols ], n = 10 , verbose = True ) fig = cross_correlation(df=df_pd[cont_cols], n=10, verbose=True) Top 10 absolute correlations vehicle_speed fuel_rg_pressure 0.648543 maf 0.544904 engine_load engine_coolant_temp 0.531108 engine_speed vehicle_speed 0.525832 engine_coolant_temp throttle_position 0.491290 engine_speed maf 0.398281 maf fuel_rg_pressure 0.331396 engine_speed fuel_rg_pressure 0.329811 engine_coolant_temp intake_air_temp 0.305559 engine_load intake_air_temp 0.207530 dtype: float64 In [11]: Copied! fig = distributions ( df = df_pd [ cont_cols ], low_per_cut = 0 , high_per_cut = 1 , type = \"box\" , ) fig = distributions( df=df_pd[cont_cols], low_per_cut=0, high_per_cut=1, type=\"box\", ) /home/jovyan/success6g-edge/inference_model/eda/features/plotting.py:98: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding. ax[i].set_xlim(data[col].quantile(low_per_cut), data[col].quantile(high_per_cut))","title":"Plotting"},{"location":"notebooks/02_target_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? 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var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Target analysis \u00b6 Purpose : put features into context with predicted or actual target values analyse feature distribution per group in binary classification check correlation of features with target value check 'certainty' of the model predictions by plotting prob_distrib_per_class to see how certain the model is in predicting each class visually check predicted vs actual(ground truth values) by simple scatter plot by 'improved scatter plot' ( joint_dist ) - this can be usefull if the actual values form a cluster (eg. regression applied to clusters of users) and we want check if the model predictions also form a nice cluster with similar distribution as actual values Intended use : check how your model is performing check the possible connection between feature and target values (eg. if the distributions of features are different for each class) Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls , ) import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls, ) import ast Dataset \u00b6 In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None Analysis \u00b6 In [4]: Copied! sorted_corr_cols , fig = correlation ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], scale = \"linear\" , plot = True , ) sorted_corr_cols . head () sorted_corr_cols, fig = correlation( df=df_pd[cont_cols], target=df_pd[target_col], scale=\"linear\", plot=True, ) sorted_corr_cols.head() Out[4]: engine_load 0.818614 throttle_position 0.245668 maf 0.087101 control_voltage 0.042763 engine_speed -0.062052 Name: class, dtype: float64 Plotting \u00b6 Predicted and actual/ground_truth values were gathered using LightGBM with Optuna optimizer on the dataset In [5]: Copied! fig = distributions_in_binary_cls ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], low_per_cut = 0 , high_per_cut = 1 , ) fig = distributions_in_binary_cls( df=df_pd[cont_cols], target=df_pd[target_col], low_per_cut=0, high_per_cut=1, ) /home/jovyan/success6g-edge/inference_model/eda/target/plotting.py:123: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding. ax[i].set_xlim(","title":"02_target_analysis"},{"location":"notebooks/02_target_analysis.html#target-analysis","text":"Purpose : put features into context with predicted or actual target values analyse feature distribution per group in binary classification check correlation of features with target value check 'certainty' of the model predictions by plotting prob_distrib_per_class to see how certain the model is in predicting each class visually check predicted vs actual(ground truth values) by simple scatter plot by 'improved scatter plot' ( joint_dist ) - this can be usefull if the actual values form a cluster (eg. regression applied to clusters of users) and we want check if the model predictions also form a nice cluster with similar distribution as actual values Intended use : check how your model is performing check the possible connection between feature and target values (eg. if the distributions of features are different for each class)","title":"Target analysis"},{"location":"notebooks/02_target_analysis.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls , ) import ast import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import ( distributions_in_binary_cls, ) import ast","title":"Imports"},{"location":"notebooks/02_target_analysis.html#dataset","text":"In [2]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [3]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) cat_cols = None target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) cat_cols = None","title":"Dataset"},{"location":"notebooks/02_target_analysis.html#analysis","text":"In [4]: Copied! sorted_corr_cols , fig = correlation ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], scale = \"linear\" , plot = True , ) sorted_corr_cols . head () sorted_corr_cols, fig = correlation( df=df_pd[cont_cols], target=df_pd[target_col], scale=\"linear\", plot=True, ) sorted_corr_cols.head() Out[4]: engine_load 0.818614 throttle_position 0.245668 maf 0.087101 control_voltage 0.042763 engine_speed -0.062052 Name: class, dtype: float64","title":"Analysis"},{"location":"notebooks/02_target_analysis.html#plotting","text":"Predicted and actual/ground_truth values were gathered using LightGBM with Optuna optimizer on the dataset In [5]: Copied! fig = distributions_in_binary_cls ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], low_per_cut = 0 , high_per_cut = 1 , ) fig = distributions_in_binary_cls( df=df_pd[cont_cols], target=df_pd[target_col], low_per_cut=0, high_per_cut=1, ) /home/jovyan/success6g-edge/inference_model/eda/target/plotting.py:123: UserWarning: Attempting to set identical low and high xlims makes transformation singular; 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Models \u00b6 Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [2]: Copied! import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report , RocCurveDisplay , roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw , predict_proba_lgbm_from_raw , ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, RocCurveDisplay, roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw, predict_proba_lgbm_from_raw, ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() Dataset \u00b6 In [3]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True In [4]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [8]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) check possible class imbalance In [9]: Copied! df_pd [ target_col ] . value_counts () df_pd[target_col].value_counts() Out[9]: class 0 934 1 101 Name: count, dtype: int64 Preprocessing \u00b6 divide dataset into train, test, valid scale continuous columns by standard scaler(not needed for LightGBM but for other mdoels...) In [10]: Copied! df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) df_train . reset_index ( inplace = True , drop = True ) df_valid . reset_index ( inplace = True , drop = True ) df_test . reset_index ( inplace = True , drop = True ) scaler_mapper_def = { \"cont_cols\" : StandardScaler , \"cat_cols\" : None , \"id_cols\" : None , } scaler = scaler_mapper ( cont_cols = cont_cols , cat_cols = cat_cols , id_cols = [ target_col ] + id_cols , scaler_mapper_def = scaler_mapper_def , ) df_train_scaled = scaler . fit_transform ( df_train ) df_test_scaled = scaler . transform ( df_test ) df_valid_scaled = scaler . transform ( df_valid ) df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) df_train.reset_index(inplace=True, drop=True) df_valid.reset_index(inplace=True, drop=True) df_test.reset_index(inplace=True, drop=True) scaler_mapper_def = { \"cont_cols\": StandardScaler, \"cat_cols\": None, \"id_cols\": None, } scaler = scaler_mapper( cont_cols=cont_cols, cat_cols=cat_cols, id_cols=[target_col] + id_cols, scaler_mapper_def=scaler_mapper_def, ) df_train_scaled = scaler.fit_transform(df_train) df_test_scaled = scaler.transform(df_test) df_valid_scaled = scaler.transform(df_valid) Logistic Regression \u00b6 In [11]: Copied! LR_clf = LogisticRegression ( class_weight = \"balanced\" ) LR_clf . fit ( df_train_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ), df_train_scaled [ target_col ], ) LR_predicted = LR_clf . predict ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) print ( \"LR classification report : \\n \" + str ( classification_report ( df_test_scaled [ target_col ], LR_predicted )) ) LR_clf = LogisticRegression(class_weight=\"balanced\") LR_clf.fit( df_train_scaled.drop(columns=[target_col] + id_cols + cat_cols), df_train_scaled[target_col], ) LR_predicted = LR_clf.predict( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) print( \"LR classification report :\\n\" + str(classification_report(df_test_scaled[target_col], LR_predicted)) ) LR classification report : precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [12]: Copied! # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np . arange ( 0.2 , 0.8 , 0.01 ): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) )[:, 1 ] >= t ) . astype ( int ) threshold_score . append ( ( t , criterion ( df_test_scaled [ target_col ], preds_bin , average = \"weighted\" )) ) threshold_score = sorted ( threshold_score , key = lambda x : x [ 1 ], reverse = True ) best_threshold , best_score = threshold_score [ 0 ][ 0 ], threshold_score [ 0 ][ 1 ] print ( f \"The best threshold \\n { best_threshold } \\n , with score: \\n { best_score } \" ) # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np.arange(0.2, 0.8, 0.01): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) )[:, 1] >= t ).astype(int) threshold_score.append( (t, criterion(df_test_scaled[target_col], preds_bin, average=\"weighted\")) ) threshold_score = sorted(threshold_score, key=lambda x: x[1], reverse=True) best_threshold, best_score = threshold_score[0][0], threshold_score[0][1] print(f\"The best threshold\\n{best_threshold}\\n, with score:\\n{best_score}\") The best threshold 0.2 , with score: 1.0 In [13]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) In [14]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0 H2O AutoML \u00b6 NOTE : java is required : sudo apt install openjdk-17-jdk In [16]: Copied! # initialize H2O h2o . init ( log_dir = \"h2o_logs\" , log_level = \"WARN\" ) # read as h2o file h2o_train = h2o . H2OFrame ( df_train_scaled . drop ( columns = id_cols )) h2o_valid = h2o . H2OFrame ( df_valid_scaled . drop ( columns = id_cols )) h2o_test = h2o . H2OFrame ( df_test_scaled . drop ( columns = id_cols )) # For binary classification, response should be a factor h2o_train [ target_col ] = h2o_train [ target_col ] . asfactor () h2o_valid [ target_col ] = h2o_valid [ target_col ] . asfactor () h2o_test [ target_col ] = h2o_test [ target_col ] . asfactor () # Define AML task aml = H2OAutoML ( seed = random_state , max_runtime_secs = 180 ) # over/under sample for classification tasks aml . balance_classes = True # Run it _ = aml . train ( x = list ( cont_cols + cat_cols ), y = target_col , training_frame = h2o_train , leaderboard_frame = h2o_valid , ) m = aml . get_best_model () # Leaderboard, show and save lb = h2o . automl . get_leaderboard ( aml , extra_columns = \"ALL\" ) print ( lb ) print ( m . model_performance ( h2o_valid )) predictions = m . predict ( h2o_test ) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") # initialize H2O h2o.init(log_dir=\"h2o_logs\", log_level=\"WARN\") # read as h2o file h2o_train = h2o.H2OFrame(df_train_scaled.drop(columns=id_cols)) h2o_valid = h2o.H2OFrame(df_valid_scaled.drop(columns=id_cols)) h2o_test = h2o.H2OFrame(df_test_scaled.drop(columns=id_cols)) # For binary classification, response should be a factor h2o_train[target_col] = h2o_train[target_col].asfactor() h2o_valid[target_col] = h2o_valid[target_col].asfactor() h2o_test[target_col] = h2o_test[target_col].asfactor() # Define AML task aml = H2OAutoML(seed=random_state, max_runtime_secs=180) # over/under sample for classification tasks aml.balance_classes = True # Run it _ = aml.train( x=list(cont_cols + cat_cols), y=target_col, training_frame=h2o_train, leaderboard_frame=h2o_valid, ) m = aml.get_best_model() # Leaderboard, show and save lb = h2o.automl.get_leaderboard(aml, extra_columns=\"ALL\") print(lb) print(m.model_performance(h2o_valid)) predictions = m.predict(h2o_test) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") Checking whether there is an H2O instance running at http://localhost:54321..... not found. Attempting to start a local H2O server... Java Version: openjdk version \"17.0.10\" 2024-01-16; OpenJDK Runtime Environment (build 17.0.10+7-Ubuntu-122.04.1); OpenJDK 64-Bit Server VM (build 17.0.10+7-Ubuntu-122.04.1, mixed mode, sharing) Starting server from /opt/conda/lib/python3.10/site-packages/h2o/backend/bin/h2o.jar Ice root: /tmp/tmp0jm6weez JVM stdout: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.out JVM stderr: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.err Server is running at http://127.0.0.1:54321 Connecting to H2O server at http://127.0.0.1:54321 ... successful. #h2o-table-1.h2o-container { overflow-x: auto; } #h2o-table-1 .h2o-table { /* width: 100%; */ margin-top: 1em; margin-bottom: 1em; } #h2o-table-1 .h2o-table caption { white-space: nowrap; caption-side: top; text-align: left; /* margin-left: 1em; */ margin: 0; font-size: larger; } #h2o-table-1 .h2o-table thead { white-space: nowrap; position: sticky; top: 0; box-shadow: 0 -1px inset; } #h2o-table-1 .h2o-table tbody { overflow: auto; } #h2o-table-1 .h2o-table th, #h2o-table-1 .h2o-table td { text-align: right; /* border: 1px solid; */ } #h2o-table-1 .h2o-table tr:nth-child(even) { /* background: #F5F5F5 */ } H2O_cluster_uptime: 01 secs H2O_cluster_timezone: Etc/UTC H2O_data_parsing_timezone: UTC H2O_cluster_version: 3.46.0.2 H2O_cluster_version_age: 8 days H2O_cluster_name: H2O_from_python_unknownUser_maoqtf H2O_cluster_total_nodes: 1 H2O_cluster_free_memory: 16 Gb H2O_cluster_total_cores: 12 H2O_cluster_allowed_cores: 12 H2O_cluster_status: locked, healthy H2O_connection_url: http://127.0.0.1:54321 H2O_connection_proxy: {\"http\": null, \"https\": null} H2O_internal_security: False Python_version: 3.10.11 final Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% AutoML progress: | 13:16:41.801: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:44.939: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:46.154: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:16:59.224: _train param, Dropping unused columns: [barometric_pressure] 13:16:59.913: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:03.240: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:05.128: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:16.407: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588 13:17:27.855: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:17:40.196: _train param, Dropping unused columns: [barometric_pressure] 13:17:40.764: _train param, Dropping unused columns: [barometric_pressure] 13:17:41.338: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:44.575: _train param, Dropping bad and constant columns: [barometric_pressure] 13:17:45.413: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:53.937: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:55.712: _train param, Dropping unused columns: [barometric_pressure] 13:17:56.367: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 13:19:35.448: _train param, Dropping unused columns: [barometric_pressure] \u2588 13:19:36.22: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588| (done) 100% model_id auc logloss aucpr mean_per_class_error rmse mse training_time_ms predict_time_per_row_ms algo DeepLearning_grid_1_AutoML_1_20240522_131641_model_8 1 0.0177159 1 0 0.0582198 0.00338955 628 0.127786 DeepLearning DeepLearning_grid_1_AutoML_1_20240522_131641_model_4 1 0.0155053 1 0 0.0481989 0.00232314 117 0.1101 DeepLearning XGBoost_1_AutoML_1_20240522_131641 1 0.0347672 1 0 0.0789026 0.00622562 464 0.078661 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_12 1 0.0651884 1 0 0.106454 0.0113325 328 0.072441 XGBoost DeepLearning_grid_1_AutoML_1_20240522_131641_model_1 1 0.0235103 1 0 0.0600679 0.00360815 99 0.098096 DeepLearning XGBoost_grid_1_AutoML_1_20240522_131641_model_11 1 0.00556141 1 0 0.0320985 0.00103031 996 0.105708 XGBoost GBM_4_AutoML_1_20240522_131641 1 9.26741e-06 1 0 9.39779e-05 8.83184e-09 4293 0.280377 GBM XGBoost_grid_1_AutoML_1_20240522_131641_model_6 1 0.0141646 1 0 0.0244465 0.000597631 630 0.057813 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_13 1 0.0318374 1 0 0.0810283 0.00656559 745 0.069021 XGBoost GBM_3_AutoML_1_20240522_131641 1 2.55592e-05 1 0 0.000259039 6.7101e-08 4037 0.153118 GBM [47 rows x 10 columns] ModelMetricsBinomial: deeplearning ** Reported on test data. ** MSE: 0.0033895466349161047 RMSE: 0.05821981307867713 LogLoss: 0.017715874610611436 Mean Per-Class Error: 0.0 AUC: 1.0 AUCPR: 1.0 Gini: 1.0 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.9979014979275557 0 1 Error Rate ----- --- --- ------- ----------- 0 93 0 0 (0.0/93.0) 1 0 10 0 (0.0/10.0) Total 93 10 0 (0.0/103.0) Maximum Metrics: Maximum metrics at their respective thresholds metric threshold value idx --------------------------- ----------- ------- ----- max f1 0.997901 1 9 max f2 0.997901 1 9 max f0point5 0.997901 1 9 max accuracy 0.997901 1 9 max precision 0.999961 1 0 max recall 0.997901 1 9 max specificity 0.999961 1 0 max absolute_mcc 0.997901 1 9 max min_per_class_accuracy 0.997901 1 9 max mean_per_class_accuracy 0.997901 1 9 max tns 0.999961 93 0 max fns 0.999961 9 0 max fps 3.49717e-09 93 102 max tps 0.997901 10 9 max tnr 0.999961 1 0 max fnr 0.999961 0.9 0 max fpr 3.49717e-09 1 102 max tpr 0.997901 1 9 Gains/Lift Table: Avg response rate: 9.71 %, avg score: 11.26 % group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain kolmogorov_smirnov ------- -------------------------- ----------------- ------ ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------ ----------------- -------------------- 1 0.0194175 0.999958 10.3 10.3 1 0.999959 1 0.999959 0.2 0.2 930 930 0.2 2 0.0291262 0.999947 10.3 10.3 1 0.999947 1 0.999955 0.1 0.3 930 930 0.3 3 0.038835 0.999937 10.3 10.3 1 0.999938 1 0.999951 0.1 0.4 930 930 0.4 4 0.0485437 0.999927 10.3 10.3 1 0.99993 1 0.999947 0.1 0.5 930 930 0.5 5 0.0582524 0.999888 10.3 10.3 1 0.999889 1 0.999937 0.1 0.6 930 930 0.6 6 0.106796 0.372647 8.24 9.36364 0.8 0.879862 0.909091 0.945358 0.4 1 724 836.364 0.989247 7 0.15534 0.10825 0 6.4375 0 0.168191 0.625 0.702493 0 1 -100 543.75 0.935484 8 0.203883 0.00316095 0 4.90476 0 0.0682301 0.47619 0.551478 0 1 -100 390.476 0.88172 9 0.300971 0.000343083 0 3.32258 0 0.00100323 0.322581 0.373906 0 1 -100 232.258 0.774194 10 0.398058 0.000119786 0 2.5122 0 0.000202804 0.243902 0.282759 0 1 -100 151.22 0.666667 11 0.504854 8.52118e-05 0 1.98077 0 0.000102754 0.192308 0.222966 0 1 -100 98.0769 0.548387 12 0.601942 6.24023e-05 0 1.66129 0 7.12997e-05 0.16129 0.187015 0 1 -100 66.129 0.44086 13 0.699029 4.02512e-05 0 1.43056 0 5.12346e-05 0.138889 0.161048 0 1 -100 43.0556 0.333333 14 0.796117 2.31994e-05 0 1.2561 0 3.1514e-05 0.121951 0.141412 0 1 -100 25.6098 0.225806 15 0.893204 1.36306e-05 0 1.11957 0 1.91168e-05 0.108696 0.126043 0 1 -100 11.9565 0.11828 16 1 3.49717e-09 0 1 0 5.41845e-06 0.0970874 0.112583 0 1 -100 0 0 deeplearning prediction progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% In [17]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = h2o . as_list ( predictions . drop ([ \"predict\" ])) . values micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = h2o.as_list(predictions.drop([\"predict\"])).values micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" In [18]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0 In [19]: Copied! fig , ax = plt . subplots ( figsize = ( 6 , 6 )) n_classes = 2 for class_id in range ( n_classes ): RocCurveDisplay . from_predictions ( y_onehot_test [:, class_id ], y_score [:, class_id ], name = f \"ROC curve for class { class_id } \" , ax = ax , # plot_chance_level=(class_id == 2), ) fig, ax = plt.subplots(figsize=(6, 6)) n_classes = 2 for class_id in range(n_classes): RocCurveDisplay.from_predictions( y_onehot_test[:, class_id], y_score[:, class_id], name=f\"ROC curve for class {class_id}\", ax=ax, # plot_chance_level=(class_id == 2), ) In [20]: Copied! print ( \"Classification report: \\n {} \" . format ( classification_report ( df_test [ target_col ], h2o . as_list ( predictions [ \"predict\" ])) ) ) print( \"Classification report:\\n{}\".format( classification_report(df_test[target_col], h2o.as_list(predictions[\"predict\"])) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" LightGBM \u00b6 In [21]: Copied! config = {} config [ \"objective\" ] = \"binary\" df_train_scaled_enc = df_train_scaled . copy () df_valid_scaled_enc = df_valid_scaled . copy () df_test_scaled_enc = df_test_scaled . copy () lgb_cat_cols = cat_cols label_encoder = LabelEncoder ( lgb_cat_cols ) df_train_scaled_enc = label_encoder . fit_transform ( df_train_scaled_enc ) df_valid_scaled_enc = label_encoder . transform ( df_valid_scaled_enc ) df_test_scaled_enc = label_encoder . transform ( df_test_scaled_enc ) lgbtrain = lgbm . Dataset ( df_train_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_train_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbvalid = lgbm . Dataset ( df_valid_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_valid_scaled_enc [ target_col ], reference = lgbtrain , free_raw_data = False , ) # Final TRAIN/TEST ftrain = pd . concat ([ df_train_scaled_enc , df_valid_scaled_enc ]) . reset_index ( drop = True ) flgbtrain = lgbm . Dataset ( ftrain . drop ( columns = [ target_col ] + id_cols ), ftrain [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbtest = lgbm . Dataset ( df_test_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_test_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , reference = flgbtrain , free_raw_data = False , ) config = {} config[\"objective\"] = \"binary\" df_train_scaled_enc = df_train_scaled.copy() df_valid_scaled_enc = df_valid_scaled.copy() df_test_scaled_enc = df_test_scaled.copy() lgb_cat_cols = cat_cols label_encoder = LabelEncoder(lgb_cat_cols) df_train_scaled_enc = label_encoder.fit_transform(df_train_scaled_enc) df_valid_scaled_enc = label_encoder.transform(df_valid_scaled_enc) df_test_scaled_enc = label_encoder.transform(df_test_scaled_enc) lgbtrain = lgbm.Dataset( df_train_scaled_enc.drop(columns=[target_col] + id_cols), df_train_scaled_enc[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbvalid = lgbm.Dataset( df_valid_scaled_enc.drop(columns=[target_col] + id_cols), df_valid_scaled_enc[target_col], reference=lgbtrain, free_raw_data=False, ) # Final TRAIN/TEST ftrain = pd.concat([df_train_scaled_enc, df_valid_scaled_enc]).reset_index(drop=True) flgbtrain = lgbm.Dataset( ftrain.drop(columns=[target_col] + id_cols), ftrain[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbtest = lgbm.Dataset( df_test_scaled_enc.drop(columns=[target_col] + id_cols), df_test_scaled_enc[target_col], categorical_feature=lgb_cat_cols, reference=flgbtrain, free_raw_data=False, ) In [22]: Copied! model = lgbm . train ( config , lgbtrain , valid_sets = [ lgbvalid ], valid_names = [ \"\" ], feval = None , ) model = lgbm.train( config, lgbtrain, valid_sets=[lgbvalid], valid_names=[\"\"], feval=None, ) [LightGBM] [Info] Number of positive: 81, number of negative: 747 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002491 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 467 [LightGBM] [Info] Number of data points in the train set: 828, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097826 -> initscore=-2.221616 [LightGBM] [Info] Start training from score -2.221616 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf In [23]: Copied! res = predict_cls_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data , raw_score = True ), task = \"binary\" , ) result = pd . DataFrame ( { \"predicted\" : res , \"ground_truth\" : df_test [ target_col ] . values , } ) print ( \"Classification report: \\n {} \" . format ( classification_report ( result [ \"ground_truth\" ], result [ \"predicted\" ]) ) ) res = predict_cls_lgbm_from_raw( preds_raw=model.predict(lgbtest.data, raw_score=True), task=\"binary\", ) result = pd.DataFrame( { \"predicted\": res, \"ground_truth\": df_test[target_col].values, } ) print( \"Classification report:\\n{}\".format( classification_report(result[\"ground_truth\"], result[\"predicted\"]) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [24]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = predict_proba_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data ), task = \"binary\" , binary2d = True , ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = predict_proba_lgbm_from_raw( preds_raw=model.predict(lgbtest.data), task=\"binary\", binary2d=True, ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0","title":"03_models"},{"location":"notebooks/03_models.html#models","text":"","title":"Models"},{"location":"notebooks/03_models.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [2]: Copied! import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report , RocCurveDisplay , roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw , predict_proba_lgbm_from_raw , ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, RocCurveDisplay, roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from inference_model.preprocessing.scaler import scaler_mapper from inference_model.training.utils import ( predict_cls_lgbm_from_raw, predict_proba_lgbm_from_raw, ) from inference_model.preprocessing.label_encoder import LabelEncoder import ast # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start()","title":"Imports"},{"location":"notebooks/03_models.html#dataset","text":"In [3]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True In [4]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [8]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) check possible class imbalance In [9]: Copied! df_pd [ target_col ] . value_counts () df_pd[target_col].value_counts() Out[9]: class 0 934 1 101 Name: count, dtype: int64","title":"Dataset"},{"location":"notebooks/03_models.html#preprocessing","text":"divide dataset into train, test, valid scale continuous columns by standard scaler(not needed for LightGBM but for other mdoels...) In [10]: Copied! df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) df_train . reset_index ( inplace = True , drop = True ) df_valid . reset_index ( inplace = True , drop = True ) df_test . reset_index ( inplace = True , drop = True ) scaler_mapper_def = { \"cont_cols\" : StandardScaler , \"cat_cols\" : None , \"id_cols\" : None , } scaler = scaler_mapper ( cont_cols = cont_cols , cat_cols = cat_cols , id_cols = [ target_col ] + id_cols , scaler_mapper_def = scaler_mapper_def , ) df_train_scaled = scaler . fit_transform ( df_train ) df_test_scaled = scaler . transform ( df_test ) df_valid_scaled = scaler . transform ( df_valid ) df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) df_train.reset_index(inplace=True, drop=True) df_valid.reset_index(inplace=True, drop=True) df_test.reset_index(inplace=True, drop=True) scaler_mapper_def = { \"cont_cols\": StandardScaler, \"cat_cols\": None, \"id_cols\": None, } scaler = scaler_mapper( cont_cols=cont_cols, cat_cols=cat_cols, id_cols=[target_col] + id_cols, scaler_mapper_def=scaler_mapper_def, ) df_train_scaled = scaler.fit_transform(df_train) df_test_scaled = scaler.transform(df_test) df_valid_scaled = scaler.transform(df_valid)","title":"Preprocessing"},{"location":"notebooks/03_models.html#logistic-regression","text":"In [11]: Copied! LR_clf = LogisticRegression ( class_weight = \"balanced\" ) LR_clf . fit ( df_train_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ), df_train_scaled [ target_col ], ) LR_predicted = LR_clf . predict ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) print ( \"LR classification report : \\n \" + str ( classification_report ( df_test_scaled [ target_col ], LR_predicted )) ) LR_clf = LogisticRegression(class_weight=\"balanced\") LR_clf.fit( df_train_scaled.drop(columns=[target_col] + id_cols + cat_cols), df_train_scaled[target_col], ) LR_predicted = LR_clf.predict( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) print( \"LR classification report :\\n\" + str(classification_report(df_test_scaled[target_col], LR_predicted)) ) LR classification report : precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [12]: Copied! # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np . arange ( 0.2 , 0.8 , 0.01 ): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) )[:, 1 ] >= t ) . astype ( int ) threshold_score . append ( ( t , criterion ( df_test_scaled [ target_col ], preds_bin , average = \"weighted\" )) ) threshold_score = sorted ( threshold_score , key = lambda x : x [ 1 ], reverse = True ) best_threshold , best_score = threshold_score [ 0 ][ 0 ], threshold_score [ 0 ][ 1 ] print ( f \"The best threshold \\n { best_threshold } \\n , with score: \\n { best_score } \" ) # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np.arange(0.2, 0.8, 0.01): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) )[:, 1] >= t ).astype(int) threshold_score.append( (t, criterion(df_test_scaled[target_col], preds_bin, average=\"weighted\")) ) threshold_score = sorted(threshold_score, key=lambda x: x[1], reverse=True) best_threshold, best_score = threshold_score[0][0], threshold_score[0][1] print(f\"The best threshold\\n{best_threshold}\\n, with score:\\n{best_score}\") The best threshold 0.2 , with score: 1.0 In [13]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) In [14]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0","title":"Logistic Regression"},{"location":"notebooks/03_models.html#h2o-automl","text":"NOTE : java is required : sudo apt install openjdk-17-jdk In [16]: Copied! # initialize H2O h2o . init ( log_dir = \"h2o_logs\" , log_level = \"WARN\" ) # read as h2o file h2o_train = h2o . H2OFrame ( df_train_scaled . drop ( columns = id_cols )) h2o_valid = h2o . H2OFrame ( df_valid_scaled . drop ( columns = id_cols )) h2o_test = h2o . H2OFrame ( df_test_scaled . drop ( columns = id_cols )) # For binary classification, response should be a factor h2o_train [ target_col ] = h2o_train [ target_col ] . asfactor () h2o_valid [ target_col ] = h2o_valid [ target_col ] . asfactor () h2o_test [ target_col ] = h2o_test [ target_col ] . asfactor () # Define AML task aml = H2OAutoML ( seed = random_state , max_runtime_secs = 180 ) # over/under sample for classification tasks aml . balance_classes = True # Run it _ = aml . train ( x = list ( cont_cols + cat_cols ), y = target_col , training_frame = h2o_train , leaderboard_frame = h2o_valid , ) m = aml . get_best_model () # Leaderboard, show and save lb = h2o . automl . get_leaderboard ( aml , extra_columns = \"ALL\" ) print ( lb ) print ( m . model_performance ( h2o_valid )) predictions = m . predict ( h2o_test ) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") # initialize H2O h2o.init(log_dir=\"h2o_logs\", log_level=\"WARN\") # read as h2o file h2o_train = h2o.H2OFrame(df_train_scaled.drop(columns=id_cols)) h2o_valid = h2o.H2OFrame(df_valid_scaled.drop(columns=id_cols)) h2o_test = h2o.H2OFrame(df_test_scaled.drop(columns=id_cols)) # For binary classification, response should be a factor h2o_train[target_col] = h2o_train[target_col].asfactor() h2o_valid[target_col] = h2o_valid[target_col].asfactor() h2o_test[target_col] = h2o_test[target_col].asfactor() # Define AML task aml = H2OAutoML(seed=random_state, max_runtime_secs=180) # over/under sample for classification tasks aml.balance_classes = True # Run it _ = aml.train( x=list(cont_cols + cat_cols), y=target_col, training_frame=h2o_train, leaderboard_frame=h2o_valid, ) m = aml.get_best_model() # Leaderboard, show and save lb = h2o.automl.get_leaderboard(aml, extra_columns=\"ALL\") print(lb) print(m.model_performance(h2o_valid)) predictions = m.predict(h2o_test) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") Checking whether there is an H2O instance running at http://localhost:54321..... not found. Attempting to start a local H2O server... Java Version: openjdk version \"17.0.10\" 2024-01-16; OpenJDK Runtime Environment (build 17.0.10+7-Ubuntu-122.04.1); OpenJDK 64-Bit Server VM (build 17.0.10+7-Ubuntu-122.04.1, mixed mode, sharing) Starting server from /opt/conda/lib/python3.10/site-packages/h2o/backend/bin/h2o.jar Ice root: /tmp/tmp0jm6weez JVM stdout: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.out JVM stderr: /tmp/tmp0jm6weez/h2o_unknownUser_started_from_python.err Server is running at http://127.0.0.1:54321 Connecting to H2O server at http://127.0.0.1:54321 ... successful. #h2o-table-1.h2o-container { overflow-x: auto; } #h2o-table-1 .h2o-table { /* width: 100%; */ margin-top: 1em; margin-bottom: 1em; } #h2o-table-1 .h2o-table caption { white-space: nowrap; caption-side: top; text-align: left; /* margin-left: 1em; */ margin: 0; font-size: larger; } #h2o-table-1 .h2o-table thead { white-space: nowrap; position: sticky; top: 0; box-shadow: 0 -1px inset; } #h2o-table-1 .h2o-table tbody { overflow: auto; } #h2o-table-1 .h2o-table th, #h2o-table-1 .h2o-table td { text-align: right; /* border: 1px solid; */ } #h2o-table-1 .h2o-table tr:nth-child(even) { /* background: #F5F5F5 */ } H2O_cluster_uptime: 01 secs H2O_cluster_timezone: Etc/UTC H2O_data_parsing_timezone: UTC H2O_cluster_version: 3.46.0.2 H2O_cluster_version_age: 8 days H2O_cluster_name: H2O_from_python_unknownUser_maoqtf H2O_cluster_total_nodes: 1 H2O_cluster_free_memory: 16 Gb H2O_cluster_total_cores: 12 H2O_cluster_allowed_cores: 12 H2O_cluster_status: locked, healthy H2O_connection_url: http://127.0.0.1:54321 H2O_connection_proxy: {\"http\": null, \"https\": null} H2O_internal_security: False Python_version: 3.10.11 final Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% AutoML progress: | 13:16:41.801: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:44.939: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:16:46.154: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:16:59.224: _train param, Dropping unused columns: [barometric_pressure] 13:16:59.913: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:03.240: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:05.128: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:16.407: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588 13:17:27.855: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588 13:17:40.196: _train param, Dropping unused columns: [barometric_pressure] 13:17:40.764: _train param, Dropping unused columns: [barometric_pressure] 13:17:41.338: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:44.575: _train param, Dropping bad and constant columns: [barometric_pressure] 13:17:45.413: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588\u2588\u2588 13:17:53.937: _train param, Dropping bad and constant columns: [barometric_pressure] \u2588 13:17:55.712: _train param, Dropping unused columns: [barometric_pressure] 13:17:56.367: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 13:19:35.448: _train param, Dropping unused columns: [barometric_pressure] \u2588 13:19:36.22: _train param, Dropping unused columns: [barometric_pressure] \u2588\u2588| (done) 100% model_id auc logloss aucpr mean_per_class_error rmse mse training_time_ms predict_time_per_row_ms algo DeepLearning_grid_1_AutoML_1_20240522_131641_model_8 1 0.0177159 1 0 0.0582198 0.00338955 628 0.127786 DeepLearning DeepLearning_grid_1_AutoML_1_20240522_131641_model_4 1 0.0155053 1 0 0.0481989 0.00232314 117 0.1101 DeepLearning XGBoost_1_AutoML_1_20240522_131641 1 0.0347672 1 0 0.0789026 0.00622562 464 0.078661 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_12 1 0.0651884 1 0 0.106454 0.0113325 328 0.072441 XGBoost DeepLearning_grid_1_AutoML_1_20240522_131641_model_1 1 0.0235103 1 0 0.0600679 0.00360815 99 0.098096 DeepLearning XGBoost_grid_1_AutoML_1_20240522_131641_model_11 1 0.00556141 1 0 0.0320985 0.00103031 996 0.105708 XGBoost GBM_4_AutoML_1_20240522_131641 1 9.26741e-06 1 0 9.39779e-05 8.83184e-09 4293 0.280377 GBM XGBoost_grid_1_AutoML_1_20240522_131641_model_6 1 0.0141646 1 0 0.0244465 0.000597631 630 0.057813 XGBoost XGBoost_grid_1_AutoML_1_20240522_131641_model_13 1 0.0318374 1 0 0.0810283 0.00656559 745 0.069021 XGBoost GBM_3_AutoML_1_20240522_131641 1 2.55592e-05 1 0 0.000259039 6.7101e-08 4037 0.153118 GBM [47 rows x 10 columns] ModelMetricsBinomial: deeplearning ** Reported on test data. ** MSE: 0.0033895466349161047 RMSE: 0.05821981307867713 LogLoss: 0.017715874610611436 Mean Per-Class Error: 0.0 AUC: 1.0 AUCPR: 1.0 Gini: 1.0 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.9979014979275557 0 1 Error Rate ----- --- --- ------- ----------- 0 93 0 0 (0.0/93.0) 1 0 10 0 (0.0/10.0) Total 93 10 0 (0.0/103.0) Maximum Metrics: Maximum metrics at their respective thresholds metric threshold value idx --------------------------- ----------- ------- ----- max f1 0.997901 1 9 max f2 0.997901 1 9 max f0point5 0.997901 1 9 max accuracy 0.997901 1 9 max precision 0.999961 1 0 max recall 0.997901 1 9 max specificity 0.999961 1 0 max absolute_mcc 0.997901 1 9 max min_per_class_accuracy 0.997901 1 9 max mean_per_class_accuracy 0.997901 1 9 max tns 0.999961 93 0 max fns 0.999961 9 0 max fps 3.49717e-09 93 102 max tps 0.997901 10 9 max tnr 0.999961 1 0 max fnr 0.999961 0.9 0 max fpr 3.49717e-09 1 102 max tpr 0.997901 1 9 Gains/Lift Table: Avg response rate: 9.71 %, avg score: 11.26 % group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain kolmogorov_smirnov ------- -------------------------- ----------------- ------ ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------ ----------------- -------------------- 1 0.0194175 0.999958 10.3 10.3 1 0.999959 1 0.999959 0.2 0.2 930 930 0.2 2 0.0291262 0.999947 10.3 10.3 1 0.999947 1 0.999955 0.1 0.3 930 930 0.3 3 0.038835 0.999937 10.3 10.3 1 0.999938 1 0.999951 0.1 0.4 930 930 0.4 4 0.0485437 0.999927 10.3 10.3 1 0.99993 1 0.999947 0.1 0.5 930 930 0.5 5 0.0582524 0.999888 10.3 10.3 1 0.999889 1 0.999937 0.1 0.6 930 930 0.6 6 0.106796 0.372647 8.24 9.36364 0.8 0.879862 0.909091 0.945358 0.4 1 724 836.364 0.989247 7 0.15534 0.10825 0 6.4375 0 0.168191 0.625 0.702493 0 1 -100 543.75 0.935484 8 0.203883 0.00316095 0 4.90476 0 0.0682301 0.47619 0.551478 0 1 -100 390.476 0.88172 9 0.300971 0.000343083 0 3.32258 0 0.00100323 0.322581 0.373906 0 1 -100 232.258 0.774194 10 0.398058 0.000119786 0 2.5122 0 0.000202804 0.243902 0.282759 0 1 -100 151.22 0.666667 11 0.504854 8.52118e-05 0 1.98077 0 0.000102754 0.192308 0.222966 0 1 -100 98.0769 0.548387 12 0.601942 6.24023e-05 0 1.66129 0 7.12997e-05 0.16129 0.187015 0 1 -100 66.129 0.44086 13 0.699029 4.02512e-05 0 1.43056 0 5.12346e-05 0.138889 0.161048 0 1 -100 43.0556 0.333333 14 0.796117 2.31994e-05 0 1.2561 0 3.1514e-05 0.121951 0.141412 0 1 -100 25.6098 0.225806 15 0.893204 1.36306e-05 0 1.11957 0 1.91168e-05 0.108696 0.126043 0 1 -100 11.9565 0.11828 16 1 3.49717e-09 0 1 0 5.41845e-06 0.0970874 0.112583 0 1 -100 0 0 deeplearning prediction progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% In [17]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = h2o . as_list ( predictions . drop ([ \"predict\" ])) . values micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = h2o.as_list(predictions.drop([\"predict\"])).values micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" In [18]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0 In [19]: Copied! fig , ax = plt . subplots ( figsize = ( 6 , 6 )) n_classes = 2 for class_id in range ( n_classes ): RocCurveDisplay . from_predictions ( y_onehot_test [:, class_id ], y_score [:, class_id ], name = f \"ROC curve for class { class_id } \" , ax = ax , # plot_chance_level=(class_id == 2), ) fig, ax = plt.subplots(figsize=(6, 6)) n_classes = 2 for class_id in range(n_classes): RocCurveDisplay.from_predictions( y_onehot_test[:, class_id], y_score[:, class_id], name=f\"ROC curve for class {class_id}\", ax=ax, # plot_chance_level=(class_id == 2), ) In [20]: Copied! print ( \"Classification report: \\n {} \" . format ( classification_report ( df_test [ target_col ], h2o . as_list ( predictions [ \"predict\" ])) ) ) print( \"Classification report:\\n{}\".format( classification_report(df_test[target_col], h2o.as_list(predictions[\"predict\"])) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\"","title":"H2O AutoML"},{"location":"notebooks/03_models.html#lightgbm","text":"In [21]: Copied! config = {} config [ \"objective\" ] = \"binary\" df_train_scaled_enc = df_train_scaled . copy () df_valid_scaled_enc = df_valid_scaled . copy () df_test_scaled_enc = df_test_scaled . copy () lgb_cat_cols = cat_cols label_encoder = LabelEncoder ( lgb_cat_cols ) df_train_scaled_enc = label_encoder . fit_transform ( df_train_scaled_enc ) df_valid_scaled_enc = label_encoder . transform ( df_valid_scaled_enc ) df_test_scaled_enc = label_encoder . transform ( df_test_scaled_enc ) lgbtrain = lgbm . Dataset ( df_train_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_train_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbvalid = lgbm . Dataset ( df_valid_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_valid_scaled_enc [ target_col ], reference = lgbtrain , free_raw_data = False , ) # Final TRAIN/TEST ftrain = pd . concat ([ df_train_scaled_enc , df_valid_scaled_enc ]) . reset_index ( drop = True ) flgbtrain = lgbm . Dataset ( ftrain . drop ( columns = [ target_col ] + id_cols ), ftrain [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbtest = lgbm . Dataset ( df_test_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_test_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , reference = flgbtrain , free_raw_data = False , ) config = {} config[\"objective\"] = \"binary\" df_train_scaled_enc = df_train_scaled.copy() df_valid_scaled_enc = df_valid_scaled.copy() df_test_scaled_enc = df_test_scaled.copy() lgb_cat_cols = cat_cols label_encoder = LabelEncoder(lgb_cat_cols) df_train_scaled_enc = label_encoder.fit_transform(df_train_scaled_enc) df_valid_scaled_enc = label_encoder.transform(df_valid_scaled_enc) df_test_scaled_enc = label_encoder.transform(df_test_scaled_enc) lgbtrain = lgbm.Dataset( df_train_scaled_enc.drop(columns=[target_col] + id_cols), df_train_scaled_enc[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbvalid = lgbm.Dataset( df_valid_scaled_enc.drop(columns=[target_col] + id_cols), df_valid_scaled_enc[target_col], reference=lgbtrain, free_raw_data=False, ) # Final TRAIN/TEST ftrain = pd.concat([df_train_scaled_enc, df_valid_scaled_enc]).reset_index(drop=True) flgbtrain = lgbm.Dataset( ftrain.drop(columns=[target_col] + id_cols), ftrain[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbtest = lgbm.Dataset( df_test_scaled_enc.drop(columns=[target_col] + id_cols), df_test_scaled_enc[target_col], categorical_feature=lgb_cat_cols, reference=flgbtrain, free_raw_data=False, ) In [22]: Copied! model = lgbm . train ( config , lgbtrain , valid_sets = [ lgbvalid ], valid_names = [ \"\" ], feval = None , ) model = lgbm.train( config, lgbtrain, valid_sets=[lgbvalid], valid_names=[\"\"], feval=None, ) [LightGBM] [Info] Number of positive: 81, number of negative: 747 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002491 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 467 [LightGBM] [Info] Number of data points in the train set: 828, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097826 -> initscore=-2.221616 [LightGBM] [Info] Start training from score -2.221616 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best 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positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf In [23]: Copied! res = predict_cls_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data , raw_score = True ), task = \"binary\" , ) result = pd . DataFrame ( { \"predicted\" : res , \"ground_truth\" : df_test [ target_col ] . values , } ) print ( \"Classification report: \\n {} \" . format ( classification_report ( result [ \"ground_truth\" ], result [ \"predicted\" ]) ) ) res = predict_cls_lgbm_from_raw( preds_raw=model.predict(lgbtest.data, raw_score=True), task=\"binary\", ) result = pd.DataFrame( { \"predicted\": res, \"ground_truth\": df_test[target_col].values, } ) print( \"Classification report:\\n{}\".format( classification_report(result[\"ground_truth\"], result[\"predicted\"]) ) ) Classification report: precision recall f1-score support 0 1.00 1.00 1.00 94 1 1.00 1.00 1.00 10 accuracy 1.00 104 macro avg 1.00 1.00 1.00 104 weighted avg 1.00 1.00 1.00 104 In [24]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = predict_proba_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data ), task = \"binary\" , binary2d = True , ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = predict_proba_lgbm_from_raw( preds_raw=model.predict(lgbtest.data), task=\"binary\", binary2d=True, ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [1. 1.] Weighted AUC: 1.0","title":"LightGBM"},{"location":"notebooks/04_model_training_pipeline.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Example usage notebook \u00b6 Objects and functions in this notebook are listed with all paramaters to ilustrate their capabillities. Most of the paramaters have default values in the implementation Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd import mlflow pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) os . environ [ \"AWS_ACCESS_KEY_ID\" ] = \"minioadmin\" os . environ [ \"AWS_SECRET_ACCESS_KEY\" ] = \"minioadmin\" os . environ [ \"MLFLOW_S3_ENDPOINT_URL\" ] = f \"http://10.152.183.156:9000\" import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd import mlflow pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\" In [2]: Copied! import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump , dill_load from inference_model.training.utils import flatten_dict , get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump, dill_load from inference_model.training.utils import flatten_dict, get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast Train a testing model \u00b6 In [3]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [4]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) In [6]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) In [7]: Copied! prepare_data = PreprocessData ( id_cols = id_cols , target_col = target_col , cat_cols = cat_cols , cont_cols = cont_cols , ) # this should be fitted only on training data _ = prepare_data . fit ( df = df_pd ) prepare_data = PreprocessData( id_cols=id_cols, target_col=target_col, cat_cols=cat_cols, cont_cols=cont_cols, ) # this should be fitted only on training data _ = prepare_data.fit(df=df_pd) In [8]: Copied! optimizer = LGBOptunaOptimizer ( objective = \"binary\" , n_class = 2 , ) trainer = Trainer ( cat_cols = prepare_data . cat_cols , target_col = prepare_data . target_col , id_cols = id_cols , objective = \"binary\" , n_class = 2 , optimizer = optimizer , preprocessors = [ prepare_data ], ) metrics_dict = trainer . fit ( df_train = df_train , df_valid = df_valid , df_test = df_test , ) optimizer = LGBOptunaOptimizer( objective=\"binary\", n_class=2, ) trainer = Trainer( cat_cols=prepare_data.cat_cols, target_col=prepare_data.target_col, id_cols=id_cols, objective=\"binary\", n_class=2, optimizer=optimizer, preprocessors=[prepare_data], ) metrics_dict = trainer.fit( df_train=df_train, df_valid=df_valid, df_test=df_test, ) [I 2024-05-22 13:01:18,589] A new study created in memory with name: LightGBMTuner feature_fraction, val_score: inf: 0%| | 0/7 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000002: 14%|#4 | 1/7 [00:03<00:21, 3.62s/it][I 2024-05-22 13:01:22,217] Trial 0 finished with value: 1.6789085046471396e-06 and parameters: {'feature_fraction': 0.4}. Best is trial 0 with value: 1.6789085046471396e-06. feature_fraction, val_score: 0.000002: 14%|#4 | 1/7 [00:03<00:21, 3.62s/it] Early stopping, best iteration is: [588] valid_0's binary_logloss: 1.67891e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000002: 29%|##8 | 2/7 [00:05<00:13, 2.72s/it][I 2024-05-22 13:01:24,308] Trial 1 finished with value: 1.9702431647601935e-06 and parameters: {'feature_fraction': 0.8999999999999999}. Best is trial 0 with value: 1.6789085046471396e-06. feature_fraction, val_score: 0.000002: 29%|##8 | 2/7 [00:05<00:13, 2.72s/it] Early stopping, best iteration is: [380] valid_0's binary_logloss: 1.97024e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 43%|####2 | 3/7 [00:06<00:06, 1.71s/it][I 2024-05-22 13:01:24,812] Trial 2 finished with value: 1.3616097829410516e-06 and parameters: {'feature_fraction': 1.0}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 43%|####2 | 3/7 [00:06<00:06, 1.71s/it] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 57%|#####7 | 4/7 [00:07<00:04, 1.40s/it][I 2024-05-22 13:01:25,731] Trial 3 finished with value: 2.2594885710041037e-06 and parameters: {'feature_fraction': 0.6}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 57%|#####7 | 4/7 [00:07<00:04, 1.40s/it] Early stopping, best iteration is: [794] valid_0's binary_logloss: 2.25949e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 71%|#######1 | 5/7 [00:08<00:02, 1.21s/it][I 2024-05-22 13:01:26,610] Trial 4 finished with value: 2.5490428264265304e-06 and parameters: {'feature_fraction': 0.8}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 71%|#######1 | 5/7 [00:08<00:02, 1.21s/it] Early stopping, best iteration is: [485] valid_0's binary_logloss: 2.54904e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 86%|########5 | 6/7 [00:09<00:01, 1.14s/it][I 2024-05-22 13:01:27,606] Trial 5 finished with value: 2.2594885710041037e-06 and parameters: {'feature_fraction': 0.5}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 86%|########5 | 6/7 [00:09<00:01, 1.14s/it] Early stopping, best iteration is: [794] valid_0's binary_logloss: 2.25949e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 100%|##########| 7/7 [00:10<00:00, 1.10s/it][I 2024-05-22 13:01:28,615] Trial 6 finished with value: 2.5249718256638313e-06 and parameters: {'feature_fraction': 0.7}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 100%|##########| 7/7 [00:10<00:00, 1.43s/it] Early stopping, best iteration is: [715] valid_0's binary_logloss: 2.52497e-06 num_leaves, val_score: 0.000001: 0%| | 0/20 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 5%|5 | 1/20 [00:00<00:09, 1.99it/s][I 2024-05-22 13:01:29,127] Trial 7 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 227}. Best is trial 7 with value: 1.3616097829410516e-06. num_leaves, val_score: 0.000001: 5%|5 | 1/20 [00:00<00:09, 1.99it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 10%|# | 2/20 [00:00<00:08, 2.01it/s][I 2024-05-22 13:01:29,623] Trial 8 finished with value: 1.361609811862523e-06 and parameters: {'num_leaves': 14}. Best is trial 7 with value: 1.3616097829410516e-06. num_leaves, val_score: 0.000001: 10%|# | 2/20 [00:01<00:08, 2.01it/s] Early stopping, best iteration is: [390] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 15%|#5 | 3/20 [00:01<00:08, 2.00it/s][I 2024-05-22 13:01:30,125] Trial 9 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 90}. Best is trial 7 with value: 1.3616097829410516e-06. num_leaves, val_score: 0.000001: 15%|#5 | 3/20 [00:01<00:08, 2.00it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 20%|## | 4/20 [00:02<00:08, 1.88it/s][I 2024-05-22 13:01:30,707] Trial 10 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 255}. Best is trial 10 with value: 1.3616097829410512e-06. num_leaves, val_score: 0.000001: 20%|## | 4/20 [00:02<00:08, 1.88it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 25%|##5 | 5/20 [00:02<00:07, 1.89it/s][I 2024-05-22 13:01:31,227] Trial 11 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 252}. Best is trial 10 with value: 1.3616097829410512e-06. num_leaves, val_score: 0.000001: 25%|##5 | 5/20 [00:02<00:07, 1.89it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 30%|### | 6/20 [00:03<00:08, 1.70it/s][I 2024-05-22 13:01:31,929] Trial 12 finished with value: 1.361609782941051e-06 and parameters: {'num_leaves': 161}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 30%|### | 6/20 [00:03<00:08, 1.70it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 35%|###5 | 7/20 [00:03<00:07, 1.70it/s][I 2024-05-22 13:01:32,519] Trial 13 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 169}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 35%|###5 | 7/20 [00:03<00:07, 1.70it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 40%|#### | 8/20 [00:04<00:06, 1.77it/s][I 2024-05-22 13:01:33,097] Trial 14 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 156}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 40%|#### | 8/20 [00:04<00:06, 1.77it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 45%|####5 | 9/20 [00:05<00:06, 1.67it/s][I 2024-05-22 13:01:33,710] Trial 15 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 196}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 45%|####5 | 9/20 [00:05<00:06, 1.67it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 50%|##### | 10/20 [00:05<00:05, 1.75it/s][I 2024-05-22 13:01:34,220] Trial 16 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 111}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 50%|##### | 10/20 [00:05<00:05, 1.75it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 55%|#####5 | 11/20 [00:06<00:04, 1.82it/s][I 2024-05-22 13:01:34,717] Trial 17 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 57}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 55%|#####5 | 11/20 [00:06<00:04, 1.82it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 60%|###### | 12/20 [00:06<00:04, 1.87it/s][I 2024-05-22 13:01:35,221] Trial 18 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 212}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 60%|###### | 12/20 [00:06<00:04, 1.87it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 65%|######5 | 13/20 [00:07<00:03, 1.90it/s][I 2024-05-22 13:01:35,730] Trial 19 finished with value: 1.3616097829410518e-06 and parameters: {'num_leaves': 255}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 65%|######5 | 13/20 [00:07<00:03, 1.90it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 70%|####### | 14/20 [00:07<00:03, 1.85it/s][I 2024-05-22 13:01:36,303] Trial 20 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 150}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 70%|####### | 14/20 [00:07<00:03, 1.85it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 75%|#######5 | 15/20 [00:08<00:02, 1.86it/s][I 2024-05-22 13:01:36,827] Trial 21 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 186}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 75%|#######5 | 15/20 [00:08<00:02, 1.86it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 80%|######## | 16/20 [00:08<00:02, 1.82it/s][I 2024-05-22 13:01:37,407] Trial 22 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 171}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 80%|######## | 16/20 [00:08<00:02, 1.82it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 85%|########5 | 17/20 [00:09<00:01, 1.86it/s][I 2024-05-22 13:01:37,920] Trial 23 finished with value: 1.3616097829410518e-06 and parameters: {'num_leaves': 127}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 85%|########5 | 17/20 [00:09<00:01, 1.86it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 90%|######### | 18/20 [00:09<00:01, 1.88it/s][I 2024-05-22 13:01:38,433] Trial 24 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 220}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 90%|######### | 18/20 [00:09<00:01, 1.88it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 95%|#########5| 19/20 [00:10<00:00, 1.83it/s][I 2024-05-22 13:01:39,016] Trial 25 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 85}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 95%|#########5| 19/20 [00:10<00:00, 1.83it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 100%|##########| 20/20 [00:10<00:00, 1.77it/s][I 2024-05-22 13:01:39,624] Trial 26 finished with value: 1.361609782941051e-06 and parameters: {'num_leaves': 140}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 100%|##########| 20/20 [00:11<00:00, 1.82it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 bagging, val_score: 0.000001: 0%| | 0/10 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 10%|# | 1/10 [00:00<00:05, 1.65it/s][I 2024-05-22 13:01:40,309] Trial 27 finished with value: 3.449205755838957e-07 and parameters: {'bagging_fraction': 0.4418842108373299, 'bagging_freq': 5}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 10%|# | 1/10 [00:00<00:05, 1.65it/s] Early stopping, best iteration is: [266] valid_0's binary_logloss: 3.44921e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 20%|## | 2/10 [00:00<00:03, 2.63it/s][I 2024-05-22 13:01:40,531] Trial 28 finished with value: 0.003198735879283877 and parameters: {'bagging_fraction': 0.40010886623598485, 'bagging_freq': 5}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 20%|## | 2/10 [00:00<00:03, 2.63it/s] Early stopping, best iteration is: [49] valid_0's binary_logloss: 0.00319874 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 30%|### | 3/10 [00:01<00:03, 2.29it/s][I 2024-05-22 13:01:41,034] Trial 29 finished with value: 2.3253951344156617e-06 and parameters: {'bagging_fraction': 0.5615890028222774, 'bagging_freq': 3}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 30%|### | 3/10 [00:01<00:03, 2.29it/s] Early stopping, best iteration is: [216] valid_0's binary_logloss: 2.3254e-06 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 40%|#### | 4/10 [00:02<00:03, 1.89it/s][I 2024-05-22 13:01:41,710] Trial 30 finished with value: 1.6847441854237672e-06 and parameters: {'bagging_fraction': 0.9729155031966343, 'bagging_freq': 7}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 40%|#### | 4/10 [00:02<00:03, 1.89it/s] Early stopping, best iteration is: [253] valid_0's binary_logloss: 1.68474e-06 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 50%|##### | 5/10 [00:02<00:02, 1.90it/s][I 2024-05-22 13:01:42,229] Trial 31 finished with value: 2.3371325286333586e-06 and parameters: {'bagging_fraction': 0.7517048260537027, 'bagging_freq': 1}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 50%|##### | 5/10 [00:02<00:02, 1.90it/s] Early stopping, best iteration is: [197] valid_0's binary_logloss: 2.33713e-06 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 60%|###### | 6/10 [00:02<00:01, 2.23it/s][I 2024-05-22 13:01:42,525] Trial 32 finished with value: 0.0031987358794118973 and parameters: {'bagging_fraction': 0.40032139731038635, 'bagging_freq': 5}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 60%|###### | 6/10 [00:02<00:01, 2.23it/s] Early stopping, best iteration is: [49] valid_0's binary_logloss: 0.00319874 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 70%|####### | 7/10 [00:03<00:01, 2.17it/s][I 2024-05-22 13:01:43,013] Trial 33 finished with value: 2.9743999611270926e-07 and parameters: {'bagging_fraction': 0.6425832769598463, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 70%|####### | 7/10 [00:03<00:01, 2.17it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.9744e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 80%|######## | 8/10 [00:03<00:00, 2.24it/s][I 2024-05-22 13:01:43,427] Trial 34 finished with value: 3.040896374710074e-07 and parameters: {'bagging_fraction': 0.6280540860504722, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 80%|######## | 8/10 [00:03<00:00, 2.24it/s] Early stopping, best iteration is: [208] valid_0's binary_logloss: 3.0409e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 90%|######### | 9/10 [00:04<00:00, 2.19it/s][I 2024-05-22 13:01:43,909] Trial 35 finished with value: 7.219543528343957e-07 and parameters: {'bagging_fraction': 0.6210765540576059, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 90%|######### | 9/10 [00:04<00:00, 2.19it/s] Early stopping, best iteration is: [187] valid_0's binary_logloss: 7.21954e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 100%|##########| 10/10 [00:04<00:00, 2.25it/s][I 2024-05-22 13:01:44,327] Trial 36 finished with value: 3.404675474216927e-07 and parameters: {'bagging_fraction': 0.6658803881381716, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 100%|##########| 10/10 [00:04<00:00, 2.16it/s] Early stopping, best iteration is: [251] valid_0's binary_logloss: 3.40468e-07 feature_fraction_stage2, val_score: 0.000000: 0%| | 0/3 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.000000: 33%|###3 | 1/3 [00:00<00:00, 2.51it/s][I 2024-05-22 13:01:44,809] Trial 37 finished with value: 2.9743999611270936e-07 and parameters: {'feature_fraction': 0.9520000000000001}. Best is trial 37 with value: 2.9743999611270936e-07. feature_fraction_stage2, val_score: 0.000000: 33%|###3 | 1/3 [00:00<00:00, 2.51it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.9744e-07 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.000000: 67%|######6 | 2/3 [00:00<00:00, 2.80it/s][I 2024-05-22 13:01:45,199] Trial 38 finished with value: 2.9743999611270936e-07 and parameters: {'feature_fraction': 0.9840000000000001}. Best is trial 37 with value: 2.9743999611270936e-07. feature_fraction_stage2, val_score: 0.000000: 67%|######6 | 2/3 [00:00<00:00, 2.80it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.9744e-07 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.000000: 100%|##########| 3/3 [00:01<00:00, 2.20it/s][I 2024-05-22 13:01:45,710] Trial 39 finished with value: 4.498413698090123e-07 and parameters: {'feature_fraction': 0.92}. Best is trial 37 with value: 2.9743999611270936e-07. feature_fraction_stage2, val_score: 0.000000: 100%|##########| 3/3 [00:01<00:00, 2.29it/s] Early stopping, best iteration is: [256] valid_0's binary_logloss: 4.49841e-07 regularization_factors, val_score: 0.000000: 0%| | 0/20 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds Early stopping, best iteration is: [227] valid_0's binary_logloss: 0.00690416 regularization_factors, val_score: 0.000000: 5%|5 | 1/20 [00:00<00:05, 3.40it/s][I 2024-05-22 13:01:46,021] Trial 40 finished with value: 0.006904162868543424 and parameters: {'lambda_l1': 2.2800500671305803, 'lambda_l2': 0.0011641838798314252}. Best is trial 40 with value: 0.006904162868543424. regularization_factors, val_score: 0.000000: 5%|5 | 1/20 [00:00<00:05, 3.40it/s] Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 10%|# | 2/20 [00:00<00:06, 2.87it/s][I 2024-05-22 13:01:46,408] Trial 41 finished with value: 2.974565285658327e-07 and parameters: {'lambda_l1': 3.2596973482462435e-08, 'lambda_l2': 1.061451695187828e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 10%|# | 2/20 [00:00<00:06, 2.87it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97457e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 15%|#5 | 3/20 [00:01<00:06, 2.68it/s][I 2024-05-22 13:01:46,811] Trial 42 finished with value: 2.974705202215368e-07 and parameters: {'lambda_l1': 2.241952369984708e-08, 'lambda_l2': 1.8880584030540923e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 15%|#5 | 3/20 [00:01<00:06, 2.68it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97471e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 20%|## | 4/20 [00:01<00:05, 2.83it/s][I 2024-05-22 13:01:47,131] Trial 43 finished with value: 2.9748702210105415e-07 and parameters: {'lambda_l1': 1.0199507520333984e-08, 'lambda_l2': 2.130651303908614e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 20%|## | 4/20 [00:01<00:05, 2.83it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97487e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 25%|##5 | 5/20 [00:01<00:05, 2.72it/s][I 2024-05-22 13:01:47,526] Trial 44 finished with value: 2.974846523730242e-07 and parameters: {'lambda_l1': 1.6736475077289633e-08, 'lambda_l2': 1.0112045153335179e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 25%|##5 | 5/20 [00:01<00:05, 2.72it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97485e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 30%|### | 6/20 [00:02<00:04, 2.89it/s][I 2024-05-22 13:01:47,829] Trial 45 finished with value: 3.176935636511711e-07 and parameters: {'lambda_l1': 5.882861681810232e-06, 'lambda_l2': 1.1233467846223274e-06}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 30%|### | 6/20 [00:02<00:04, 2.89it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 3.17694e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 35%|###5 | 7/20 [00:03<00:08, 1.61it/s][I 2024-05-22 13:01:49,021] Trial 46 finished with value: 0.00013806811413863104 and parameters: {'lambda_l1': 1.0772166664037463e-05, 'lambda_l2': 3.5907966551315282}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 35%|###5 | 7/20 [00:03<00:08, 1.61it/s] Did not meet early stopping. Best iteration is: [1000] valid_0's binary_logloss: 0.000138068 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 40%|#### | 8/20 [00:03<00:06, 1.91it/s][I 2024-05-22 13:01:49,331] Trial 47 finished with value: 3.3174632310732203e-07 and parameters: {'lambda_l1': 9.228838671620809e-07, 'lambda_l2': 1.2357091396245195e-05}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 40%|#### | 8/20 [00:03<00:06, 1.91it/s] Early stopping, best iteration is: [209] valid_0's binary_logloss: 3.31746e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 45%|####5 | 9/20 [00:03<00:05, 2.10it/s][I 2024-05-22 13:01:49,706] Trial 48 finished with value: 2.8970612681555003e-06 and parameters: {'lambda_l1': 0.004116041688790151, 'lambda_l2': 4.91159030541583e-07}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 45%|####5 | 9/20 [00:03<00:05, 2.10it/s] Early stopping, best iteration is: [206] valid_0's binary_logloss: 2.89706e-06 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 50%|##### | 10/20 [00:04<00:04, 2.33it/s][I 2024-05-22 13:01:50,028] Trial 49 finished with value: 5.975862396554039e-07 and parameters: {'lambda_l1': 2.4738617566052645e-07, 'lambda_l2': 0.003148588239925189}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 50%|##### | 10/20 [00:04<00:04, 2.33it/s] Early stopping, best iteration is: [283] valid_0's binary_logloss: 5.97586e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 55%|#####5 | 11/20 [00:04<00:03, 2.41it/s][I 2024-05-22 13:01:50,411] Trial 50 finished with value: 5.528528493352603e-07 and parameters: {'lambda_l1': 0.0005147056784853866, 'lambda_l2': 1.3373340341538605e-05}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 55%|#####5 | 11/20 [00:04<00:03, 2.41it/s] Early stopping, best iteration is: [204] valid_0's binary_logloss: 5.52853e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 60%|###### | 12/20 [00:05<00:03, 2.45it/s][I 2024-05-22 13:01:50,806] Trial 51 finished with value: 2.97483914403635e-07 and parameters: {'lambda_l1': 1.1161313264875577e-08, 'lambda_l2': 1.3457934495742431e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 60%|###### | 12/20 [00:05<00:03, 2.45it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97484e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 65%|######5 | 13/20 [00:05<00:02, 2.63it/s][I 2024-05-22 13:01:51,122] Trial 52 finished with value: 2.974834213929394e-07 and parameters: {'lambda_l1': 1.1851264993602946e-08, 'lambda_l2': 1.1579633227443254e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 65%|######5 | 13/20 [00:05<00:02, 2.63it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97483e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 70%|####### | 14/20 [00:05<00:02, 2.59it/s][I 2024-05-22 13:01:51,521] Trial 53 finished with value: 3.0224491028537356e-07 and parameters: {'lambda_l1': 1.8444434562217248e-07, 'lambda_l2': 1.904342905861566e-07}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 70%|####### | 14/20 [00:05<00:02, 2.59it/s] Early stopping, best iteration is: [204] valid_0's binary_logloss: 3.02245e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 75%|#######5 | 15/20 [00:06<00:02, 2.38it/s][I 2024-05-22 13:01:52,020] Trial 54 finished with value: 2.97111604544352e-07 and parameters: {'lambda_l1': 1.8697547616086913e-07, 'lambda_l2': 1.4195276461779107e-07}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 75%|#######5 | 15/20 [00:06<00:02, 2.38it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97112e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 80%|######## | 16/20 [00:06<00:01, 2.61it/s][I 2024-05-22 13:01:52,319] Trial 55 finished with value: 2.7975386625160922e-05 and parameters: {'lambda_l1': 9.1209069884061e-06, 'lambda_l2': 2.832296835692325e-07}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 80%|######## | 16/20 [00:06<00:01, 2.61it/s] Early stopping, best iteration is: [150] valid_0's binary_logloss: 2.79754e-05 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 85%|########5 | 17/20 [00:06<00:01, 2.77it/s][I 2024-05-22 13:01:52,623] Trial 56 finished with value: 3.012783257895089e-07 and parameters: {'lambda_l1': 5.249197497865728e-07, 'lambda_l2': 8.723247474648092e-06}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 85%|########5 | 17/20 [00:06<00:01, 2.77it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 3.01278e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 90%|######### | 18/20 [00:07<00:00, 2.31it/s][I 2024-05-22 13:01:53,223] Trial 57 finished with value: 6.216923691382048e-06 and parameters: {'lambda_l1': 1.2654511369988902e-07, 'lambda_l2': 0.026856741790607278}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 90%|######### | 18/20 [00:07<00:00, 2.31it/s] Early stopping, best iteration is: [494] valid_0's binary_logloss: 6.21692e-06 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 95%|#########5| 19/20 [00:07<00:00, 2.57it/s][I 2024-05-22 13:01:53,511] Trial 58 finished with value: 4.6829312861054814e-05 and parameters: {'lambda_l1': 0.02854037816163236, 'lambda_l2': 1.2681271167280724e-07}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 95%|#########5| 19/20 [00:07<00:00, 2.57it/s] Early stopping, best iteration is: [182] valid_0's binary_logloss: 4.68293e-05 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 100%|##########| 20/20 [00:08<00:00, 2.74it/s][I 2024-05-22 13:01:53,821] Trial 59 finished with value: 2.468284408525897e-07 and parameters: {'lambda_l1': 0.0001184573048303984, 'lambda_l2': 4.182514336496212e-06}. Best is trial 59 with value: 2.468284408525897e-07. regularization_factors, val_score: 0.000000: 100%|##########| 20/20 [00:08<00:00, 2.47it/s] Early stopping, best iteration is: [240] valid_0's binary_logloss: 2.46828e-07 min_child_samples, val_score: 0.000000: 0%| | 0/5 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 20%|## | 1/5 [00:00<00:01, 3.44it/s][I 2024-05-22 13:01:54,119] Trial 60 finished with value: 5.156782324432179e-06 and parameters: {'min_child_samples': 10}. Best is trial 60 with value: 5.156782324432179e-06. min_child_samples, val_score: 0.000000: 20%|## | 1/5 [00:00<00:01, 3.44it/s] Early stopping, best iteration is: [199] valid_0's binary_logloss: 5.15678e-06 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 40%|#### | 2/5 [00:00<00:00, 3.32it/s][I 2024-05-22 13:01:54,427] Trial 61 finished with value: 3.282327081150373e-07 and parameters: {'min_child_samples': 100}. Best is trial 61 with value: 3.282327081150373e-07. min_child_samples, val_score: 0.000000: 40%|#### | 2/5 [00:00<00:00, 3.32it/s] Early stopping, best iteration is: [269] valid_0's binary_logloss: 3.28233e-07 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 60%|###### | 3/5 [00:00<00:00, 3.41it/s][I 2024-05-22 13:01:54,712] Trial 62 finished with value: 3.6291058037098226e-06 and parameters: {'min_child_samples': 5}. Best is trial 61 with value: 3.282327081150373e-07. min_child_samples, val_score: 0.000000: 60%|###### | 3/5 [00:00<00:00, 3.41it/s] Early stopping, best iteration is: [176] valid_0's binary_logloss: 3.62911e-06 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 80%|######## | 4/5 [00:01<00:00, 3.78it/s][I 2024-05-22 13:01:54,932] Trial 63 finished with value: 3.8920361508245125e-07 and parameters: {'min_child_samples': 25}. Best is trial 61 with value: 3.282327081150373e-07. min_child_samples, val_score: 0.000000: 80%|######## | 4/5 [00:01<00:00, 3.78it/s] Early stopping, best iteration is: [204] valid_0's binary_logloss: 3.89204e-07 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 100%|##########| 5/5 [00:01<00:00, 3.62it/s][I 2024-05-22 13:01:55,228] Trial 64 finished with value: 2.1603725624802887e-07 and parameters: {'min_child_samples': 50}. Best is trial 64 with value: 2.1603725624802887e-07. min_child_samples, val_score: 0.000000: 100%|##########| 5/5 [00:01<00:00, 3.56it/s] Early stopping, best iteration is: [252] valid_0's binary_logloss: 2.16037e-07 In [12]: Copied! # save/load trainer dill_dump ( \"lgbm_trainer.dill\" , trainer ) trainer = dill_load ( \"lgbm_trainer.dill\" ) # save/load trainer dill_dump(\"lgbm_trainer.dill\", trainer) trainer = dill_load(\"lgbm_trainer.dill\") In [13]: Copied! pprint ( metrics_dict ) pprint(metrics_dict) {'cls_report': {'0': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 94}, '1': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 10}, 'accuracy': 1.0, 'macro avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}, 'weighted avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}}, 'cm': [[94, 0], [0, 10]], 'prec_rec_curve': [[0.09615384615384616, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 1.0]]} In [14]: Copied! pprint ( trainer . compute_metrics ( df_test , with_dynamic_binary_threshold = True )) pprint(trainer.compute_metrics(df_test, with_dynamic_binary_threshold=True)) {'cls_report': {'0': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 94}, '1': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 10}, 'accuracy': 1.0, 'macro avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}, 'weighted avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}}, 'cm': [[94, 0], [0, 10]], 'prec_rec_curve': [[0.09615384615384616, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 1.0]]} Mlflow \u00b6 tracking model registration in Minio permanent experiment deletion # in python mlflow.get_experiment_by_name(\"success6g_lgbm\").experiment_id # in cmd mlflow experiments delete -x 1 In [26]: Copied! mlflow_host = \"10.152.183.54\" mlflow_host_url = \"mlflow.mlflow.svc.cluster.local\" mlflow_port = \"5000\" os . environ [ \"AWS_ACCESS_KEY_ID\" ] = \"minioadmin\" os . environ [ \"AWS_SECRET_ACCESS_KEY\" ] = \"minioadmin\" os . environ [ \"MLFLOW_S3_ENDPOINT_URL\" ] = f \"http://10.152.183.156:9000\" mlflow . set_tracking_uri ( \"http://\" + mlflow_host + \":\" + mlflow_port ) experiment_id = get_or_create_experiment ( \"success6g\" ) mlflow . set_experiment ( experiment_id = experiment_id ) mlflow_host = \"10.152.183.54\" mlflow_host_url = \"mlflow.mlflow.svc.cluster.local\" mlflow_port = \"5000\" os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\" mlflow.set_tracking_uri(\"http://\" + mlflow_host + \":\" + mlflow_port) experiment_id = get_or_create_experiment(\"success6g\") mlflow.set_experiment(experiment_id=experiment_id) Out[26]: <Experiment: artifact_location='s3://mlflow/3', creation_time=1716388845676, experiment_id='3', last_update_time=1716388845676, lifecycle_stage='active', name='success6g', tags={}> In [27]: Copied! metrics_dict_flattened = flatten_dict ( metrics_dict ) # mlflow metrics can be only int, float not list del metrics_dict_flattened [ \"cm\" ] del metrics_dict_flattened [ \"prec_rec_curve\" ] metrics_dict_flattened = flatten_dict(metrics_dict) # mlflow metrics can be only int, float not list del metrics_dict_flattened[\"cm\"] del metrics_dict_flattened[\"prec_rec_curve\"] In [29]: Copied! run_name = \"test\" with mlflow . start_run ( experiment_id = experiment_id , run_name = run_name , nested = True ) as run : mlflow . log_params ( trainer . optimizer . best ) mlflow . log_metrics ( metrics_dict_flattened ) # Log tags mlflow . set_tags ( tags = { \"project\" : \"SUCCESS6G\" , \"optimizer_engine\" : \"optuna\" , \"model_family\" : \"ligtgbm\" , \"feature_set_version\" : 1 , } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow . pyfunc . log_model ( python_model = trainer , artifact_path = artifact_path , registered_model_name = registered_model_name , ) model_uri = mlflow . get_artifact_uri ( artifact_path ) print ( f \"Run ID: \\n { run . info . run_id } \\n Model uri: \\n { model_uri } \" ) run_name = \"test\" with mlflow.start_run( experiment_id=experiment_id, run_name=run_name, nested=True ) as run: mlflow.log_params(trainer.optimizer.best) mlflow.log_metrics(metrics_dict_flattened) # Log tags mlflow.set_tags( tags={ \"project\": \"SUCCESS6G\", \"optimizer_engine\": \"optuna\", \"model_family\": \"ligtgbm\", \"feature_set_version\": 1, } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow.pyfunc.log_model( python_model=trainer, artifact_path=artifact_path, registered_model_name=registered_model_name, ) model_uri = mlflow.get_artifact_uri(artifact_path) print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\") 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: typing.Union[pandas.core.frame.DataFrame, dict], skipping schema inference 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: <class 'pandas.core.frame.DataFrame'>, skipping schema inference Successfully registered model 'success6g_model'. 2024/05/22 14:44:22 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: success6g_model, version 1 Run ID: 24fedb96c5004767800fa32a3220c8dd Model uri: s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model Created version '1' of model 'success6g_model'. Testing of predictions \u00b6 Example data \u00b6 In [30]: Copied! df_pd . iloc [: 2 ] df_pd.iloc[:2] Out[30]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc In [31]: Copied! # for testing using mlflow inference service request_dict = { \"engine_load\" : [ 100.0 , 100.0 ], \"engine_coolant_temp\" : [ 17.0 , 17.0 ], \"engine_speed\" : [ 904.5 , 906.0 ], \"vehicle_speed\" : [ 0.0 , 0.0 ], \"intake_air_temp\" : [ 10.0 , 11.0 ], \"maf\" : [ 12.55 , 12.36 ], \"throttle_position\" : [ 83.14 , 83.14 ], \"fuel_rg_pressure\" : [ 37270.0 , 37800.0 ], \"barometric_pressure\" : [ 101.0 , 101.0 ], \"control_voltage\" : [ 0.06 , 14.56 ], \"class\" : [ 1 , 1 ], \"car_id\" : [ \"123abc\" , \"123abc\" ], } # for testing using mlflow inference service request_dict = { \"engine_load\": [100.0, 100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"class\": [1, 1], \"car_id\": [\"123abc\", \"123abc\"], } Trainer \u00b6 In [32]: Copied! trainer . predict ( df = df_pd . iloc [: 2 ] . drop ( columns = [ \"class\" ]), context = {}) trainer.predict(df=df_pd.iloc[:2].drop(columns=[\"class\"]), context={}) Out[32]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 0 14.269467 1 14.269467 Downloaded Trainer \u00b6 In [33]: Copied! model_uri model_uri Out[33]: 's3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model' In [34]: Copied! loaded_trainer = mlflow . pyfunc . load_model ( model_uri ) loaded_trainer = mlflow.pyfunc.load_model(model_uri) Downloading artifacts: 0%| | 0/9 [00:00<?, ?it/s] In [35]: Copied! loaded_trainer . predict ( df_pd . iloc [: 2 ] . drop ( columns = [ \"class\" ])) loaded_trainer.predict(df_pd.iloc[:2].drop(columns=[\"class\"])) Out[35]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 0 14.269467 1 14.269467 Downloaded/Served Trainer \u00b6 i.e. testing model locally In [36]: Copied! model_uri model_uri Out[36]: 's3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model' In [ ]: Copied! ! mlflow models serve - m \"s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model\" -- env - manager local - p 5000 ! mlflow models serve -m \"s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model\" --env-manager local -p 5000 Downloading artifacts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 2088.80it/s] 2024/05/22 14:45:07 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function' Downloading artifacts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 9/9 [00:00<00:00, 69.72it/s] 2024/05/22 14:45:07 INFO mlflow.pyfunc.backend: === Running command 'exec gunicorn --timeout=60 -b 127.0.0.1:5000 -w 1 ${GUNICORN_CMD_ARGS} -- mlflow.pyfunc.scoring_server.wsgi:app' [2024-05-22 14:45:07 +0000] [17971] [INFO] Starting gunicorn 22.0.0 [2024-05-22 14:45:07 +0000] [17971] [INFO] Listening at: http://127.0.0.1:5000 (17971) [2024-05-22 14:45:07 +0000] [17971] [INFO] Using worker: sync [2024-05-22 14:45:07 +0000] [17972] [INFO] Booting worker with pid: 17972 root@jupyter-5uperpalo:~/assignment# curl -X POST -H \"Content-Type:application/json\" --data '{\"inputs\": {\"engine_load\": [100.0 ,100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"car_id\": [\"123abc\", \"123abc\"]}}' http://127.0.0.1:5000/invocations {\"predictions\": [{\"class\": 14.26946728558632}, {\"class\": 14.26946728558632}]} Model deployment using Kserve \u00b6 https://mlflow.org/docs/latest/deployment/deploy-model-to-kubernetes/tutorial.html?highlight=kserve#step-7-deploying-the-model-to-kserve Using model_uri \u00b6 since I configured minio as artifact store I can use model_uri - I was WRONG! procedure: make sure the kserve is able to reach minio * by [defining s3-secret and attaching it to service account](https://kserve.github.io/website/0.7/modelserving/storage/s3/s3/#create-s3-secret) * created ServiceAccount must be in a same namespace as the InferenceService we are going to create, [e.g. stackoverflow here](https://stackoverflow.com/a/70977798) * `If an application does not specify a serviceAccountName, it uses the \"default\" service account.`[e.g. here](https://kubernetes.io/docs/reference/access-authn-authz/rbac/#role-binding-examples) ``` apiVersion: v1 kind: Secret metadata: name: s3creds namespace: mlflow-kserve-success6g annotations: serving.kserve.io/s3-endpoint: 10.152.183.156:9000 # replace with your s3 endpoint e.g minio-service.kubeflow:9000 serving.kserve.io/s3-usehttps: \"0\" # by default 1, if testing with minio you can set to 0 # serving.kserve.io/s3-region: \"us-east-2\" serving.kserve.io/s3-useanoncredential: \"false\" # omitting this is the same as false, if true will ignore provided credential and use anonymous credentials type: Opaque stringData: # use `stringData` for raw credential string or `data` for base64 encoded string AWS_ACCESS_KEY_ID: \"minioadmin\" AWS_SECRET_ACCESS_KEY: \"minioadmin\" ``` `kubectl apply -f create-s3-secret.yaml` create namespace ``` kubectl create namespace mlflow-kserve-success6g namespace/mlflow-kserve-success6g created ``` specify configuration of the deployment in the file `mlflow-kserve-success6g_model_uri.yaml` ``` apiVersion: \"serving.kserve.io/v1beta1\" kind: \"InferenceService\" metadata: name: \"mlflow-success6g\" namespace: \"mlflow-kserve-success6g\" spec: predictor: model: modelFormat: name: mlflow protocolVersion: v2 storageUri: \"s3://mlflow/1/777cf64c922149a4b77c85987865deb0/artifacts/success6g_model\" ``` deploy ``` kubectl apply -f mlflow-kserve-success6g.yaml inferenceservice.serving.kserve.io/mlflow-success6g created ``` check the status of the deployment `kubectl get inferenceservice mlflow-success6g` specify testing json in `test-input.json` ``` { \"inputs\": [ { \"name\": \"input\", \"shape\": [13], \"datatype\": \"FP32\", \"data\": { \"engine_load\": [100.0 ,100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"car_id\": [\"123abc\", \"123abc\"], } } ] } ``` test json ``` SERVICE_HOSTNAME=$(kubectl get inferenceservice mlflow-success6g -n mlflow-kserve-success6g -o jsonpath='{.status.url}' | cut -d \"/\" -f 3) curl -v \\ -H \"Host: ${SERVICE_HOSTNAME}\" \\ -H \"Content-Type: application/json\" \\ -d @./test-input.json \\ http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/mlflow-success6g/infer ``` but after all the trouble this can still fail if you developed the model in different python version than kserve is using ``` pmulinka@saiacheron:~/kubernetes/kserve$ kubectl logs -p mlflow-success6g-predictor-00001-deployment-74ccf4fb5d-lktz9 -n mlflow-kserve-success6g Defaulted container \"kserve-container\" out of: kserve-container, queue-proxy, storage-initializer (init) Environment tarball not found at '/mnt/models/environment.tar.gz' Environment not found at './envs/environment' 2024-05-16 15:32:55,293 [mlserver.parallel] DEBUG - Starting response processing loop... 2024-05-16 15:32:55,298 [mlserver.rest] INFO - HTTP server running on http://0.0.0.0:8080 INFO: Started server process [1] INFO: Waiting for application startup. 2024-05-16 15:32:55,411 [mlserver.metrics] INFO - Metrics server running on http://0.0.0.0:8082 2024-05-16 15:32:55,412 [mlserver.metrics] INFO - Prometheus scraping endpoint can be accessed on http://0.0.0.0:8082/metrics INFO: Started server process [1] INFO: Waiting for application startup. INFO: Application startup complete. 2024-05-16 15:32:57,613 [mlserver.grpc] INFO - gRPC server running on http://0.0.0.0:9000 INFO: Application startup complete. INFO: Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit) INFO: Uvicorn running on http://0.0.0.0:8082 (Press CTRL+C to quit) 2024/05/16 15:32:58 WARNING mlflow.pyfunc: Detected one or more mismatches between the model's dependencies and the current Python environment: - mlflow (current: 2.3.1, required: mlflow==2.12.2) - cryptography (current: 38.0.4, required: cryptography==40.0.2) - cytoolz (current: uninstalled, required: cytoolz==0.12.0) - defusedxml (current: uninstalled, required: defusedxml==0.7.1) - distributed (current: uninstalled, required: distributed==2023.5.1) - lightgbm (current: 3.3.5, required: lightgbm==4.3.0) - lz4 (current: uninstalled, required: lz4==4.3.2) - numpy (current: 1.23.5, required: numpy==1.24.3) - optuna-integration (current: uninstalled, required: optuna-integration==3.6.0) - pandas (current: 2.0.1, required: pandas==2.2.2) - pyarrow (current: 11.0.0, required: pyarrow==12.0.0) - redis (current: uninstalled, required: redis==5.0.4) - scipy (current: 1.9.1, required: scipy==1.10.1) To fix the mismatches, call `mlflow.pyfunc.get_model_dependencies(model_uri)` to fetch the model's environment and install dependencies using the resulting environment file. 2024/05/16 15:32:58 WARNING mlflow.pyfunc: The version of Python that the model was saved in, `Python 3.10.11`, differs from the version of Python that is currently running, `Python 3.8.16`, and may be incompatible 2024-05-16 15:32:58,967 [mlserver] INFO - Couldn't load model 'mlflow-success6g'. Model will be removed from registry. 2024-05-16 15:32:58,967 [mlserver.parallel] ERROR - An error occurred processing a model update of type 'Load'. Traceback (most recent call last): File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/worker.py\", line 158, in _process_model_update await self._model_registry.load(model_settings) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 293, in load return await self._models[model_settings.name].load(model_settings) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 148, in load await self._load_model(new_model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 165, in _load_model model.ready = await model.load() File \"/opt/conda/lib/python3.8/site-packages/mlserver_mlflow/runtime.py\", line 155, in load self._model = mlflow.pyfunc.load_model(model_uri) File \"/opt/conda/lib/python3.8/site-packages/mlflow/pyfunc/__init__.py\", line 596, in load_model model_impl = importlib.import_module(conf[MAIN])._load_pyfunc(data_path) File \"/opt/conda/lib/python3.8/site-packages/mlflow/pyfunc/model.py\", line 291, in _load_pyfunc python_model = cloudpickle.load(f) ModuleNotFoundError: No module named 'inference_model' 2024-05-16 15:32:58,970 [mlserver] INFO - Couldn't load model 'mlflow-success6g'. Model will be removed from registry. 2024-05-16 15:32:58,975 [mlserver.parallel] ERROR - An error occurred processing a model update of type 'Unload'. Traceback (most recent call last): File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/worker.py\", line 160, in _process_model_update await self._model_registry.unload_version( File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 302, in unload_version await model_registry.unload_version(version) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 201, in unload_version model = await self.get_model(version) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 237, in get_model raise ModelNotFound(self._name, version) mlserver.errors.ModelNotFound: Model mlflow-success6g not found 2024-05-16 15:32:58,976 [mlserver] ERROR - Some of the models failed to load during startup! Traceback (most recent call last): File \"/opt/conda/lib/python3.8/site-packages/mlserver/server.py\", line 125, in start await asyncio.gather( File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 293, in load return await self._models[model_settings.name].load(model_settings) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 148, in load await self._load_model(new_model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 161, in _load_model model = await callback(model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/registry.py\", line 152, in load_model loaded = await pool.load_model(model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/pool.py\", line 74, in load_model await self._dispatcher.dispatch_update(load_message) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 123, in dispatch_update return await asyncio.gather( File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 138, in _dispatch_update return await self._dispatch(worker_update) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 146, in _dispatch return await self._wait_response(internal_id) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 152, in _wait_response inference_response = await async_response mlserver.parallel.errors.WorkerError: builtins.ModuleNotFoundError: No module named 'inference_model' 2024-05-16 15:32:58,978 [mlserver.parallel] INFO - Waiting for shutdown of default inference pool... 2024-05-16 15:32:59,163 [mlserver.parallel] INFO - Shutdown of default inference pool complete 2024-05-16 15:32:59,163 [mlserver.grpc] INFO - Waiting for gRPC server shutdown 2024-05-16 15:32:59,169 [mlserver.grpc] INFO - gRPC server shutdown complete INFO: Shutting down INFO: Shutting down INFO: Waiting for application shutdown. INFO: Application shutdown complete. INFO: Finished server process [1] INFO: Waiting for application shutdown. INFO: Application shutdown complete. INFO: Finished server process [1] ``` and some possible readiness probe issues * apparently I am not alone in this, e.g. [here](https://stackoverflow.com/questions/62569747/ml-model-pod-keeps-restarting-in-seldon-deployment) ``` pmulinka@saiacheron:~/kubernetes/kserve$ kubectl describe pod mlflow-success6g-predictor-00001-deployment-74ccf4fb5d-lktz9 -n mlflow-kserve-success6g Events: Type Reason Age From Message ---- ------ ---- ---- ------- Warning Unhealthy 4m11s (x268 over 34m) kubelet Readiness probe failed: Get \"http://10.1.4.204:8012/\": context deadline exceeded (Client.Timeout exceeded while awaiting headers) ``` Using docker image \u00b6 create docker ``` sudo apt-get install python3.10-venv python3.10 -m venv python310venv source python310venv/bin/activate pip install mlflow pip install boto3 export AWS_ACCESS_KEY_ID=minioadmin export AWS_SECRET_ACCESS_KEY=minioadmin export MLFLOW_S3_ENDPOINT_URL=http://10.152.183.156:9000 mlflow models build-docker -m s3://mlflow/1/777cf64c922149a4b77c85987865deb0/artifacts/success6g_model -n 5uperpalo/mlflow-success6g --enable-mlserver ``` generate token and login to DockerHub, [generate DockerHub token](https://hub.docker.com/settings/security?generateToken=true) and login ``` (python310venv) pmulinka@iesc-gpu3:~/scripts$ docker login -u 5uperpalo Password: WARNING! Your password will be stored unencrypted in /home/pmulinka/.docker/config.json. Configure a credential helper to remove this warning. See https://docs.docker.com/engine/reference/commandline/login/#credentials-store Login Succeeded ``` push the image to dockerhub ``` (python310venv) pmulinka@iesc-gpu3:~/scripts$ docker push 5uperpalo/mlflow-success6g Using default tag: latest The push refers to repository [docker.io/5uperpalo/mlflow-success6g] e5f76b582f6c: Pushed 624821c51019: Pushed 93256a85bb86: Pushed 2f62dcdb5ae7: Pushed 1146ddf03b42: Pushed 639ddc87d13f: Pushed 4f18e59d57c4: Pushed 5761e01a334a: Pushed f31b6bde159a: Pushed c25ef1a18d31: Pushed 40a774d6610c: Pushed a2b215b4258b: Pushed b56a9203d2a0: Pushed 2536dbfb2294: Pushed 67ea405a7d49: Pushed a81c3606ee5c: Pushed 4a1518ebc26e: Mounted from library/ubuntu latest: digest: sha256:57f653a3c8d63519c07dccffc6eb12dba5006db07ebcd869467ce752fb1b0bda size: 3899 ``` specify configuration of the deployment in the file `mlflow-kserve-success6g_model_uri.yaml` ``` apiVersion: \"serving.kserve.io/v1beta1\" kind: \"InferenceService\" metadata: name: \"mlflow-success6g\" namespace: \"mlflow-kserve-success6g\" spec: predictor: containers: - name: \"mlflow-success6g\" image: \"5uperpalo/mlflow-success6g\" ports: - containerPort: 8080 protocol: TCP #env: #- name: PROTOCOL #value: \"v2\" resources: limits: cpu: 2 memory: 8Gi requests: cpu: 2 memory: 8Gi ``` deploy ``` pmulinka@saiacheron:~/kubernetes/kserve$ kubectl apply -f mlflow-kserve-success6g_docker_image.yaml inferenceservice.serving.kserve.io/mlflow-success6g created ``` check status of deployment ``` pmulinka@saiacheron:~$ kubectl get inferenceservice -n mlflow-kserve-success6g NAME URL READY PREV LATEST PREVROLLEDOUTREVISION LATESTREADYREVISION AGE mlflow-success6g http://mlflow-success6g.mlflow-kserve-success6g.svc.cluster.local False 100 mlflow-success6g-predictor-00001 6m4s pmulinka@saiacheron:~$ kubectl get pods -n mlflow-kserve-success6g NAME READY STATUS RESTARTS AGE mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 2/2 Running 1 (2m14s ago) 4m47s pmulinka@saiacheron:~$ kubectl get inferenceservice mlflow-success6g -n mlflow-kserve-success6g -o yaml apiVersion: serving.kserve.io/v1beta1 kind: InferenceService metadata: annotations: kubectl.kubernetes.io/last-applied-configuration: | {\"apiVersion\":\"serving.kserve.io/v1beta1\",\"kind\":\"InferenceService\",\"metadata\":{\"annotations\":{},\"name\":\"mlflow-success6g\",\"namespace\":\"mlflow-kserve-success6g\"},\"spec\":{\"predictor\":{\"containers\":[{\"image\":\"5uperpalo/mlflow-success6g\",\"name\":\"mlflow-success6g\",\"ports\":[{\"containerPort\":8080,\"protocol\":\"TCP\"}],\"resources\":{\"limits\":{\"cpu\":2,\"memory\":\"8Gi\"},\"requests\":{\"cpu\":2,\"memory\":\"8Gi\"}}}]}}} creationTimestamp: \"2024-05-28T14:33:05Z\" finalizers: - inferenceservice.finalizers generation: 1 name: mlflow-success6g namespace: mlflow-kserve-success6g resourceVersion: \"76010518\" uid: b9573ff3-0f67-4650-a0c7-51941a0fffe2 spec: predictor: containers: - image: 5uperpalo/mlflow-success6g name: kserve-container ports: - containerPort: 8080 protocol: TCP resources: limits: cpu: \"2\" memory: 8Gi requests: cpu: \"2\" memory: 8Gi status: address: url: http://mlflow-success6g.mlflow-kserve-success6g.svc.cluster.local components: predictor: address: url: http://mlflow-success6g-predictor.mlflow-kserve-success6g.svc.cluster.local latestCreatedRevision: mlflow-success6g-predictor-00001 latestReadyRevision: mlflow-success6g-predictor-00001 latestRolledoutRevision: mlflow-success6g-predictor-00001 traffic: - latestRevision: true percent: 100 revisionName: mlflow-success6g-predictor-00001 url: http://mlflow-success6g-predictor.mlflow-kserve-success6g.svc.cluster.local conditions: - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: Predictor ingress not created status: \"False\" type: IngressReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: PredictorConfigurationReady not ready severity: Info status: \"False\" type: LatestDeploymentReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" message: 'Revision \"mlflow-success6g-predictor-00001\" failed with message: Container failed with: container exited with no error.' reason: RevisionFailed severity: Info status: \"False\" type: PredictorConfigurationReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" message: Revision \"mlflow-success6g-predictor-00001\" failed to become ready. reason: RevisionMissing status: \"False\" type: PredictorReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" message: Revision \"mlflow-success6g-predictor-00001\" failed to become ready. reason: RevisionMissing severity: Info status: \"False\" type: PredictorRouteReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: Predictor ingress not created status: \"False\" type: Ready - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: PredictorRouteReady not ready severity: Info status: \"False\" type: RoutesReady modelStatus: copies: failedCopies: 0 totalCopies: 1 states: activeModelState: Loaded targetModelState: Pending transitionStatus: InProgress observedGeneration: 1 url: http://mlflow-success6g.mlflow-kserve-success6g.svc.cluster.local pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get events -n mlflow-kserve-success6g LAST SEEN TYPE REASON OBJECT MESSAGE 2m28s Warning BackOff pod/mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 Back-off restarting failed container kserve-container in pod mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7_mlflow-kserve-success6g(1ad2d6da-3a40-4b14-956a-0faee3ca9b3c) 2m28s Warning InternalError revision/mlflow-success6g-predictor-00001 failed to update PA \"mlflow-success6g-predictor-00001\": Operation cannot be fulfilled on podautoscalers.autoscaling.internal.knative.dev \"mlflow-success6g-predictor-00001\": the object has been modified; please apply your changes to the latest version and try again ``` debugging ``` # https://kserve.github.io/website/0.10/developer/debug/ pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get ksvc -n mlflow-kserve-success6g NAME URL LATESTCREATED LATESTREADY READY REASON mlflow-success6g-predictor http://mlflow-success6g-predictor.mlflow-kserve-success6g.svc.cluster.local mlflow-success6g-predictor-00001 mlflow-success6g-predictor-00001 False RevisionMissing # RevisionMissing Error pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get revision $(kubectl get configuration mlflow-success6g-predictor -n mlflow-kserve-success6g --output jsonpath=\"{.status.latestCreatedRevisionName}\") Error from server (NotFound): revisions.serving.knative.dev \"mlflow-success6g-predictor-00001\" not found pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get pod -l serving.kserve.io/inferenceservice=mlflow-success6g -n mlflow-kserve-success6g NAME READY STATUS RESTARTS AGE mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 0/2 CrashLoopBackOff 8 (4m51s ago) 43m pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get pod -l serving.kserve.io/inferenceservice=mlflow-success6g -n mlflow-kserve-success6g NAME READY STATUS RESTARTS AGE mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 2/2 Running 9 (7m7s ago) 45m pmulinka@saiacheron:~/kubernetes/kserve$ kubectl logs -l model=mlflow-success6g -n mlflow-kserve-success6g -c storage-initializer No resources found in mlflow-kserve-success6g namespace. pmulinka@saiacheron:~/kubernetes/kserve$ kubectl delete inferenceservice mlflow-success6g -n mlflow-kserve-success6g inferenceservice.serving.kserve.io \"mlflow-success6g\" deleted ``` Appendix \u00b6 output from making docker ``` (python310venv) pmulinka@iesc-gpu3:~/scripts$ mlflow models build-docker -m s3://mlflow/1/777cf64c922149a4b77c85987865deb0/artifacts/success6g_model -n 5uperpalo/mlflow-success6g --enable-mlserver 2024/05/16 22:20:25 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function' 2024/05/16 22:20:25 INFO mlflow.pyfunc.backend: Building docker image with name 5uperpalo/mlflow-success6g #0 building with \"default\" instance using docker driver #1 [internal] load build definition from Dockerfile #1 transferring dockerfile: 2.53kB done #1 DONE 0.1s #2 [internal] load metadata for docker.io/library/ubuntu:20.04 #2 DONE 1.6s #3 [internal] load .dockerignore #3 transferring context: 2B done #3 DONE 0.0s #4 [ 1/17] FROM docker.io/library/ubuntu:20.04@sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b #4 resolve docker.io/library/ubuntu:20.04@sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b 0.0s done #4 ... #5 [internal] load build context #5 transferring context: 197.37kB done #5 DONE 0.1s #4 [ 1/17] FROM docker.io/library/ubuntu:20.04@sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b #4 sha256:2abc4dfd83182546da40dfae3e391db0810ad4a130fb5a887c6ceb3df8e1d310 2.29kB / 2.29kB done #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 0B / 27.51MB 0.2s #4 sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b 1.13kB / 1.13kB done #4 sha256:cc9cc8169c9517ae035cf293b15f06922cb8c6c864d625a72b7b18667f264b70 424B / 424B done #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 3.15MB / 27.51MB 0.4s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 8.39MB / 27.51MB 0.7s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 11.53MB / 27.51MB 0.9s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 16.78MB / 27.51MB 1.2s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 22.02MB / 27.51MB 1.5s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 25.17MB / 27.51MB 1.7s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 27.26MB / 27.51MB 1.8s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 27.51MB / 27.51MB 1.9s done #4 extracting sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 #4 extracting sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 1.8s done #4 DONE 4.0s #6 [ 2/17] RUN apt-get -y update && DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get install -y --no-install-recommends wget curl nginx ca-certificates bzip2 build-essential cmake git-core #6 0.681 Get:1 http://security.ubuntu.com/ubuntu focal-security InRelease [114 kB] #6 0.682 Get:2 http://archive.ubuntu.com/ubuntu focal InRelease [265 kB] #6 1.324 Get:3 http://archive.ubuntu.com/ubuntu focal-updates InRelease [114 kB] #6 1.432 Get:4 http://security.ubuntu.com/ubuntu focal-security/universe amd64 Packages [1205 kB] #6 1.476 Get:5 http://archive.ubuntu.com/ubuntu focal-backports InRelease [108 kB] #6 1.690 Get:6 http://archive.ubuntu.com/ubuntu focal/restricted amd64 Packages [33.4 kB] #6 1.838 Get:7 http://archive.ubuntu.com/ubuntu focal/multiverse amd64 Packages [177 kB] #6 1.897 Get:8 http://archive.ubuntu.com/ubuntu focal/main amd64 Packages [1275 kB] #6 2.177 Get:9 http://security.ubuntu.com/ubuntu focal-security/restricted amd64 Packages [3608 kB] #6 2.537 Get:10 http://archive.ubuntu.com/ubuntu focal/universe amd64 Packages [11.3 MB] #6 3.807 Get:11 http://security.ubuntu.com/ubuntu focal-security/main amd64 Packages [3669 kB] #6 5.468 Get:12 http://security.ubuntu.com/ubuntu focal-security/multiverse amd64 Packages [29.8 kB] #6 7.649 Get:13 http://archive.ubuntu.com/ubuntu focal-updates/restricted amd64 Packages [3758 kB] #6 9.304 Get:14 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 Packages [1502 kB] #6 9.973 Get:15 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 Packages [4143 kB] #6 12.14 Get:16 http://archive.ubuntu.com/ubuntu focal-updates/multiverse amd64 Packages [32.5 kB] #6 12.15 Get:17 http://archive.ubuntu.com/ubuntu focal-backports/main amd64 Packages [55.2 kB] #6 12.18 Get:18 http://archive.ubuntu.com/ubuntu focal-backports/universe amd64 Packages [28.6 kB] #6 12.44 Fetched 31.5 MB in 12s (2621 kB/s) #6 12.44 Reading package lists... #6 13.60 Reading package lists... #6 14.67 Building dependency tree... #6 14.83 Reading state information... #6 15.04 bzip2 is already the newest version (1.0.8-2). #6 15.04 The following additional packages will be installed: #6 15.04 binutils binutils-common binutils-x86-64-linux-gnu cmake-data cpp cpp-9 #6 15.04 dpkg-dev fontconfig-config fonts-dejavu-core g++ g++-9 gcc gcc-9 gcc-9-base #6 15.04 git-man iproute2 libarchive13 libasan5 libasn1-8-heimdal libatomic1 #6 15.04 libbinutils libbrotli1 libbsd0 libc-dev-bin libc6-dev libcap2 libcap2-bin #6 15.04 libcc1-0 libcrypt-dev libctf-nobfd0 libctf0 libcurl3-gnutls libcurl4 #6 15.04 libdpkg-perl libelf1 liberror-perl libexpat1 libfontconfig1 libfreetype6 #6 15.04 libgcc-9-dev libgd3 libgdbm-compat4 libgdbm6 libgomp1 libgssapi-krb5-2 #6 15.04 libgssapi3-heimdal libhcrypto4-heimdal libheimbase1-heimdal #6 15.04 libheimntlm0-heimdal libhx509-5-heimdal libicu66 libisl22 libitm1 libjbig0 #6 15.04 libjpeg-turbo8 libjpeg8 libjsoncpp1 libk5crypto3 libkeyutils1 #6 15.04 libkrb5-26-heimdal libkrb5-3 libkrb5support0 libldap-2.4-2 libldap-common #6 15.04 liblsan0 libmnl0 libmpc3 libmpfr6 libnghttp2-14 #6 15.04 libnginx-mod-http-image-filter libnginx-mod-http-xslt-filter #6 15.04 libnginx-mod-mail libnginx-mod-stream libperl5.30 libpng16-16 libpsl5 #6 15.04 libquadmath0 librhash0 libroken18-heimdal librtmp1 libsasl2-2 #6 15.04 libsasl2-modules-db libsqlite3-0 libssh-4 libssl1.1 libstdc++-9-dev libtiff5 #6 15.04 libtsan0 libubsan1 libuv1 libwebp6 libwind0-heimdal libx11-6 libx11-data #6 15.04 libxau6 libxcb1 libxdmcp6 libxml2 libxpm4 libxslt1.1 libxtables12 #6 15.04 linux-libc-dev make nginx-common nginx-core openssl patch perl #6 15.04 perl-modules-5.30 tzdata ucf xz-utils #6 15.04 Suggested packages: #6 15.04 binutils-doc cmake-doc ninja-build cpp-doc gcc-9-locales debian-keyring #6 15.04 g++-multilib g++-9-multilib gcc-9-doc gcc-multilib manpages-dev autoconf #6 15.04 automake libtool flex bison gdb gcc-doc gcc-9-multilib gettext-base #6 15.04 git-daemon-run | git-daemon-sysvinit git-doc git-el git-email git-gui gitk #6 15.04 gitweb git-cvs git-mediawiki git-svn iproute2-doc lrzip glibc-doc gnupg #6 15.04 | gnupg2 bzr libgd-tools gdbm-l10n krb5-doc krb5-user libstdc++-9-doc #6 15.04 make-doc fcgiwrap nginx-doc ssl-cert ed diffutils-doc perl-doc #6 15.04 libterm-readline-gnu-perl | libterm-readline-perl-perl libb-debug-perl #6 15.04 liblocale-codes-perl #6 15.04 Recommended packages: #6 15.04 fakeroot gnupg | gnupg2 libalgorithm-merge-perl less ssh-client libatm1 #6 15.04 manpages manpages-dev libpam-cap libfile-fcntllock-perl #6 15.04 liblocale-gettext-perl krb5-locales publicsuffix libsasl2-modules netbase #6 15.30 The following NEW packages will be installed: #6 15.30 binutils binutils-common binutils-x86-64-linux-gnu build-essential #6 15.30 ca-certificates cmake cmake-data cpp cpp-9 curl dpkg-dev fontconfig-config #6 15.30 fonts-dejavu-core g++ g++-9 gcc gcc-9 gcc-9-base git git-man iproute2 #6 15.30 libarchive13 libasan5 libasn1-8-heimdal libatomic1 libbinutils libbrotli1 #6 15.30 libbsd0 libc-dev-bin libc6-dev libcap2 libcap2-bin libcc1-0 libcrypt-dev #6 15.30 libctf-nobfd0 libctf0 libcurl3-gnutls libcurl4 libdpkg-perl libelf1 #6 15.30 liberror-perl libexpat1 libfontconfig1 libfreetype6 libgcc-9-dev libgd3 #6 15.30 libgdbm-compat4 libgdbm6 libgomp1 libgssapi-krb5-2 libgssapi3-heimdal #6 15.30 libhcrypto4-heimdal libheimbase1-heimdal libheimntlm0-heimdal #6 15.30 libhx509-5-heimdal libicu66 libisl22 libitm1 libjbig0 libjpeg-turbo8 #6 15.30 libjpeg8 libjsoncpp1 libk5crypto3 libkeyutils1 libkrb5-26-heimdal libkrb5-3 #6 15.30 libkrb5support0 libldap-2.4-2 libldap-common liblsan0 libmnl0 libmpc3 #6 15.30 libmpfr6 libnghttp2-14 libnginx-mod-http-image-filter #6 15.30 libnginx-mod-http-xslt-filter libnginx-mod-mail libnginx-mod-stream #6 15.30 libperl5.30 libpng16-16 libpsl5 libquadmath0 librhash0 libroken18-heimdal #6 15.30 librtmp1 libsasl2-2 libsasl2-modules-db libsqlite3-0 libssh-4 libssl1.1 #6 15.30 libstdc++-9-dev libtiff5 libtsan0 libubsan1 libuv1 libwebp6 libwind0-heimdal #6 15.30 libx11-6 libx11-data libxau6 libxcb1 libxdmcp6 libxml2 libxpm4 libxslt1.1 #6 15.30 libxtables12 linux-libc-dev make nginx nginx-common nginx-core openssl patch #6 15.30 perl perl-modules-5.30 tzdata ucf wget xz-utils #6 15.41 0 upgraded, 119 newly installed, 0 to remove and 0 not upgraded. #6 15.41 Need to get 82.1 MB of archives. #6 15.41 After this operation, 389 MB of additional disk space will be used. #6 15.41 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 perl-modules-5.30 all 5.30.0-9ubuntu0.5 [2739 kB] #6 16.07 Get:2 http://archive.ubuntu.com/ubuntu focal/main amd64 libgdbm6 amd64 1.18.1-5 [27.4 kB] #6 16.07 Get:3 http://archive.ubuntu.com/ubuntu focal/main amd64 libgdbm-compat4 amd64 1.18.1-5 [6244 B] #6 16.07 Get:4 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libperl5.30 amd64 5.30.0-9ubuntu0.5 [3941 kB] #6 16.76 Get:5 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 perl amd64 5.30.0-9ubuntu0.5 [224 kB] #6 16.79 Get:6 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libssl1.1 amd64 1.1.1f-1ubuntu2.22 [1323 kB] #6 17.04 Get:7 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openssl amd64 1.1.1f-1ubuntu2.22 [622 kB] #6 17.15 Get:8 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 ca-certificates all 20230311ubuntu0.20.04.1 [152 kB] #6 17.18 Get:9 http://archive.ubuntu.com/ubuntu focal/main amd64 libbsd0 amd64 0.10.0-1 [45.4 kB] #6 17.20 Get:10 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcap2 amd64 1:2.32-1ubuntu0.1 [15.8 kB] #6 17.20 Get:11 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libelf1 amd64 0.176-1.1ubuntu0.1 [44.2 kB] #6 17.33 Get:12 http://archive.ubuntu.com/ubuntu focal/main amd64 libmnl0 amd64 1.0.4-2 [12.3 kB] #6 17.38 Get:13 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxtables12 amd64 1.8.4-3ubuntu2.1 [28.7 kB] #6 17.42 Get:14 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcap2-bin amd64 1:2.32-1ubuntu0.1 [26.2 kB] #6 17.46 Get:15 http://archive.ubuntu.com/ubuntu focal/main amd64 iproute2 amd64 5.5.0-1ubuntu1 [858 kB] #6 20.61 Get:16 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libexpat1 amd64 2.2.9-1ubuntu0.6 [74.6 kB] #6 21.00 Get:17 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 tzdata all 2024a-0ubuntu0.20.04 [301 kB] #6 22.38 Get:18 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libicu66 amd64 66.1-2ubuntu2.1 [8515 kB] #6 51.24 Get:19 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsqlite3-0 amd64 3.31.1-4ubuntu0.6 [549 kB] #6 53.10 Get:20 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxml2 amd64 2.9.10+dfsg-5ubuntu0.20.04.7 [640 kB] #6 55.32 Get:21 http://archive.ubuntu.com/ubuntu focal/main amd64 ucf all 3.0038+nmu1 [51.6 kB] #6 55.50 Get:21 http://archive.ubuntu.com/ubuntu focal/main amd64 ucf all 3.0038+nmu1 [51.6 kB] #6 55.60 Get:22 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 xz-utils amd64 5.2.4-1ubuntu1.1 [82.6 kB] #6 55.66 Get:23 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkrb5support0 amd64 1.17-6ubuntu4.4 [31.0 kB] #6 55.70 Get:24 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libk5crypto3 amd64 1.17-6ubuntu4.4 [79.9 kB] #6 55.72 Get:25 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkeyutils1 amd64 1.6-6ubuntu1.1 [10.3 kB] #6 55.73 Get:26 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkrb5-3 amd64 1.17-6ubuntu4.4 [330 kB] #6 55.80 Get:27 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgssapi-krb5-2 amd64 1.17-6ubuntu4.4 [121 kB] #6 55.82 Get:28 http://archive.ubuntu.com/ubuntu focal/main amd64 libpng16-16 amd64 1.6.37-2 [179 kB] #6 55.86 Get:29 http://archive.ubuntu.com/ubuntu focal/main amd64 libpsl5 amd64 0.21.0-1ubuntu1 [51.5 kB] #6 55.86 Get:30 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56.46 Get:39 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libctf-nobfd0 amd64 2.34-6ubuntu1.9 [48.2 kB] #6 56.47 Get:40 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libctf0 amd64 2.34-6ubuntu1.9 [46.6 kB] #6 56.48 Get:41 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 binutils-x86-64-linux-gnu amd64 2.34-6ubuntu1.9 [1614 kB] #6 56.76 Get:42 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 binutils amd64 2.34-6ubuntu1.9 [3380 B] #6 56.85 Get:43 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libc-dev-bin amd64 2.31-0ubuntu9.15 [71.8 kB] #6 57.00 Get:44 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 linux-libc-dev amd64 5.4.0-182.202 [1134 kB] #6 57.28 Get:45 http://archive.ubuntu.com/ubuntu focal/main amd64 libcrypt-dev amd64 1:4.4.10-10ubuntu4 [104 kB] #6 57.30 Get:46 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libc6-dev amd64 2.31-0ubuntu9.15 [2519 kB] #6 57.75 Get:47 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 gcc-9-base amd64 9.4.0-1ubuntu1~20.04.2 [18.9 kB] #6 57.75 Get:48 http://archive.ubuntu.com/ubuntu focal/main amd64 libisl22 amd64 0.22.1-1 [592 kB] #6 57.87 Get:49 http://archive.ubuntu.com/ubuntu focal/main amd64 libmpfr6 amd64 4.0.2-1 [240 kB] #6 57.91 Get:50 http://archive.ubuntu.com/ubuntu focal/main amd64 libmpc3 amd64 1.1.0-1 [40.8 kB] #6 57.92 Get:51 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 cpp-9 amd64 9.4.0-1ubuntu1~20.04.2 [7502 kB] #6 59.27 Get:52 http://archive.ubuntu.com/ubuntu focal/main amd64 cpp amd64 4:9.3.0-1ubuntu2 [27.6 kB] #6 59.27 Get:53 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcc1-0 amd64 10.5.0-1ubuntu1~20.04 [48.8 kB] #6 59.36 Get:54 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgomp1 amd64 10.5.0-1ubuntu1~20.04 [102 kB] #6 59.54 Get:55 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libitm1 amd64 10.5.0-1ubuntu1~20.04 [26.2 kB] #6 59.55 Get:56 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http://archive.ubuntu.com/ubuntu focal/main amd64 gcc amd64 4:9.3.0-1ubuntu2 [5208 B] #6 62.91 Get:65 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libstdc++-9-dev amd64 9.4.0-1ubuntu1~20.04.2 [1722 kB] #6 63.39 Get:66 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 g++-9 amd64 9.4.0-1ubuntu1~20.04.2 [8421 kB] #6 65.04 Get:67 http://archive.ubuntu.com/ubuntu focal/main amd64 g++ amd64 4:9.3.0-1ubuntu2 [1604 B] #6 65.06 Get:68 http://archive.ubuntu.com/ubuntu focal/main amd64 make amd64 4.2.1-1.2 [162 kB] #6 65.07 Get:69 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdpkg-perl all 1.19.7ubuntu3.2 [231 kB] #6 65.16 Get:70 http://archive.ubuntu.com/ubuntu focal/main amd64 patch amd64 2.7.6-6 [105 kB] #6 65.20 Get:71 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 dpkg-dev all 1.19.7ubuntu3.2 [679 kB] #6 65.32 Get:72 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 build-essential amd64 12.8ubuntu1.1 [4664 B] #6 65.32 Get:73 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66.71 Get:81 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libhx509-5-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [107 kB] #6 67.13 Get:82 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkrb5-26-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [207 kB] #6 67.89 Get:83 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libheimntlm0-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [15.1 kB] #6 67.92 Get:84 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgssapi3-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [96.5 kB] #6 68.05 Get:85 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsasl2-modules-db amd64 2.1.27+dfsg-2ubuntu0.1 [14.7 kB] #6 68.06 Get:86 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsasl2-2 amd64 2.1.27+dfsg-2ubuntu0.1 [49.3 kB] #6 68.29 Get:87 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libldap-common all 2.4.49+dfsg-2ubuntu1.10 [16.5 kB] #6 68.38 Get:88 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libldap-2.4-2 amd64 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1.18.0-0ubuntu1.4 [14.8 kB] #6 71.41 Get:114 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxslt1.1 amd64 1.1.34-4ubuntu0.20.04.1 [151 kB] #6 71.43 Get:115 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnginx-mod-http-xslt-filter amd64 1.18.0-0ubuntu1.4 [13.0 kB] #6 71.44 Get:116 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnginx-mod-mail amd64 1.18.0-0ubuntu1.4 [42.9 kB] #6 71.47 Get:117 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnginx-mod-stream amd64 1.18.0-0ubuntu1.4 [67.4 kB] #6 71.49 Get:118 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 nginx-core amd64 1.18.0-0ubuntu1.4 [425 kB] #6 71.58 Get:119 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 nginx all 1.18.0-0ubuntu1.4 [3620 B] #6 71.74 debconf: delaying package configuration, since apt-utils is not installed #6 71.80 Fetched 82.1 MB in 56s (1459 kB/s) #6 71.85 Selecting previously unselected package perl-modules-5.30. #6 71.85 (Reading database 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75.93 Preparing to unpack .../022-libkrb5support0_1.17-6ubuntu4.4_amd64.deb ... #6 75.93 Unpacking libkrb5support0:amd64 (1.17-6ubuntu4.4) ... #6 76.00 Selecting previously unselected package libk5crypto3:amd64. #6 76.01 Preparing to unpack .../023-libk5crypto3_1.17-6ubuntu4.4_amd64.deb ... #6 76.01 Unpacking libk5crypto3:amd64 (1.17-6ubuntu4.4) ... #6 76.10 Selecting previously unselected package libkeyutils1:amd64. #6 76.10 Preparing to unpack .../024-libkeyutils1_1.6-6ubuntu1.1_amd64.deb ... #6 76.11 Unpacking libkeyutils1:amd64 (1.6-6ubuntu1.1) ... #6 76.17 Selecting previously unselected package libkrb5-3:amd64. #6 76.18 Preparing to unpack .../025-libkrb5-3_1.17-6ubuntu4.4_amd64.deb ... #6 76.18 Unpacking libkrb5-3:amd64 (1.17-6ubuntu4.4) ... #6 76.31 Selecting previously unselected package libgssapi-krb5-2:amd64. #6 76.31 Preparing to unpack .../026-libgssapi-krb5-2_1.17-6ubuntu4.4_amd64.deb ... #6 76.32 Unpacking libgssapi-krb5-2:amd64 (1.17-6ubuntu4.4) ... #6 76.41 Selecting previously unselected package libpng16-16:amd64. #6 76.41 Preparing to unpack .../027-libpng16-16_1.6.37-2_amd64.deb ... #6 76.42 Unpacking libpng16-16:amd64 (1.6.37-2) ... #6 76.52 Selecting previously unselected package libpsl5:amd64. #6 76.52 Preparing to unpack .../028-libpsl5_0.21.0-1ubuntu1_amd64.deb ... #6 76.53 Unpacking libpsl5:amd64 (0.21.0-1ubuntu1) ... #6 76.61 Selecting previously unselected package libuv1:amd64. #6 76.61 Preparing to unpack .../029-libuv1_1.34.2-1ubuntu1.5_amd64.deb ... #6 76.62 Unpacking libuv1:amd64 (1.34.2-1ubuntu1.5) ... #6 76.70 Selecting previously unselected package libxau6:amd64. #6 76.71 Preparing to unpack .../030-libxau6_1%3a1.0.9-0ubuntu1_amd64.deb ... #6 76.71 Unpacking libxau6:amd64 (1:1.0.9-0ubuntu1) ... #6 76.78 Selecting previously unselected package libxdmcp6:amd64. #6 76.79 Preparing to unpack .../031-libxdmcp6_1%3a1.1.3-0ubuntu1_amd64.deb ... #6 76.79 Unpacking libxdmcp6:amd64 (1:1.1.3-0ubuntu1) ... #6 76.86 Selecting previously unselected package libxcb1:amd64. #6 76.87 Preparing to unpack .../032-libxcb1_1.14-2_amd64.deb ... #6 76.87 Unpacking libxcb1:amd64 (1.14-2) ... #6 76.95 Selecting previously unselected package libx11-data. #6 76.95 Preparing to unpack .../033-libx11-data_2%3a1.6.9-2ubuntu1.6_all.deb ... #6 76.96 Unpacking libx11-data (2:1.6.9-2ubuntu1.6) ... #6 77.08 Selecting previously unselected package libx11-6:amd64. #6 77.08 Preparing to unpack .../034-libx11-6_2%3a1.6.9-2ubuntu1.6_amd64.deb ... #6 77.09 Unpacking libx11-6:amd64 (2:1.6.9-2ubuntu1.6) ... #6 77.22 Selecting previously unselected package wget. #6 77.22 Preparing to unpack .../035-wget_1.20.3-1ubuntu2_amd64.deb ... #6 77.23 Unpacking wget (1.20.3-1ubuntu2) ... #6 77.33 Selecting previously unselected package binutils-common:amd64. #6 77.33 Preparing to unpack .../036-binutils-common_2.34-6ubuntu1.9_amd64.deb ... #6 77.34 Unpacking binutils-common:amd64 (2.34-6ubuntu1.9) ... #6 77.44 Selecting previously unselected package 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Selecting previously unselected package libc-dev-bin. #6 78.12 Preparing to unpack .../042-libc-dev-bin_2.31-0ubuntu9.15_amd64.deb ... #6 78.13 Unpacking libc-dev-bin (2.31-0ubuntu9.15) ... #6 78.20 Selecting previously unselected package linux-libc-dev:amd64. #6 78.21 Preparing to unpack .../043-linux-libc-dev_5.4.0-182.202_amd64.deb ... #6 78.21 Unpacking linux-libc-dev:amd64 (5.4.0-182.202) ... #6 78.41 Selecting previously unselected package libcrypt-dev:amd64. #6 78.42 Preparing to unpack .../044-libcrypt-dev_1%3a4.4.10-10ubuntu4_amd64.deb ... #6 78.42 Unpacking libcrypt-dev:amd64 (1:4.4.10-10ubuntu4) ... #6 78.50 Selecting previously unselected package libc6-dev:amd64. #6 78.51 Preparing to unpack .../045-libc6-dev_2.31-0ubuntu9.15_amd64.deb ... #6 78.51 Unpacking libc6-dev:amd64 (2.31-0ubuntu9.15) ... #6 78.88 Selecting previously unselected package gcc-9-base:amd64. #6 78.88 Preparing to unpack .../046-gcc-9-base_9.4.0-1ubuntu1~20.04.2_amd64.deb ... #6 78.89 Unpacking gcc-9-base:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 78.97 Selecting previously unselected package libisl22:amd64. #6 78.97 Preparing to unpack .../047-libisl22_0.22.1-1_amd64.deb ... #6 78.98 Unpacking libisl22:amd64 (0.22.1-1) ... #6 79.12 Selecting previously unselected package libmpfr6:amd64. #6 79.12 Preparing to unpack .../048-libmpfr6_4.0.2-1_amd64.deb ... #6 79.13 Unpacking libmpfr6:amd64 (4.0.2-1) ... #6 79.24 Selecting previously unselected package libmpc3:amd64. #6 79.24 Preparing to unpack .../049-libmpc3_1.1.0-1_amd64.deb ... #6 79.25 Unpacking libmpc3:amd64 (1.1.0-1) ... #6 79.32 Selecting previously unselected package cpp-9. #6 79.32 Preparing to unpack .../050-cpp-9_9.4.0-1ubuntu1~20.04.2_amd64.deb ... #6 79.33 Unpacking cpp-9 (9.4.0-1ubuntu1~20.04.2) ... #6 80.06 Selecting previously unselected package cpp. #6 80.06 Preparing to unpack .../051-cpp_4%3a9.3.0-1ubuntu2_amd64.deb ... #6 80.07 Unpacking cpp (4:9.3.0-1ubuntu2) ... #6 80.14 Selecting previously unselected package libcc1-0:amd64. #6 80.14 Preparing to unpack .../052-libcc1-0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.15 Unpacking libcc1-0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.23 Selecting previously unselected package libgomp1:amd64. #6 80.23 Preparing to unpack .../053-libgomp1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.24 Unpacking libgomp1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.34 Selecting previously unselected package libitm1:amd64. #6 80.34 Preparing to unpack .../054-libitm1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.35 Unpacking libitm1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.43 Selecting previously unselected package libatomic1:amd64. #6 80.43 Preparing to unpack .../055-libatomic1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.44 Unpacking libatomic1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.51 Selecting previously unselected package libasan5:amd64. #6 80.51 Preparing to unpack .../056-libasan5_9.4.0-1ubuntu1~20.04.2_amd64.deb ... #6 80.52 Unpacking libasan5:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 80.87 Selecting previously unselected package liblsan0:amd64. #6 80.88 Preparing to unpack .../057-liblsan0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.88 Unpacking liblsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.05 Selecting previously unselected package libtsan0:amd64. #6 81.05 Preparing to unpack .../058-libtsan0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 81.06 Unpacking libtsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.35 Selecting previously unselected package libubsan1:amd64. #6 81.35 Preparing to unpack .../059-libubsan1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 81.36 Unpacking libubsan1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.52 Selecting previously unselected package libquadmath0:amd64. #6 81.52 Preparing to unpack .../060-libquadmath0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 81.53 Unpacking libquadmath0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.62 Selecting previously unselected package libgcc-9-dev:amd64. #6 81.62 Preparing to unpack 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Selecting previously unselected package cmake-data. #6 84.52 Preparing to unpack .../072-cmake-data_3.16.3-1ubuntu1.20.04.1_all.deb ... #6 84.54 Unpacking cmake-data (3.16.3-1ubuntu1.20.04.1) ... #6 84.99 Selecting previously unselected package libarchive13:amd64. #6 84.99 Preparing to unpack .../073-libarchive13_3.4.0-2ubuntu1.2_amd64.deb ... #6 85.00 Unpacking libarchive13:amd64 (3.4.0-2ubuntu1.2) ... #6 85.12 Selecting previously unselected package libbrotli1:amd64. #6 85.12 Preparing to unpack .../074-libbrotli1_1.0.7-6ubuntu0.1_amd64.deb ... #6 85.13 Unpacking libbrotli1:amd64 (1.0.7-6ubuntu0.1) ... #6 85.24 Selecting previously unselected package libroken18-heimdal:amd64. #6 85.25 Preparing to unpack .../075-libroken18-heimdal_7.7.0+dfsg-1ubuntu1.4_amd64.deb ... #6 85.25 Unpacking libroken18-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 85.33 Selecting previously unselected package libasn1-8-heimdal:amd64. #6 85.33 Preparing to unpack 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Unpacking libcurl3-gnutls:amd64 (7.68.0-1ubuntu2.22) ... #6 88.11 Selecting previously unselected package liberror-perl. #6 88.12 Preparing to unpack .../099-liberror-perl_0.17029-1_all.deb ... #6 88.12 Unpacking liberror-perl (0.17029-1) ... #6 88.19 Selecting previously unselected package git-man. #6 88.19 Preparing to unpack .../100-git-man_1%3a2.25.1-1ubuntu3.11_all.deb ... #6 88.20 Unpacking git-man (1:2.25.1-1ubuntu3.11) ... #6 88.37 Selecting previously unselected package git. #6 88.38 Preparing to unpack .../101-git_1%3a2.25.1-1ubuntu3.11_amd64.deb ... #6 88.43 Unpacking git (1:2.25.1-1ubuntu3.11) ... #6 88.98 Selecting previously unselected package libfreetype6:amd64. #6 88.99 Preparing to unpack .../102-libfreetype6_2.10.1-2ubuntu0.3_amd64.deb ... #6 89.00 Unpacking libfreetype6:amd64 (2.10.1-2ubuntu0.3) ... #6 89.12 Selecting previously unselected package libfontconfig1:amd64. #6 89.12 Preparing to unpack .../103-libfontconfig1_2.13.1-2ubuntu3_amd64.deb ... #6 89.13 Unpacking libfontconfig1:amd64 (2.13.1-2ubuntu3) ... #6 89.23 Selecting previously unselected package libjpeg-turbo8:amd64. #6 89.23 Preparing to unpack .../104-libjpeg-turbo8_2.0.3-0ubuntu1.20.04.3_amd64.deb ... #6 89.24 Unpacking libjpeg-turbo8:amd64 (2.0.3-0ubuntu1.20.04.3) ... #6 89.32 Selecting previously unselected package libjpeg8:amd64. #6 89.33 Preparing to unpack .../105-libjpeg8_8c-2ubuntu8_amd64.deb ... #6 89.33 Unpacking libjpeg8:amd64 (8c-2ubuntu8) ... #6 89.40 Selecting previously unselected package libjbig0:amd64. #6 89.40 Preparing to unpack .../106-libjbig0_2.1-3.1ubuntu0.20.04.1_amd64.deb ... #6 89.41 Unpacking libjbig0:amd64 (2.1-3.1ubuntu0.20.04.1) ... #6 89.49 Selecting previously unselected package libwebp6:amd64. #6 89.49 Preparing to unpack .../107-libwebp6_0.6.1-2ubuntu0.20.04.3_amd64.deb ... #6 89.50 Unpacking libwebp6:amd64 (0.6.1-2ubuntu0.20.04.3) ... #6 89.60 Selecting previously unselected package libtiff5:amd64. #6 89.60 Preparing to unpack .../108-libtiff5_4.1.0+git191117-2ubuntu0.20.04.12_amd64.deb ... #6 89.61 Unpacking libtiff5:amd64 (4.1.0+git191117-2ubuntu0.20.04.12) ... #6 89.71 Selecting previously unselected package libxpm4:amd64. #6 89.71 Preparing to unpack .../109-libxpm4_1%3a3.5.12-1ubuntu0.20.04.2_amd64.deb ... #6 89.72 Unpacking libxpm4:amd64 (1:3.5.12-1ubuntu0.20.04.2) ... #6 89.80 Selecting previously unselected package libgd3:amd64. #6 89.80 Preparing to unpack .../110-libgd3_2.2.5-5.2ubuntu2.1_amd64.deb ... #6 89.81 Unpacking libgd3:amd64 (2.2.5-5.2ubuntu2.1) ... #6 89.92 Selecting previously unselected package nginx-common. #6 89.92 Preparing to unpack .../111-nginx-common_1.18.0-0ubuntu1.4_all.deb ... #6 89.99 Unpacking nginx-common (1.18.0-0ubuntu1.4) ... #6 90.08 Selecting previously unselected package libnginx-mod-http-image-filter. #6 90.08 Preparing to unpack .../112-libnginx-mod-http-image-filter_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.09 Unpacking libnginx-mod-http-image-filter (1.18.0-0ubuntu1.4) ... #6 90.17 Selecting previously unselected package libxslt1.1:amd64. #6 90.17 Preparing to unpack .../113-libxslt1.1_1.1.34-4ubuntu0.20.04.1_amd64.deb ... #6 90.18 Unpacking libxslt1.1:amd64 (1.1.34-4ubuntu0.20.04.1) ... #6 90.28 Selecting previously unselected package libnginx-mod-http-xslt-filter. #6 90.28 Preparing to unpack .../114-libnginx-mod-http-xslt-filter_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.29 Unpacking libnginx-mod-http-xslt-filter (1.18.0-0ubuntu1.4) ... #6 90.37 Selecting previously unselected package libnginx-mod-mail. #6 90.37 Preparing to unpack .../115-libnginx-mod-mail_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.38 Unpacking libnginx-mod-mail (1.18.0-0ubuntu1.4) ... #6 90.46 Selecting previously unselected package libnginx-mod-stream. #6 90.46 Preparing to unpack .../116-libnginx-mod-stream_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.47 Unpacking libnginx-mod-stream (1.18.0-0ubuntu1.4) ... #6 90.56 Selecting previously unselected package nginx-core. #6 90.56 Preparing to unpack .../117-nginx-core_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.57 Unpacking nginx-core (1.18.0-0ubuntu1.4) ... #6 90.69 Selecting previously unselected package nginx. #6 90.70 Preparing to unpack .../118-nginx_1.18.0-0ubuntu1.4_all.deb ... #6 90.71 Unpacking nginx (1.18.0-0ubuntu1.4) ... #6 90.79 Setting up libexpat1:amd64 (2.2.9-1ubuntu0.6) ... #6 90.81 Setting up libxau6:amd64 (1:1.0.9-0ubuntu1) ... #6 90.84 Setting up libkeyutils1:amd64 (1.6-6ubuntu1.1) ... #6 90.86 Setting up libpsl5:amd64 (0.21.0-1ubuntu1) ... #6 90.88 Setting up perl-modules-5.30 (5.30.0-9ubuntu0.5) ... #6 90.90 Setting up libssl1.1:amd64 (1.1.1f-1ubuntu2.22) ... #6 91.02 Setting up libbrotli1:amd64 (1.0.7-6ubuntu0.1) ... #6 91.04 Setting up libsqlite3-0:amd64 (3.31.1-4ubuntu0.6) ... #6 91.06 Setting up binutils-common:amd64 (2.34-6ubuntu1.9) ... #6 91.08 Setting up libnghttp2-14:amd64 (1.40.0-1ubuntu0.3) ... #6 91.11 Setting up linux-libc-dev:amd64 (5.4.0-182.202) ... #6 91.13 Setting up libctf-nobfd0:amd64 (2.34-6ubuntu1.9) ... #6 91.15 Setting up nginx-common (1.18.0-0ubuntu1.4) ... #6 91.69 Setting up libgomp1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 91.71 Setting up libldap-common (2.4.49+dfsg-2ubuntu1.10) ... #6 91.74 Setting up libjbig0:amd64 (2.1-3.1ubuntu0.20.04.1) ... #6 91.76 Setting up libcap2:amd64 (1:2.32-1ubuntu0.1) ... #6 91.79 Setting up libkrb5support0:amd64 (1.17-6ubuntu4.4) ... #6 91.81 Setting up libsasl2-modules-db:amd64 (2.1.27+dfsg-2ubuntu0.1) ... #6 91.83 Setting up tzdata (2024a-0ubuntu0.20.04) ... #6 91.96 #6 91.96 Current default time zone: 'Etc/UTC' #6 91.96 Local time is now: Thu May 16 20:22:03 UTC 2024. #6 91.96 Universal Time is now: Thu May 16 20:22:03 UTC 2024. #6 91.96 Run 'dpkg-reconfigure tzdata' if you wish to change it. #6 91.96 #6 91.99 Setting up libcap2-bin (1:2.32-1ubuntu0.1) ... #6 92.01 Setting up libuv1:amd64 (1.34.2-1ubuntu1.5) ... #6 92.03 Setting up libx11-data (2:1.6.9-2ubuntu1.6) ... #6 92.05 Setting up make (4.2.1-1.2) ... #6 92.08 Setting up libmpfr6:amd64 (4.0.2-1) ... #6 92.10 Setting up librtmp1:amd64 (2.4+20151223.gitfa8646d.1-2build1) ... #6 92.12 Setting up xz-utils (5.2.4-1ubuntu1.1) ... #6 92.14 update-alternatives: using /usr/bin/xz to provide /usr/bin/lzma (lzma) in auto mode #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzma.1.gz because associated file /usr/share/man/man1/xz.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/unlzma.1.gz because associated file /usr/share/man/man1/unxz.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzcat.1.gz because associated file /usr/share/man/man1/xzcat.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzmore.1.gz because associated file /usr/share/man/man1/xzmore.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzless.1.gz because associated file /usr/share/man/man1/xzless.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzdiff.1.gz because associated file /usr/share/man/man1/xzdiff.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzcmp.1.gz because associated file /usr/share/man/man1/xzcmp.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzgrep.1.gz because associated file /usr/share/man/man1/xzgrep.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzegrep.1.gz because associated file /usr/share/man/man1/xzegrep.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzfgrep.1.gz because associated file /usr/share/man/man1/xzfgrep.1.gz (of link group lzma) doesn't exist #6 92.16 Setting up libquadmath0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.18 Setting up libpng16-16:amd64 (1.6.37-2) ... #6 92.20 Setting up libmpc3:amd64 (1.1.0-1) ... #6 92.22 Setting up libmnl0:amd64 (1.0.4-2) ... #6 92.24 Setting up libatomic1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.26 Setting up patch (2.7.6-6) ... #6 92.28 Setting up libwebp6:amd64 (0.6.1-2ubuntu0.20.04.3) ... #6 92.30 Setting up fonts-dejavu-core (2.37-1) ... #6 92.40 Setting up ucf (3.0038+nmu1) ... #6 92.53 Setting up libk5crypto3:amd64 (1.17-6ubuntu4.4) ... #6 92.55 Setting up libjpeg-turbo8:amd64 (2.0.3-0ubuntu1.20.04.3) ... #6 92.57 Setting up libxtables12:amd64 (1.8.4-3ubuntu2.1) ... #6 92.59 Setting up libsasl2-2:amd64 (2.1.27+dfsg-2ubuntu0.1) ... #6 92.61 Setting up libroken18-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 92.64 Setting up libubsan1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.66 Setting up librhash0:amd64 (1.3.9-1) ... #6 92.68 Setting up libcrypt-dev:amd64 (1:4.4.10-10ubuntu4) ... #6 92.70 Setting up git-man (1:2.25.1-1ubuntu3.11) ... #6 92.72 Setting up libisl22:amd64 (0.22.1-1) ... #6 92.74 Setting up cmake-data (3.16.3-1ubuntu1.20.04.1) ... #6 92.77 Setting up libkrb5-3:amd64 (1.17-6ubuntu4.4) ... #6 92.79 Setting up libbinutils:amd64 (2.34-6ubuntu1.9) ... #6 92.81 Setting up libc-dev-bin (2.31-0ubuntu9.15) ... #6 92.83 Setting up openssl (1.1.1f-1ubuntu2.22) ... #6 92.86 Setting up libbsd0:amd64 (0.10.0-1) ... #6 92.88 Setting up libelf1:amd64 (0.176-1.1ubuntu0.1) ... #6 92.91 Setting up libcc1-0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.93 Setting up liblsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.95 Setting up libitm1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.97 Setting up libjsoncpp1:amd64 (1.7.4-3.1ubuntu2) ... #6 92.99 Setting up libheimbase1-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 93.01 Setting up libgdbm6:amd64 (1.18.1-5) ... #6 93.03 Setting up gcc-9-base:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 93.05 Setting up libtsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 93.07 Setting up libctf0:amd64 (2.34-6ubuntu1.9) ... #6 93.09 Setting up libjpeg8:amd64 (8c-2ubuntu8) ... #6 93.11 Setting up libnginx-mod-mail (1.18.0-0ubuntu1.4) ... #6 93.16 Setting up libxdmcp6:amd64 (1:1.1.3-0ubuntu1) ... #6 93.18 Setting up libxcb1:amd64 (1.14-2) ... #6 93.20 Setting up wget (1.20.3-1ubuntu2) ... #6 93.22 Setting up fontconfig-config (2.13.1-2ubuntu3) ... #6 93.55 Setting up iproute2 (5.5.0-1ubuntu1) ... #6 93.75 Setting up libicu66:amd64 (66.1-2ubuntu2.1) ... #6 93.77 Setting up libasn1-8-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 93.79 Setting up libnginx-mod-stream (1.18.0-0ubuntu1.4) ... #6 93.84 Setting up libhcrypto4-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 93.86 Setting up ca-certificates (20230311ubuntu0.20.04.1) ... #6 95.13 Updating certificates in /etc/ssl/certs... #6 96.88 137 added, 0 removed; done. #6 96.95 Setting up libasan5:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 96.98 Setting up libwind0-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.00 Setting up libfreetype6:amd64 (2.10.1-2ubuntu0.3) ... #6 97.02 Setting up libgssapi-krb5-2:amd64 (1.17-6ubuntu4.4) ... #6 97.04 Setting up libgdbm-compat4:amd64 (1.18.1-5) ... #6 97.06 Setting up libssh-4:amd64 (0.9.3-2ubuntu2.5) ... #6 97.08 Setting up cpp-9 (9.4.0-1ubuntu1~20.04.2) ... #6 97.10 Setting up libperl5.30:amd64 (5.30.0-9ubuntu0.5) ... #6 97.12 Setting up libc6-dev:amd64 (2.31-0ubuntu9.15) ... #6 97.14 Setting up libx11-6:amd64 (2:1.6.9-2ubuntu1.6) ... #6 97.16 Setting up libtiff5:amd64 (4.1.0+git191117-2ubuntu0.20.04.12) ... #6 97.18 Setting up libfontconfig1:amd64 (2.13.1-2ubuntu3) ... #6 97.20 Setting up libxml2:amd64 (2.9.10+dfsg-5ubuntu0.20.04.7) ... #6 97.22 Setting up binutils-x86-64-linux-gnu (2.34-6ubuntu1.9) ... #6 97.24 Setting up libxpm4:amd64 (1:3.5.12-1ubuntu0.20.04.2) ... #6 97.26 Setting up libarchive13:amd64 (3.4.0-2ubuntu1.2) ... #6 97.28 Setting up libhx509-5-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.30 Setting up binutils (2.34-6ubuntu1.9) ... #6 97.32 Setting up libgcc-9-dev:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 97.34 Setting up perl (5.30.0-9ubuntu0.5) ... #6 97.39 Setting up libgd3:amd64 (2.2.5-5.2ubuntu2.1) ... #6 97.41 Setting up libdpkg-perl (1.19.7ubuntu3.2) ... #6 97.43 Setting up cpp (4:9.3.0-1ubuntu2) ... #6 97.46 Setting up libxslt1.1:amd64 (1.1.34-4ubuntu0.20.04.1) ... #6 97.48 Setting up libkrb5-26-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.50 Setting up gcc-9 (9.4.0-1ubuntu1~20.04.2) ... #6 97.52 Setting up libstdc++-9-dev:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 97.54 Setting up libnginx-mod-http-image-filter (1.18.0-0ubuntu1.4) ... #6 97.59 Setting up libheimntlm0-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.61 Setting up gcc (4:9.3.0-1ubuntu2) ... #6 97.65 Setting up dpkg-dev (1.19.7ubuntu3.2) ... #6 97.69 Setting up liberror-perl (0.17029-1) ... #6 97.71 Setting up libnginx-mod-http-xslt-filter (1.18.0-0ubuntu1.4) ... #6 97.75 Setting up libgssapi3-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.77 Setting up g++-9 (9.4.0-1ubuntu1~20.04.2) ... #6 97.79 Setting up g++ (4:9.3.0-1ubuntu2) ... #6 97.85 update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode #6 97.85 update-alternatives: warning: skip creation of /usr/share/man/man1/c++.1.gz because associated file /usr/share/man/man1/g++.1.gz (of link group c++) doesn't exist #6 97.87 Setting up build-essential (12.8ubuntu1.1) ... #6 97.89 Setting up nginx-core (1.18.0-0ubuntu1.4) ... #6 97.98 invoke-rc.d: could not determine current runlevel #6 97.99 invoke-rc.d: policy-rc.d denied execution of start. #6 98.00 Setting up nginx (1.18.0-0ubuntu1.4) ... #6 98.02 Setting up libldap-2.4-2:amd64 (2.4.49+dfsg-2ubuntu1.10) ... #6 98.04 Setting up libcurl3-gnutls:amd64 (7.68.0-1ubuntu2.22) ... #6 98.06 Setting up git (1:2.25.1-1ubuntu3.11) ... #6 98.14 Setting up libcurl4:amd64 (7.68.0-1ubuntu2.22) ... #6 98.16 Setting up curl (7.68.0-1ubuntu2.22) ... #6 98.18 Setting up cmake (3.16.3-1ubuntu1.20.04.1) ... #6 98.20 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #6 98.25 Processing triggers for ca-certificates (20230311ubuntu0.20.04.1) ... #6 98.26 Updating certificates in /etc/ssl/certs... #6 99.63 0 added, 0 removed; done. #6 99.63 Running hooks in /etc/ca-certificates/update.d... #6 99.63 done. #6 DONE 100.6s #7 [ 3/17] RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev #7 0.464 Reading package lists... #7 1.538 Building dependency tree... #7 1.711 Reading state information... #7 1.915 curl is already the newest version (7.68.0-1ubuntu2.22). #7 1.915 tzdata is already the newest version (2024a-0ubuntu0.20.04). #7 1.915 tzdata set to manually installed. #7 1.915 wget is already the newest version (1.20.3-1ubuntu2). #7 1.915 xz-utils is already the newest version (5.2.4-1ubuntu1.1). #7 1.915 xz-utils set to manually installed. #7 1.915 The following additional packages will be installed: #7 1.915 binfmt-support bzip2-doc file icu-devtools libclang-cpp10 libdrm-amdgpu1 #7 1.915 libdrm-common libdrm-intel1 libdrm-nouveau2 libdrm-radeon1 libdrm2 libedit2 #7 1.915 libevent-2.1-7 libexpat1-dev libfontconfig1-dev libfontenc1 libfreetype-dev #7 1.915 libfreetype6-dev libgcrypt20-dev libgl1 libgl1-mesa-dri libglapi-mesa #7 1.915 libglib2.0-0 libglib2.0-data libglvnd0 libglx-mesa0 libglx0 libgmp-dev #7 1.915 libgmpxx4ldbl libgnutls-dane0 libgnutls-openssl27 libgnutls28-dev #7 1.915 libgnutlsxx28 libgpg-error-dev libice-dev libice6 libicu-dev libidn2-dev #7 1.915 libllvm10 libllvm12 libmagic-mgc libmagic1 libmpdec2 libncurses-dev libnspr4 #7 1.915 libnspr4-dev libnss3 libnss3-dev libp11-kit-dev libpciaccess0 libpfm4 #7 1.915 libpipeline1 libpng-dev libpng-tools libpthread-stubs0-dev libpython3-stdlib #7 1.915 libpython3.8-minimal libpython3.8-stdlib libreadline8 libsensors-config #7 1.915 libsensors5 libsm-dev libsm6 libtasn1-6-dev libtasn1-doc libtcl8.6 libtk8.6 #7 1.915 libunbound8 libutempter0 libvulkan1 libwayland-client0 libx11-dev #7 1.915 libx11-xcb1 libxau-dev libxaw7 libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 #7 1.915 libxcb-present0 libxcb-randr0 libxcb-shape0 libxcb-shm0 libxcb-sync1 #7 1.915 libxcb-xfixes0 libxcb1-dev libxcomposite1 libxdmcp-dev libxext-dev libxext6 #7 1.915 libxfixes3 libxft-dev libxft2 libxi6 libxinerama1 libxkbfile1 libxmlsec1 #7 1.915 libxmlsec1-gcrypt libxmlsec1-gnutls libxmlsec1-nss libxmlsec1-openssl #7 1.915 libxmu6 libxmuu1 libxrandr2 libxrender-dev libxrender1 libxshmfence1 #7 1.915 libxslt1-dev libxss-dev libxss1 libxt-dev libxt6 libxtst6 libxv1 #7 1.915 libxxf86dga1 libxxf86vm1 libyaml-0-2 libz3-4 libz3-dev llvm-10 llvm-10-dev #7 1.915 llvm-10-runtime llvm-10-tools llvm-runtime mesa-vulkan-drivers mime-support #7 1.915 nettle-dev pkg-config python3 python3-minimal python3-pkg-resources #7 1.915 python3-pygments python3-yaml python3.8 python3.8-minimal readline-common #7 1.915 shared-mime-info tcl tcl-dev tcl8.6 tcl8.6-dev tk tk8.6 tk8.6-dev uuid-dev #7 1.915 x11-common x11-utils x11proto-core-dev x11proto-dev x11proto-scrnsaver-dev #7 1.915 x11proto-xext-dev xbitmaps xdg-user-dirs xorg-sgml-doctools xterm xtrans-dev #7 1.917 Suggested packages: #7 1.917 freetype2-doc libgcrypt20-doc gmp-doc libgmp10-doc libmpfr-dev dns-root-data #7 1.917 gnutls-bin gnutls-doc libice-doc icu-doc liblzma-doc ncurses-doc pciutils #7 1.917 readline-doc lm-sensors libsm-doc sqlite3-doc libssl-doc libx11-doc #7 1.917 libxcb-doc libxext-doc libxt-doc llvm-10-doc python3-doc python3-tk #7 1.917 python3-venv python3-setuptools python-pygments-doc ttf-bitstream-vera #7 1.917 python3.8-venv python3.8-doc tcl-doc tcl-tclreadline tcl8.6-doc tk-doc #7 1.917 tk8.6-doc mesa-utils xfonts-cyrillic #7 2.228 The following NEW packages will be installed: #7 2.228 binfmt-support bzip2-doc file icu-devtools libbz2-dev libclang-cpp10 #7 2.228 libdrm-amdgpu1 libdrm-common libdrm-intel1 libdrm-nouveau2 libdrm-radeon1 #7 2.228 libdrm2 libedit2 libevent-2.1-7 libexpat1-dev libffi-dev libfontconfig1-dev #7 2.228 libfontenc1 libfreetype-dev libfreetype6-dev libgcrypt20-dev libgl1 #7 2.228 libgl1-mesa-dri libglapi-mesa libglib2.0-0 libglib2.0-data libglvnd0 #7 2.228 libglx-mesa0 libglx0 libgmp-dev libgmpxx4ldbl libgnutls-dane0 #7 2.228 libgnutls-openssl27 libgnutls28-dev libgnutlsxx28 libgpg-error-dev #7 2.228 libice-dev libice6 libicu-dev libidn2-dev libllvm10 libllvm12 liblzma-dev #7 2.228 libmagic-mgc libmagic1 libmpdec2 libncurses-dev libncursesw5-dev libnspr4 #7 2.228 libnspr4-dev libnss3 libnss3-dev libp11-kit-dev libpciaccess0 libpfm4 #7 2.228 libpipeline1 libpng-dev libpng-tools libpthread-stubs0-dev libpython3-stdlib #7 2.229 libpython3.8-minimal libpython3.8-stdlib libreadline-dev libreadline8 #7 2.229 libsensors-config libsensors5 libsm-dev libsm6 libsqlite3-dev libssl-dev #7 2.229 libtasn1-6-dev libtasn1-doc libtcl8.6 libtk8.6 libunbound8 libutempter0 #7 2.229 libvulkan1 libwayland-client0 libx11-dev libx11-xcb1 libxau-dev libxaw7 #7 2.229 libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 libxcb-present0 libxcb-randr0 #7 2.229 libxcb-shape0 libxcb-shm0 libxcb-sync1 libxcb-xfixes0 libxcb1-dev #7 2.229 libxcomposite1 libxdmcp-dev libxext-dev libxext6 libxfixes3 libxft-dev #7 2.229 libxft2 libxi6 libxinerama1 libxkbfile1 libxml2-dev libxmlsec1 #7 2.229 libxmlsec1-dev libxmlsec1-gcrypt libxmlsec1-gnutls libxmlsec1-nss #7 2.229 libxmlsec1-openssl libxmu6 libxmuu1 libxrandr2 libxrender-dev libxrender1 #7 2.229 libxshmfence1 libxslt1-dev libxss-dev libxss1 libxt-dev libxt6 libxtst6 #7 2.229 libxv1 libxxf86dga1 libxxf86vm1 libyaml-0-2 libz3-4 libz3-dev llvm llvm-10 #7 2.229 llvm-10-dev llvm-10-runtime llvm-10-tools llvm-runtime mesa-vulkan-drivers #7 2.229 mime-support nettle-dev pkg-config python3 python3-minimal #7 2.229 python3-pkg-resources python3-pygments python3-yaml python3.8 #7 2.229 python3.8-minimal readline-common shared-mime-info tcl tcl-dev tcl8.6 #7 2.229 tcl8.6-dev tk tk-dev tk8.6 tk8.6-dev uuid-dev x11-common x11-utils #7 2.229 x11proto-core-dev x11proto-dev x11proto-scrnsaver-dev x11proto-xext-dev #7 2.229 xbitmaps xdg-user-dirs xorg-sgml-doctools xterm xtrans-dev zlib1g-dev #7 2.493 0 upgraded, 167 newly installed, 0 to remove and 0 not upgraded. #7 2.493 Need to get 138 MB of archives. #7 2.493 After this operation, 1062 MB of additional disk space will be used. #7 2.493 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libpython3.8-minimal amd64 3.8.10-0ubuntu1~20.04.9 [718 kB] #7 3.260 Get:2 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3.8-minimal amd64 3.8.10-0ubuntu1~20.04.9 [1890 kB] #7 4.013 Get:3 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-minimal amd64 3.8.2-0ubuntu2 [23.6 kB] #7 4.022 Get:4 http://archive.ubuntu.com/ubuntu focal/main amd64 mime-support all 3.64ubuntu1 [30.6 kB] #7 4.038 Get:5 http://archive.ubuntu.com/ubuntu focal/main amd64 libmpdec2 amd64 2.4.2-3 [81.1 kB] #7 4.067 Get:6 http://archive.ubuntu.com/ubuntu focal/main amd64 readline-common all 8.0-4 [53.5 kB] #7 4.089 Get:7 http://archive.ubuntu.com/ubuntu focal/main amd64 libreadline8 amd64 8.0-4 [131 kB] #7 4.143 Get:8 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libpython3.8-stdlib amd64 3.8.10-0ubuntu1~20.04.9 [1674 kB] #7 4.808 Get:9 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3.8 amd64 3.8.10-0ubuntu1~20.04.9 [387 kB] #7 4.961 Get:10 http://archive.ubuntu.com/ubuntu focal/main amd64 libpython3-stdlib amd64 3.8.2-0ubuntu2 [7068 B] #7 4.963 Get:11 http://archive.ubuntu.com/ubuntu focal/main amd64 python3 amd64 3.8.2-0ubuntu2 [47.6 kB] #7 4.983 Get:12 http://archive.ubuntu.com/ubuntu focal/main amd64 libmagic-mgc amd64 1:5.38-4 [218 kB] #7 5.068 Get:13 http://archive.ubuntu.com/ubuntu focal/main amd64 libmagic1 amd64 1:5.38-4 [75.9 kB] #7 5.100 Get:14 http://archive.ubuntu.com/ubuntu focal/main amd64 file amd64 1:5.38-4 [23.3 kB] #7 5.108 Get:15 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglib2.0-0 amd64 2.64.6-1~ubuntu20.04.7 [1289 kB] #7 5.621 Get:16 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglib2.0-data all 2.64.6-1~ubuntu20.04.7 [5944 B] #7 5.623 Get:17 http://archive.ubuntu.com/ubuntu focal/main amd64 libyaml-0-2 amd64 0.2.2-1 [48.9 kB] #7 5.640 Get:18 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-pkg-resources all 45.2.0-1ubuntu0.1 [130 kB] #7 5.693 Get:19 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-yaml amd64 5.3.1-1ubuntu0.1 [136 kB] #7 5.752 Get:20 http://archive.ubuntu.com/ubuntu focal/main amd64 shared-mime-info amd64 1.15-1 [430 kB] #7 5.921 Get:21 http://archive.ubuntu.com/ubuntu focal/main amd64 xdg-user-dirs amd64 0.17-2ubuntu1 [48.3 kB] #7 5.940 Get:22 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-common all 2.4.107-8ubuntu1~20.04.2 [5396 B] #7 5.941 Get:23 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm2 amd64 2.4.107-8ubuntu1~20.04.2 [34.1 kB] #7 5.952 Get:24 http://archive.ubuntu.com/ubuntu focal/main amd64 libedit2 amd64 3.1-20191231-1 [87.0 kB] #7 5.991 Get:25 http://archive.ubuntu.com/ubuntu focal/main amd64 libpipeline1 amd64 1.5.2-2build1 [27.7 kB] #7 6.000 Get:26 http://archive.ubuntu.com/ubuntu focal/main amd64 libxext6 amd64 2:1.3.4-0ubuntu1 [29.1 kB] #7 6.016 Get:27 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmuu1 amd64 2:1.1.3-0ubuntu1 [9728 B] #7 6.019 Get:28 http://archive.ubuntu.com/ubuntu focal/universe amd64 binfmt-support amd64 2.2.0-2 [58.2 kB] #7 6.041 Get:29 http://archive.ubuntu.com/ubuntu focal/main amd64 bzip2-doc all 1.0.8-2 [501 kB] #7 6.241 Get:30 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 icu-devtools amd64 66.1-2ubuntu2.1 [189 kB] #7 6.320 Get:31 http://archive.ubuntu.com/ubuntu focal/main amd64 libbz2-dev amd64 1.0.8-2 [30.2 kB] #7 6.329 Get:32 http://archive.ubuntu.com/ubuntu focal/main amd64 libllvm10 amd64 1:10.0.0-4ubuntu1 [15.3 MB] #7 12.57 Get:33 http://archive.ubuntu.com/ubuntu focal/universe amd64 libclang-cpp10 amd64 1:10.0.0-4ubuntu1 [9944 kB] #7 16.83 Get:34 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-amdgpu1 amd64 2.4.107-8ubuntu1~20.04.2 [18.6 kB] #7 16.83 Get:35 http://archive.ubuntu.com/ubuntu focal/main amd64 libpciaccess0 amd64 0.16-0ubuntu1 [17.9 kB] #7 16.84 Get:36 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-intel1 amd64 2.4.107-8ubuntu1~20.04.2 [60.3 kB] #7 16.86 Get:37 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-nouveau2 amd64 2.4.107-8ubuntu1~20.04.2 [16.6 kB] #7 16.87 Get:38 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-radeon1 amd64 2.4.107-8ubuntu1~20.04.2 [19.7 kB] #7 16.87 Get:39 http://archive.ubuntu.com/ubuntu focal/main amd64 libevent-2.1-7 amd64 2.1.11-stable-1 [138 kB] #7 16.93 Get:40 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libexpat1-dev amd64 2.2.9-1ubuntu0.6 [116 kB] #7 16.98 Get:41 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 zlib1g-dev amd64 1:1.2.11.dfsg-2ubuntu1.5 [155 kB] #7 17.04 Get:42 http://archive.ubuntu.com/ubuntu focal/main amd64 libpng-dev amd64 1.6.37-2 [175 kB] #7 17.11 Get:43 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libfreetype-dev amd64 2.10.1-2ubuntu0.3 [493 kB] #7 17.30 Get:44 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libfreetype6-dev amd64 2.10.1-2ubuntu0.3 [9808 B] #7 17.31 Get:45 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 uuid-dev amd64 2.34-0.1ubuntu9.6 [33.6 kB] #7 17.32 Get:46 http://archive.ubuntu.com/ubuntu focal/main amd64 pkg-config amd64 0.29.1-0ubuntu4 [45.5 kB] #7 17.34 Get:47 http://archive.ubuntu.com/ubuntu focal/main amd64 libfontconfig1-dev amd64 2.13.1-2ubuntu3 [731 kB] #7 17.63 Get:48 http://archive.ubuntu.com/ubuntu focal/main amd64 libfontenc1 amd64 1:1.1.4-0ubuntu1 [14.0 kB] #7 17.64 Get:49 http://archive.ubuntu.com/ubuntu focal/main amd64 libgpg-error-dev amd64 1.37-1 [109 kB] #7 17.68 Get:50 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgcrypt20-dev amd64 1.8.5-5ubuntu1.1 [471 kB] #7 17.87 Get:51 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglapi-mesa amd64 21.2.6-0ubuntu0.1~20.04.2 [27.4 kB] #7 17.88 Get:52 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libllvm12 amd64 1:12.0.0-3ubuntu1~20.04.5 [18.8 MB] #7 25.65 Get:53 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsensors-config all 1:3.6.0-2ubuntu1.1 [6052 B] #7 25.67 Get:54 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsensors5 amd64 1:3.6.0-2ubuntu1.1 [27.2 kB] #7 25.67 Get:55 http://archive.ubuntu.com/ubuntu focal/main amd64 libvulkan1 amd64 1.2.131.2-1 [93.3 kB] #7 25.68 Get:56 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl1-mesa-dri amd64 21.2.6-0ubuntu0.1~20.04.2 [11.0 MB] #7 30.46 Get:57 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-xcb1 amd64 2:1.6.9-2ubuntu1.6 [9448 B] #7 30.46 Get:58 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-dri2-0 amd64 1.14-2 [6920 B] #7 30.47 Get:59 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-dri3-0 amd64 1.14-2 [6552 B] #7 30.47 Get:60 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-glx0 amd64 1.14-2 [22.1 kB] #7 30.57 Get:61 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-present0 amd64 1.14-2 [5560 B] #7 30.62 Get:62 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-shm0 amd64 1.14-2 [5584 B] #7 30.67 Get:63 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-sync1 amd64 1.14-2 [8884 B] #7 30.73 Get:64 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-xfixes0 amd64 1.14-2 [9296 B] #7 30.79 Get:65 http://archive.ubuntu.com/ubuntu focal/main amd64 libxfixes3 amd64 1:5.0.3-2 [10.9 kB] #7 30.86 Get:66 http://archive.ubuntu.com/ubuntu focal/main amd64 libxshmfence1 amd64 1.3-1 [5028 B] #7 30.90 Get:67 http://archive.ubuntu.com/ubuntu focal/main amd64 libxxf86vm1 amd64 1:1.1.4-1build1 [10.2 kB] #7 30.96 Get:68 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglx-mesa0 amd64 21.2.6-0ubuntu0.1~20.04.2 [137 kB] #7 31.14 Get:69 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgmpxx4ldbl amd64 2:6.2.0+dfsg-4ubuntu0.1 [9144 B] #7 31.19 Get:70 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgmp-dev amd64 2:6.2.0+dfsg-4ubuntu0.1 [320 kB] #7 31.31 Get:71 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutls-openssl27 amd64 3.6.13-2ubuntu1.11 [29.8 kB] #7 31.40 Get:72 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libunbound8 amd64 1.9.4-2ubuntu1.5 [353 kB] #7 31.66 Get:73 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutls-dane0 amd64 3.6.13-2ubuntu1.11 [29.1 kB] #7 31.71 Get:74 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutlsxx28 amd64 3.6.13-2ubuntu1.11 [14.7 kB] #7 31.76 Get:75 http://archive.ubuntu.com/ubuntu focal/main amd64 libidn2-dev amd64 2.2.0-2 [64.6 kB] #7 31.81 Get:76 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libp11-kit-dev amd64 0.23.20-1ubuntu0.1 [65.2 kB] #7 31.86 Get:77 http://archive.ubuntu.com/ubuntu focal/main amd64 libtasn1-6-dev amd64 4.16.0-2 [85.8 kB] #7 31.92 Get:78 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 nettle-dev amd64 3.5.1+really3.5.1-2ubuntu0.2 [987 kB] #7 32.16 Get:79 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutls28-dev amd64 3.6.13-2ubuntu1.11 [873 kB] #7 32.38 Get:80 http://archive.ubuntu.com/ubuntu focal/main amd64 x11-common all 1:7.7+19ubuntu14 [22.3 kB] #7 32.43 Get:81 http://archive.ubuntu.com/ubuntu focal/main amd64 libice6 amd64 2:1.0.10-0ubuntu1 [41.0 kB] #7 32.48 Get:82 http://archive.ubuntu.com/ubuntu focal/main amd64 xorg-sgml-doctools all 1:1.11-1 [12.9 kB] #7 32.57 Get:83 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-dev all 2019.2-1ubuntu1 [594 kB] #7 32.88 Get:84 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-core-dev all 2019.2-1ubuntu1 [2620 B] #7 32.92 Get:85 http://archive.ubuntu.com/ubuntu focal/main amd64 libice-dev amd64 2:1.0.10-0ubuntu1 [47.8 kB] #7 32.97 Get:86 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libicu-dev amd64 66.1-2ubuntu2.1 [9451 kB] #7 34.76 Get:87 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libncurses-dev amd64 6.2-0ubuntu2.1 [340 kB] #7 34.86 Get:88 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libncursesw5-dev amd64 6.2-0ubuntu2.1 [988 B] #7 34.91 Get:89 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnspr4 amd64 2:4.35-0ubuntu0.20.04.1 [108 kB] #7 34.97 Get:90 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnspr4-dev amd64 2:4.35-0ubuntu0.20.04.1 [207 kB] #7 35.05 Get:91 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnss3 amd64 2:3.98-0ubuntu0.20.04.2 [1391 kB] #7 35.35 Get:92 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnss3-dev amd64 2:3.98-0ubuntu0.20.04.2 [246 kB] #7 35.44 Get:93 http://archive.ubuntu.com/ubuntu focal/main amd64 libpng-tools amd64 1.6.37-2 [26.1 kB] #7 35.53 Get:94 http://archive.ubuntu.com/ubuntu focal/main amd64 libpthread-stubs0-dev amd64 0.4-1 [5384 B] #7 35.57 Get:95 http://archive.ubuntu.com/ubuntu focal/main amd64 libreadline-dev amd64 8.0-4 [141 kB] #7 35.75 Get:96 http://archive.ubuntu.com/ubuntu focal/main amd64 libsm6 amd64 2:1.2.3-1 [16.1 kB] #7 35.80 Get:97 http://archive.ubuntu.com/ubuntu focal/main amd64 libsm-dev amd64 2:1.2.3-1 [17.0 kB] #7 35.85 Get:98 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsqlite3-dev amd64 3.31.1-4ubuntu0.6 [696 kB] #7 36.05 Get:99 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libssl-dev amd64 1.1.1f-1ubuntu2.22 [1586 kB] #7 36.41 Get:100 http://archive.ubuntu.com/ubuntu focal/main amd64 libtcl8.6 amd64 8.6.10+dfsg-1 [902 kB] #7 36.61 Get:101 http://archive.ubuntu.com/ubuntu focal/main amd64 libxrender1 amd64 1:0.9.10-1 [18.7 kB] #7 36.66 Get:102 http://archive.ubuntu.com/ubuntu focal/main amd64 libxft2 amd64 2.3.3-0ubuntu1 [39.2 kB] #7 36.71 Get:103 http://archive.ubuntu.com/ubuntu focal/main amd64 libxss1 amd64 1:1.2.3-1 [8140 B] #7 36.75 Get:104 http://archive.ubuntu.com/ubuntu focal/main amd64 libtk8.6 amd64 8.6.10-1 [714 kB] #7 36.96 Get:105 http://archive.ubuntu.com/ubuntu focal/main amd64 libutempter0 amd64 1.1.6-4 [8256 B] #7 37.01 Get:106 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwayland-client0 amd64 1.18.0-1ubuntu0.1 [23.9 kB] #7 37.10 Get:107 http://archive.ubuntu.com/ubuntu focal/main amd64 libxau-dev amd64 1:1.0.9-0ubuntu1 [9552 B] #7 37.15 Get:108 http://archive.ubuntu.com/ubuntu focal/main amd64 libxdmcp-dev amd64 1:1.1.3-0ubuntu1 [25.3 kB] #7 37.21 Get:109 http://archive.ubuntu.com/ubuntu focal/main amd64 xtrans-dev all 1.4.0-1 [68.9 kB] #7 37.30 Get:110 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb1-dev amd64 1.14-2 [80.5 kB] #7 37.36 Get:111 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-dev amd64 2:1.6.9-2ubuntu1.6 [648 kB] #7 37.54 Get:112 http://archive.ubuntu.com/ubuntu focal/main amd64 libxt6 amd64 1:1.1.5-1 [160 kB] #7 37.61 Get:113 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmu6 amd64 2:1.1.3-0ubuntu1 [45.8 kB] #7 37.66 Get:114 http://archive.ubuntu.com/ubuntu focal/main amd64 libxaw7 amd64 2:1.0.13-1 [173 kB] #7 37.73 Get:115 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-randr0 amd64 1.14-2 [16.3 kB] #7 37.82 Get:116 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-shape0 amd64 1.14-2 [5928 B] #7 37.86 Get:117 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcomposite1 amd64 1:0.4.5-1 [6976 B] #7 37.91 Get:118 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-xext-dev all 2019.2-1ubuntu1 [2616 B] #7 37.95 Get:119 http://archive.ubuntu.com/ubuntu focal/main amd64 libxext-dev amd64 2:1.3.4-0ubuntu1 [82.2 kB] #7 38.11 Get:120 http://archive.ubuntu.com/ubuntu focal/main amd64 libxrender-dev amd64 1:0.9.10-1 [24.9 kB] #7 38.17 Get:121 http://archive.ubuntu.com/ubuntu focal/main amd64 libxft-dev amd64 2.3.3-0ubuntu1 [49.1 kB] #7 38.23 Get:122 http://archive.ubuntu.com/ubuntu focal/main amd64 libxi6 amd64 2:1.7.10-0ubuntu1 [29.9 kB] #7 38.28 Get:123 http://archive.ubuntu.com/ubuntu focal/main amd64 libxinerama1 amd64 2:1.1.4-2 [6904 B] #7 38.33 Get:124 http://archive.ubuntu.com/ubuntu focal/main amd64 libxkbfile1 amd64 1:1.1.0-1 [65.3 kB] #7 38.40 Get:125 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxml2-dev amd64 2.9.10+dfsg-5ubuntu0.20.04.7 [735 kB] #7 38.62 Get:126 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1 amd64 1.2.28-2 [127 kB] #7 38.72 Get:127 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-gcrypt amd64 1.2.28-2 [41.5 kB] #7 38.82 Get:128 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-gnutls amd64 1.2.28-2 [33.4 kB] #7 38.88 Get:129 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-nss amd64 1.2.28-2 [61.8 kB] #7 38.96 Get:130 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-openssl amd64 1.2.28-2 [75.9 kB] #7 39.03 Get:131 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxslt1-dev amd64 1.1.34-4ubuntu0.20.04.1 [219 kB] #7 39.14 Get:132 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-dev amd64 1.2.28-2 [387 kB] #7 39.25 Get:133 http://archive.ubuntu.com/ubuntu focal/main amd64 libxrandr2 amd64 2:1.5.2-0ubuntu1 [18.5 kB] #7 39.29 Get:134 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-scrnsaver-dev all 2019.2-1ubuntu1 [2624 B] #7 39.33 Get:135 http://archive.ubuntu.com/ubuntu focal/main amd64 libxss-dev amd64 1:1.2.3-1 [11.9 kB] #7 39.37 Get:136 http://archive.ubuntu.com/ubuntu focal/main amd64 libxt-dev amd64 1:1.1.5-1 [395 kB] #7 39.50 Get:137 http://archive.ubuntu.com/ubuntu focal/main amd64 libxtst6 amd64 2:1.2.3-1 [12.8 kB] #7 39.59 Get:138 http://archive.ubuntu.com/ubuntu focal/main amd64 libxv1 amd64 2:1.0.11-1 [10.7 kB] #7 39.64 Get:139 http://archive.ubuntu.com/ubuntu focal/main amd64 libxxf86dga1 amd64 2:1.1.5-0ubuntu1 [12.0 kB] #7 39.69 Get:140 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10-runtime amd64 1:10.0.0-4ubuntu1 [180 kB] #7 39.88 Get:141 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-runtime amd64 1:10.0-50~exp1 [2916 B] #7 39.93 Get:142 http://archive.ubuntu.com/ubuntu focal/main amd64 libpfm4 amd64 4.10.1+git20-g7700f49-2 [266 kB] #7 40.02 Get:143 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10 amd64 1:10.0.0-4ubuntu1 [5214 kB] #7 41.04 Get:144 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm amd64 1:10.0-50~exp1 [3880 B] #7 41.08 Get:145 http://archive.ubuntu.com/ubuntu focal/main amd64 libffi-dev amd64 3.3-4 [57.0 kB] #7 41.13 Get:146 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-pygments all 2.3.1+dfsg-1ubuntu2.2 [579 kB] #7 41.29 Get:147 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10-tools amd64 1:10.0.0-4ubuntu1 [317 kB] #7 41.38 Get:148 http://archive.ubuntu.com/ubuntu focal/universe amd64 libz3-4 amd64 4.8.7-4build1 [6792 kB] #7 42.66 Get:149 http://archive.ubuntu.com/ubuntu focal/universe amd64 libz3-dev amd64 4.8.7-4build1 [67.5 kB] #7 42.81 Get:150 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10-dev amd64 1:10.0.0-4ubuntu1 [26.0 MB] #7 47.54 Get:151 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 mesa-vulkan-drivers amd64 21.2.6-0ubuntu0.1~20.04.2 [5788 kB] #7 48.60 Get:152 http://archive.ubuntu.com/ubuntu focal/main amd64 tcl8.6 amd64 8.6.10+dfsg-1 [14.8 kB] #7 48.65 Get:153 http://archive.ubuntu.com/ubuntu focal/universe amd64 tcl amd64 8.6.9+1 [5112 B] #7 48.69 Get:154 http://archive.ubuntu.com/ubuntu focal/main amd64 tcl8.6-dev amd64 8.6.10+dfsg-1 [905 kB] #7 48.89 Get:155 http://archive.ubuntu.com/ubuntu focal/universe amd64 tcl-dev amd64 8.6.9+1 [5760 B] #7 48.94 Get:156 http://archive.ubuntu.com/ubuntu focal/main amd64 tk8.6 amd64 8.6.10-1 [12.5 kB] #7 48.98 Get:157 http://archive.ubuntu.com/ubuntu focal/universe amd64 tk amd64 8.6.9+1 [3240 B] #7 49.02 Get:158 http://archive.ubuntu.com/ubuntu focal/main amd64 tk8.6-dev amd64 8.6.10-1 [711 kB] #7 49.20 Get:159 http://archive.ubuntu.com/ubuntu focal/universe amd64 tk-dev amd64 8.6.9+1 [3076 B] #7 49.29 Get:160 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglvnd0 amd64 1.3.2-1~ubuntu0.20.04.2 [48.1 kB] #7 49.42 Get:161 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglx0 amd64 1.3.2-1~ubuntu0.20.04.2 [32.5 kB] #7 49.48 Get:162 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl1 amd64 1.3.2-1~ubuntu0.20.04.2 [85.8 kB] #7 49.57 Get:163 http://archive.ubuntu.com/ubuntu focal/main amd64 x11-utils amd64 7.7+5 [199 kB] #7 49.68 Get:164 http://archive.ubuntu.com/ubuntu focal/main amd64 xbitmaps all 1.1.1-2 [28.1 kB] #7 49.72 Get:165 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 xterm amd64 353-1ubuntu1.20.04.2 [765 kB] #7 49.91 Get:166 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 liblzma-dev amd64 5.2.4-1ubuntu1.1 [147 kB] #7 49.98 Get:167 http://archive.ubuntu.com/ubuntu focal/main amd64 libtasn1-doc all 4.16.0-2 [303 kB] #7 50.23 debconf: delaying package configuration, since apt-utils is not installed #7 50.28 Fetched 138 MB in 48s (2890 kB/s) #7 50.34 Selecting previously unselected package libpython3.8-minimal:amd64. #7 50.34 (Reading database ... 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previously unselected package libgnutls-openssl27:amd64. #7 63.61 Preparing to unpack .../060-libgnutls-openssl27_3.6.13-2ubuntu1.11_amd64.deb ... #7 63.62 Unpacking libgnutls-openssl27:amd64 (3.6.13-2ubuntu1.11) ... #7 63.69 Selecting previously unselected package libunbound8:amd64. #7 63.69 Preparing to unpack .../061-libunbound8_1.9.4-2ubuntu1.5_amd64.deb ... #7 63.70 Unpacking libunbound8:amd64 (1.9.4-2ubuntu1.5) ... #7 63.82 Selecting previously unselected package libgnutls-dane0:amd64. #7 63.83 Preparing to unpack .../062-libgnutls-dane0_3.6.13-2ubuntu1.11_amd64.deb ... #7 63.83 Unpacking libgnutls-dane0:amd64 (3.6.13-2ubuntu1.11) ... #7 63.91 Selecting previously unselected package libgnutlsxx28:amd64. #7 63.91 Preparing to unpack .../063-libgnutlsxx28_3.6.13-2ubuntu1.11_amd64.deb ... #7 63.92 Unpacking libgnutlsxx28:amd64 (3.6.13-2ubuntu1.11) ... #7 63.98 Selecting previously unselected package libidn2-dev:amd64. #7 63.99 Preparing to unpack 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unselected package x11-common. #7 64.64 Preparing to unpack .../069-x11-common_1%3a7.7+19ubuntu14_all.deb ... #7 64.67 dpkg-query: no packages found matching nux-tools #7 64.69 Unpacking x11-common (1:7.7+19ubuntu14) ... #7 64.77 Selecting previously unselected package libice6:amd64. #7 64.78 Preparing to unpack .../070-libice6_2%3a1.0.10-0ubuntu1_amd64.deb ... #7 64.78 Unpacking libice6:amd64 (2:1.0.10-0ubuntu1) ... #7 64.85 Selecting previously unselected package xorg-sgml-doctools. #7 64.86 Preparing to unpack .../071-xorg-sgml-doctools_1%3a1.11-1_all.deb ... #7 64.87 Unpacking xorg-sgml-doctools (1:1.11-1) ... #7 64.93 Selecting previously unselected package x11proto-dev. #7 64.94 Preparing to unpack .../072-x11proto-dev_2019.2-1ubuntu1_all.deb ... #7 64.94 Unpacking x11proto-dev (2019.2-1ubuntu1) ... #7 65.08 Selecting previously unselected package x11proto-core-dev. #7 65.09 Preparing to unpack .../073-x11proto-core-dev_2019.2-1ubuntu1_all.deb ... #7 65.10 Unpacking x11proto-core-dev (2019.2-1ubuntu1) ... #7 65.16 Selecting previously unselected package libice-dev:amd64. #7 65.16 Preparing to unpack .../074-libice-dev_2%3a1.0.10-0ubuntu1_amd64.deb ... #7 65.17 Unpacking libice-dev:amd64 (2:1.0.10-0ubuntu1) ... #7 65.25 Selecting previously unselected package libicu-dev:amd64. #7 65.25 Preparing to unpack .../075-libicu-dev_66.1-2ubuntu2.1_amd64.deb ... #7 65.26 Unpacking libicu-dev:amd64 (66.1-2ubuntu2.1) ... #7 66.22 Selecting previously unselected package libncurses-dev:amd64. #7 66.23 Preparing to unpack .../076-libncurses-dev_6.2-0ubuntu2.1_amd64.deb ... #7 66.24 Unpacking libncurses-dev:amd64 (6.2-0ubuntu2.1) ... #7 66.36 Selecting previously unselected package libncursesw5-dev:amd64. #7 66.36 Preparing to unpack .../077-libncursesw5-dev_6.2-0ubuntu2.1_amd64.deb ... #7 66.37 Unpacking libncursesw5-dev:amd64 (6.2-0ubuntu2.1) ... #7 66.44 Selecting previously unselected package libnspr4:amd64. #7 66.44 Preparing to unpack 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package libpthread-stubs0-dev:amd64. #7 67.07 Preparing to unpack .../083-libpthread-stubs0-dev_0.4-1_amd64.deb ... #7 67.08 Unpacking libpthread-stubs0-dev:amd64 (0.4-1) ... #7 67.17 Selecting previously unselected package libreadline-dev:amd64. #7 67.18 Preparing to unpack .../084-libreadline-dev_8.0-4_amd64.deb ... #7 67.19 Unpacking libreadline-dev:amd64 (8.0-4) ... #7 67.29 Selecting previously unselected package libsm6:amd64. #7 67.29 Preparing to unpack .../085-libsm6_2%3a1.2.3-1_amd64.deb ... #7 67.30 Unpacking libsm6:amd64 (2:1.2.3-1) ... #7 67.37 Selecting previously unselected package libsm-dev:amd64. #7 67.37 Preparing to unpack .../086-libsm-dev_2%3a1.2.3-1_amd64.deb ... #7 67.38 Unpacking libsm-dev:amd64 (2:1.2.3-1) ... #7 67.45 Selecting previously unselected package libsqlite3-dev:amd64. #7 67.45 Preparing to unpack .../087-libsqlite3-dev_3.31.1-4ubuntu0.6_amd64.deb ... #7 67.46 Unpacking libsqlite3-dev:amd64 (3.31.1-4ubuntu0.6) ... #7 67.60 Selecting previously unselected package libssl-dev:amd64. #7 67.60 Preparing to unpack .../088-libssl-dev_1.1.1f-1ubuntu2.22_amd64.deb ... #7 67.61 Unpacking libssl-dev:amd64 (1.1.1f-1ubuntu2.22) ... #7 67.84 Selecting previously unselected package libtcl8.6:amd64. #7 67.85 Preparing to unpack .../089-libtcl8.6_8.6.10+dfsg-1_amd64.deb ... #7 67.85 Unpacking libtcl8.6:amd64 (8.6.10+dfsg-1) ... #7 68.05 Selecting previously unselected package libxrender1:amd64. #7 68.05 Preparing to unpack .../090-libxrender1_1%3a0.9.10-1_amd64.deb ... #7 68.06 Unpacking libxrender1:amd64 (1:0.9.10-1) ... #7 68.13 Selecting previously unselected package libxft2:amd64. #7 68.14 Preparing to unpack .../091-libxft2_2.3.3-0ubuntu1_amd64.deb ... #7 68.14 Unpacking libxft2:amd64 (2.3.3-0ubuntu1) ... #7 68.23 Selecting previously unselected package libxss1:amd64. #7 68.23 Preparing to unpack .../092-libxss1_1%3a1.2.3-1_amd64.deb ... #7 68.24 Unpacking libxss1:amd64 (1:1.2.3-1) ... #7 68.31 Selecting previously unselected package libtk8.6:amd64. #7 68.31 Preparing to unpack .../093-libtk8.6_8.6.10-1_amd64.deb ... #7 68.32 Unpacking libtk8.6:amd64 (8.6.10-1) ... #7 68.47 Selecting previously unselected package libutempter0:amd64. #7 68.47 Preparing to unpack .../094-libutempter0_1.1.6-4_amd64.deb ... #7 68.48 Unpacking libutempter0:amd64 (1.1.6-4) ... #7 68.55 Selecting previously unselected package libwayland-client0:amd64. #7 68.56 Preparing to unpack .../095-libwayland-client0_1.18.0-1ubuntu0.1_amd64.deb ... #7 68.56 Unpacking libwayland-client0:amd64 (1.18.0-1ubuntu0.1) ... #7 68.63 Selecting previously unselected package libxau-dev:amd64. #7 68.63 Preparing to unpack .../096-libxau-dev_1%3a1.0.9-0ubuntu1_amd64.deb ... #7 68.64 Unpacking libxau-dev:amd64 (1:1.0.9-0ubuntu1) ... #7 68.70 Selecting previously unselected package libxdmcp-dev:amd64. #7 68.71 Preparing to unpack .../097-libxdmcp-dev_1%3a1.1.3-0ubuntu1_amd64.deb ... #7 68.71 Unpacking libxdmcp-dev:amd64 (1:1.1.3-0ubuntu1) ... #7 68.78 Selecting previously unselected package xtrans-dev. #7 68.78 Preparing to unpack .../098-xtrans-dev_1.4.0-1_all.deb ... #7 68.79 Unpacking xtrans-dev (1.4.0-1) ... #7 68.87 Selecting previously unselected package libxcb1-dev:amd64. #7 68.87 Preparing to unpack .../099-libxcb1-dev_1.14-2_amd64.deb ... #7 68.88 Unpacking libxcb1-dev:amd64 (1.14-2) ... #7 68.96 Selecting previously unselected package libx11-dev:amd64. #7 68.96 Preparing to unpack .../100-libx11-dev_2%3a1.6.9-2ubuntu1.6_amd64.deb ... #7 68.97 Unpacking libx11-dev:amd64 (2:1.6.9-2ubuntu1.6) ... #7 69.12 Selecting previously unselected package libxt6:amd64. #7 69.12 Preparing to unpack .../101-libxt6_1%3a1.1.5-1_amd64.deb ... #7 69.12 Unpacking libxt6:amd64 (1:1.1.5-1) ... #7 69.22 Selecting previously unselected package libxmu6:amd64. #7 69.22 Preparing to unpack .../102-libxmu6_2%3a1.1.3-0ubuntu1_amd64.deb ... #7 69.23 Unpacking libxmu6:amd64 (2:1.1.3-0ubuntu1) ... #7 69.31 Selecting previously unselected package libxaw7:amd64. #7 69.32 Preparing to unpack .../103-libxaw7_2%3a1.0.13-1_amd64.deb ... #7 69.32 Unpacking libxaw7:amd64 (2:1.0.13-1) ... #7 69.43 Selecting previously unselected package libxcb-randr0:amd64. #7 69.43 Preparing to unpack .../104-libxcb-randr0_1.14-2_amd64.deb ... #7 69.44 Unpacking libxcb-randr0:amd64 (1.14-2) ... #7 69.51 Selecting previously unselected package libxcb-shape0:amd64. #7 69.52 Preparing to unpack .../105-libxcb-shape0_1.14-2_amd64.deb ... #7 69.52 Unpacking libxcb-shape0:amd64 (1.14-2) ... #7 69.59 Selecting previously unselected package libxcomposite1:amd64. #7 69.60 Preparing to unpack .../106-libxcomposite1_1%3a0.4.5-1_amd64.deb ... #7 69.61 Unpacking libxcomposite1:amd64 (1:0.4.5-1) ... #7 69.67 Selecting previously unselected package x11proto-xext-dev. #7 69.67 Preparing to unpack .../107-x11proto-xext-dev_2019.2-1ubuntu1_all.deb ... #7 69.68 Unpacking x11proto-xext-dev (2019.2-1ubuntu1) ... #7 69.74 Selecting previously unselected package libxext-dev:amd64. #7 69.75 Preparing to unpack .../108-libxext-dev_2%3a1.3.4-0ubuntu1_amd64.deb ... #7 69.75 Unpacking libxext-dev:amd64 (2:1.3.4-0ubuntu1) ... #7 69.84 Selecting previously unselected package libxrender-dev:amd64. #7 69.84 Preparing to unpack .../109-libxrender-dev_1%3a0.9.10-1_amd64.deb ... #7 69.84 Unpacking libxrender-dev:amd64 (1:0.9.10-1) ... #7 69.90 Selecting previously unselected package libxft-dev:amd64. #7 69.90 Preparing to unpack .../110-libxft-dev_2.3.3-0ubuntu1_amd64.deb ... #7 69.91 Unpacking libxft-dev:amd64 (2.3.3-0ubuntu1) ... #7 69.98 Selecting previously unselected package libxi6:amd64. #7 69.99 Preparing to unpack .../111-libxi6_2%3a1.7.10-0ubuntu1_amd64.deb ... #7 69.99 Unpacking libxi6:amd64 (2:1.7.10-0ubuntu1) ... #7 70.07 Selecting previously unselected package libxinerama1:amd64. #7 70.07 Preparing to unpack .../112-libxinerama1_2%3a1.1.4-2_amd64.deb ... #7 70.08 Unpacking libxinerama1:amd64 (2:1.1.4-2) ... #7 70.15 Selecting previously unselected package libxkbfile1:amd64. #7 70.15 Preparing to unpack .../113-libxkbfile1_1%3a1.1.0-1_amd64.deb ... #7 70.16 Unpacking libxkbfile1:amd64 (1:1.1.0-1) ... #7 70.23 Selecting previously unselected package libxml2-dev:amd64. #7 70.24 Preparing to unpack .../114-libxml2-dev_2.9.10+dfsg-5ubuntu0.20.04.7_amd64.deb ... #7 70.25 Unpacking libxml2-dev:amd64 (2.9.10+dfsg-5ubuntu0.20.04.7) ... #7 70.40 Selecting previously unselected package libxmlsec1:amd64. #7 70.41 Preparing to unpack .../115-libxmlsec1_1.2.28-2_amd64.deb ... #7 70.42 Unpacking libxmlsec1:amd64 (1.2.28-2) ... #7 70.52 Selecting previously unselected package libxmlsec1-gcrypt:amd64. #7 70.52 Preparing to unpack .../116-libxmlsec1-gcrypt_1.2.28-2_amd64.deb ... #7 70.53 Unpacking libxmlsec1-gcrypt:amd64 (1.2.28-2) ... #7 70.61 Selecting previously unselected package libxmlsec1-gnutls:amd64. #7 70.62 Preparing to unpack .../117-libxmlsec1-gnutls_1.2.28-2_amd64.deb ... #7 70.62 Unpacking libxmlsec1-gnutls:amd64 (1.2.28-2) ... #7 70.69 Selecting previously unselected package libxmlsec1-nss:amd64. #7 70.69 Preparing to unpack .../118-libxmlsec1-nss_1.2.28-2_amd64.deb ... #7 70.70 Unpacking libxmlsec1-nss:amd64 (1.2.28-2) ... #7 70.79 Selecting previously unselected package libxmlsec1-openssl:amd64. #7 70.80 Preparing to unpack .../119-libxmlsec1-openssl_1.2.28-2_amd64.deb ... #7 70.80 Unpacking libxmlsec1-openssl:amd64 (1.2.28-2) ... #7 70.89 Selecting previously unselected package libxslt1-dev:amd64. #7 70.89 Preparing to unpack .../120-libxslt1-dev_1.1.34-4ubuntu0.20.04.1_amd64.deb ... #7 70.90 Unpacking libxslt1-dev:amd64 (1.1.34-4ubuntu0.20.04.1) ... #7 71.00 Selecting previously unselected package libxmlsec1-dev. #7 71.01 Preparing to unpack .../121-libxmlsec1-dev_1.2.28-2_amd64.deb ... #7 71.01 Unpacking libxmlsec1-dev (1.2.28-2) ... #7 71.15 Selecting previously unselected package libxrandr2:amd64. #7 71.15 Preparing to unpack .../122-libxrandr2_2%3a1.5.2-0ubuntu1_amd64.deb ... #7 71.15 Unpacking libxrandr2:amd64 (2:1.5.2-0ubuntu1) ... #7 71.21 Selecting previously unselected package x11proto-scrnsaver-dev. #7 71.22 Preparing to unpack .../123-x11proto-scrnsaver-dev_2019.2-1ubuntu1_all.deb ... #7 71.22 Unpacking x11proto-scrnsaver-dev (2019.2-1ubuntu1) ... #7 71.28 Selecting previously unselected package libxss-dev:amd64. #7 71.28 Preparing to unpack .../124-libxss-dev_1%3a1.2.3-1_amd64.deb ... #7 71.29 Unpacking libxss-dev:amd64 (1:1.2.3-1) ... #7 71.36 Selecting previously unselected package libxt-dev:amd64. #7 71.36 Preparing to unpack .../125-libxt-dev_1%3a1.1.5-1_amd64.deb ... #7 71.37 Unpacking libxt-dev:amd64 (1:1.1.5-1) ... #7 71.50 Selecting previously unselected package libxtst6:amd64. #7 71.51 Preparing to unpack .../126-libxtst6_2%3a1.2.3-1_amd64.deb ... #7 71.52 Unpacking libxtst6:amd64 (2:1.2.3-1) ... #7 71.58 Selecting previously unselected package libxv1:amd64. #7 71.59 Preparing to unpack .../127-libxv1_2%3a1.0.11-1_amd64.deb ... #7 71.59 Unpacking libxv1:amd64 (2:1.0.11-1) ... #7 71.66 Selecting previously unselected package libxxf86dga1:amd64. #7 71.66 Preparing to unpack .../128-libxxf86dga1_2%3a1.1.5-0ubuntu1_amd64.deb ... #7 71.66 Unpacking libxxf86dga1:amd64 (2:1.1.5-0ubuntu1) ... #7 71.73 Selecting previously unselected package llvm-10-runtime. #7 71.74 Preparing to unpack .../129-llvm-10-runtime_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 71.74 Unpacking llvm-10-runtime (1:10.0.0-4ubuntu1) ... #7 71.84 Selecting previously unselected package llvm-runtime. #7 71.84 Preparing to unpack .../130-llvm-runtime_1%3a10.0-50~exp1_amd64.deb ... #7 71.85 Unpacking llvm-runtime (1:10.0-50~exp1) ... #7 71.93 Selecting previously unselected package libpfm4:amd64. #7 71.93 Preparing to unpack .../131-libpfm4_4.10.1+git20-g7700f49-2_amd64.deb ... #7 71.94 Unpacking libpfm4:amd64 (4.10.1+git20-g7700f49-2) ... #7 72.05 Selecting previously unselected package llvm-10. #7 72.05 Preparing to unpack .../132-llvm-10_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 72.06 Unpacking llvm-10 (1:10.0.0-4ubuntu1) ... #7 72.62 Selecting previously unselected package llvm. #7 72.62 Preparing to unpack .../133-llvm_1%3a10.0-50~exp1_amd64.deb ... #7 72.63 Unpacking llvm (1:10.0-50~exp1) ... #7 72.71 Selecting previously unselected package libffi-dev:amd64. #7 72.71 Preparing to unpack .../134-libffi-dev_3.3-4_amd64.deb ... #7 72.72 Unpacking libffi-dev:amd64 (3.3-4) ... #7 72.81 Selecting previously unselected package python3-pygments. #7 72.81 Preparing to unpack .../135-python3-pygments_2.3.1+dfsg-1ubuntu2.2_all.deb ... #7 72.82 Unpacking python3-pygments (2.3.1+dfsg-1ubuntu2.2) ... #7 72.95 Selecting previously unselected package llvm-10-tools. #7 72.95 Preparing to unpack .../136-llvm-10-tools_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 72.96 Unpacking llvm-10-tools (1:10.0.0-4ubuntu1) ... #7 73.12 Selecting previously unselected package libz3-4:amd64. #7 73.12 Preparing to unpack .../137-libz3-4_4.8.7-4build1_amd64.deb ... #7 73.13 Unpacking libz3-4:amd64 (4.8.7-4build1) ... #7 73.78 Selecting previously unselected package libz3-dev:amd64. #7 73.78 Preparing to unpack .../138-libz3-dev_4.8.7-4build1_amd64.deb ... #7 73.79 Unpacking libz3-dev:amd64 (4.8.7-4build1) ... #7 73.88 Selecting previously unselected package llvm-10-dev. #7 73.89 Preparing to unpack .../139-llvm-10-dev_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 73.90 Unpacking llvm-10-dev (1:10.0.0-4ubuntu1) ... #7 76.60 Selecting previously unselected package mesa-vulkan-drivers:amd64. #7 76.60 Preparing to unpack .../140-mesa-vulkan-drivers_21.2.6-0ubuntu0.1~20.04.2_amd64.deb ... #7 76.61 Unpacking mesa-vulkan-drivers:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 77.24 Selecting previously unselected package tcl8.6. #7 77.24 Preparing to unpack .../141-tcl8.6_8.6.10+dfsg-1_amd64.deb ... #7 77.25 Unpacking tcl8.6 (8.6.10+dfsg-1) ... #7 77.32 Selecting previously unselected package tcl. #7 77.32 Preparing to unpack .../142-tcl_8.6.9+1_amd64.deb ... #7 77.34 Unpacking tcl (8.6.9+1) ... #7 77.40 Selecting previously unselected package tcl8.6-dev:amd64. #7 77.41 Preparing to unpack .../143-tcl8.6-dev_8.6.10+dfsg-1_amd64.deb ... #7 77.42 Unpacking tcl8.6-dev:amd64 (8.6.10+dfsg-1) ... #7 77.59 Selecting previously unselected package tcl-dev:amd64. #7 77.59 Preparing to unpack .../144-tcl-dev_8.6.9+1_amd64.deb ... #7 77.60 Unpacking tcl-dev:amd64 (8.6.9+1) ... #7 77.68 Selecting previously unselected package tk8.6. #7 77.69 Preparing to unpack .../145-tk8.6_8.6.10-1_amd64.deb ... #7 77.69 Unpacking tk8.6 (8.6.10-1) ... #7 77.76 Selecting previously unselected package tk. #7 77.77 Preparing to unpack .../146-tk_8.6.9+1_amd64.deb ... #7 77.78 Unpacking tk (8.6.9+1) ... #7 77.85 Selecting previously unselected package tk8.6-dev:amd64. #7 77.85 Preparing to unpack .../147-tk8.6-dev_8.6.10-1_amd64.deb ... #7 77.86 Unpacking tk8.6-dev:amd64 (8.6.10-1) ... #7 78.01 Selecting previously unselected package tk-dev:amd64. #7 78.01 Preparing to unpack .../148-tk-dev_8.6.9+1_amd64.deb ... #7 78.02 Unpacking tk-dev:amd64 (8.6.9+1) ... #7 78.10 Selecting previously unselected package libglvnd0:amd64. #7 78.10 Preparing to unpack .../149-libglvnd0_1.3.2-1~ubuntu0.20.04.2_amd64.deb ... #7 78.11 Unpacking libglvnd0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 78.21 Selecting previously unselected package libglx0:amd64. #7 78.21 Preparing to unpack .../150-libglx0_1.3.2-1~ubuntu0.20.04.2_amd64.deb ... #7 78.22 Unpacking libglx0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 78.31 Selecting previously unselected package libgl1:amd64. #7 78.31 Preparing to unpack .../151-libgl1_1.3.2-1~ubuntu0.20.04.2_amd64.deb ... #7 78.32 Unpacking libgl1:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 78.41 Selecting previously unselected package x11-utils. #7 78.42 Preparing to unpack .../152-x11-utils_7.7+5_amd64.deb ... #7 78.42 Unpacking x11-utils (7.7+5) ... #7 78.52 Selecting previously unselected package xbitmaps. #7 78.53 Preparing to unpack .../153-xbitmaps_1.1.1-2_all.deb ... #7 78.54 Unpacking xbitmaps (1.1.1-2) ... #7 78.63 Selecting previously unselected package xterm. #7 78.63 Preparing to unpack .../154-xterm_353-1ubuntu1.20.04.2_amd64.deb ... #7 78.64 Unpacking xterm (353-1ubuntu1.20.04.2) ... #7 78.78 Selecting previously unselected package liblzma-dev:amd64. #7 78.78 Preparing to unpack .../155-liblzma-dev_5.2.4-1ubuntu1.1_amd64.deb ... #7 78.79 Unpacking liblzma-dev:amd64 (5.2.4-1ubuntu1.1) ... #7 78.88 Selecting previously unselected package libtasn1-doc. #7 78.89 Preparing to unpack .../156-libtasn1-doc_4.16.0-2_all.deb ... #7 78.89 Unpacking libtasn1-doc (4.16.0-2) ... #7 79.03 Setting up libpipeline1:amd64 (1.5.2-2build1) ... #7 79.05 Setting up libxcb-dri3-0:amd64 (1.14-2) ... #7 79.08 Setting up bzip2-doc (1.0.8-2) ... #7 79.10 Setting up libx11-xcb1:amd64 (2:1.6.9-2ubuntu1.6) ... #7 79.12 Setting up libpciaccess0:amd64 (0.16-0ubuntu1) ... #7 79.14 Setting up libncurses-dev:amd64 (6.2-0ubuntu2.1) ... #7 79.16 Setting up libgnutls-openssl27:amd64 (3.6.13-2ubuntu1.11) ... #7 79.18 Setting up libxcb-xfixes0:amd64 (1.14-2) ... #7 79.20 Setting up mime-support (3.64ubuntu1) ... #7 79.27 Setting up libncursesw5-dev:amd64 (6.2-0ubuntu2.1) ... #7 79.29 Setting up libxrender1:amd64 (1:0.9.10-1) ... #7 79.31 Setting up xdg-user-dirs (0.17-2ubuntu1) ... #7 79.37 Setting up libpng-tools (1.6.37-2) ... #7 79.39 Setting up libmagic-mgc (1:5.38-4) ... #7 79.41 Setting up libyaml-0-2:amd64 (0.2.2-1) ... #7 79.44 Setting up libglib2.0-0:amd64 (2.64.6-1~ubuntu20.04.7) ... #7 79.46 No schema files found: doing nothing. #7 79.47 Setting up libglvnd0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 79.50 Setting up libxcb-glx0:amd64 (1.14-2) ... #7 79.52 Setting up libtasn1-doc (4.16.0-2) ... #7 79.54 Setting up libedit2:amd64 (3.1-20191231-1) ... #7 79.56 Setting up libxcb-shape0:amd64 (1.14-2) ... #7 79.59 Setting up x11-common (1:7.7+19ubuntu14) ... #7 79.84 update-rc.d: warning: start and stop actions are no longer supported; falling back to defaults #7 79.85 invoke-rc.d: could not determine current runlevel #7 79.86 invoke-rc.d: policy-rc.d denied execution of start. #7 79.87 Setting up libsensors-config (1:3.6.0-2ubuntu1.1) ... #7 79.91 Setting up libmagic1:amd64 (1:5.38-4) ... #7 79.95 Setting up libxext6:amd64 (2:1.3.4-0ubuntu1) ... #7 79.97 Setting up libxcb-shm0:amd64 (1.14-2) ... #7 79.99 Setting up file (1:5.38-4) ... #7 80.01 Setting up libffi-dev:amd64 (3.3-4) ... #7 80.04 Setting up libpthread-stubs0-dev:amd64 (0.4-1) ... #7 80.06 Setting up libxxf86vm1:amd64 (1:1.1.4-1build1) ... #7 80.08 Setting up libllvm10:amd64 (1:10.0.0-4ubuntu1) ... #7 80.10 Setting up libxcb-present0:amd64 (1.14-2) ... #7 80.12 Setting up xtrans-dev (1.4.0-1) ... #7 80.15 Setting up libfontenc1:amd64 (1:1.1.4-0ubuntu1) ... #7 80.17 Setting up libz3-4:amd64 (4.8.7-4build1) ... #7 80.19 Setting up libglib2.0-data (2.64.6-1~ubuntu20.04.7) ... #7 80.21 Setting up libgmpxx4ldbl:amd64 (2:6.2.0+dfsg-4ubuntu0.1) ... #7 80.24 Setting up libpfm4:amd64 (4.10.1+git20-g7700f49-2) ... #7 80.26 Setting up libexpat1-dev:amd64 (2.2.9-1ubuntu0.6) ... #7 80.28 Setting up libsqlite3-dev:amd64 (3.31.1-4ubuntu0.6) ... #7 80.30 Setting up libgpg-error-dev (1.37-1) ... #7 80.32 Setting up libnspr4:amd64 (2:4.35-0ubuntu0.20.04.1) ... #7 80.35 Setting up libxfixes3:amd64 (1:5.0.3-2) ... #7 80.37 Setting up libxcb-sync1:amd64 (1.14-2) ... #7 80.39 Setting up uuid-dev:amd64 (2.34-0.1ubuntu9.6) ... #7 80.41 Setting up libllvm12:amd64 (1:12.0.0-3ubuntu1~20.04.5) ... #7 80.44 Setting up shared-mime-info (1.15-1) ... #7 83.97 Setting up libxinerama1:amd64 (2:1.1.4-2) ... #7 83.99 Setting up libxv1:amd64 (2:1.0.11-1) ... #7 84.01 Setting up libssl-dev:amd64 (1.1.1f-1ubuntu2.22) ... #7 84.03 Setting up libevent-2.1-7:amd64 (2.1.11-stable-1) ... #7 84.06 Setting up libxrandr2:amd64 (2:1.5.2-0ubuntu1) ... #7 84.08 Setting up libtcl8.6:amd64 (8.6.10+dfsg-1) ... #7 84.10 Setting up binfmt-support (2.2.0-2) ... #7 84.34 invoke-rc.d: could not determine current runlevel #7 84.35 invoke-rc.d: policy-rc.d denied execution of start. #7 84.35 Setting up icu-devtools (66.1-2ubuntu2.1) ... #7 84.37 Setting up pkg-config (0.29.1-0ubuntu4) ... #7 84.45 Setting up libgnutlsxx28:amd64 (3.6.13-2ubuntu1.11) ... #7 84.47 Setting up libsensors5:amd64 (1:3.6.0-2ubuntu1.1) ... #7 84.49 Setting up libidn2-dev:amd64 (2.2.0-2) ... #7 84.51 Setting up libglapi-mesa:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 84.54 Setting up libvulkan1:amd64 (1.2.131.2-1) ... #7 84.56 Setting up libutempter0:amd64 (1.1.6-4) ... #7 84.58 Setting up liblzma-dev:amd64 (5.2.4-1ubuntu1.1) ... #7 84.60 Setting up libxcb-dri2-0:amd64 (1.14-2) ... #7 84.63 Setting up zlib1g-dev:amd64 (1:1.2.11.dfsg-2ubuntu1.5) ... #7 84.65 Setting up libxshmfence1:amd64 (1.3-1) ... #7 84.67 Setting up libxcb-randr0:amd64 (1.14-2) ... #7 84.70 Setting up xorg-sgml-doctools (1:1.11-1) ... #7 84.72 Setting up libxss1:amd64 (1:1.2.3-1) ... #7 84.74 Setting up libxkbfile1:amd64 (1:1.1.0-1) ... #7 84.76 Setting up libmpdec2:amd64 (2.4.2-3) ... #7 84.78 Setting up libxmlsec1:amd64 (1.2.28-2) ... #7 84.81 Setting up libtasn1-6-dev:amd64 (4.16.0-2) ... #7 84.83 Setting up libdrm-common (2.4.107-8ubuntu1~20.04.2) ... #7 84.85 Setting up libxcomposite1:amd64 (1:0.4.5-1) ... #7 84.90 Setting up readline-common (8.0-4) ... #7 84.93 Setting up libicu-dev:amd64 (66.1-2ubuntu2.1) ... #7 84.95 Setting up libxmuu1:amd64 (2:1.1.3-0ubuntu1) ... #7 84.98 Setting up xbitmaps (1.1.1-2) ... #7 85.00 Setting up libp11-kit-dev:amd64 (0.23.20-1ubuntu0.1) ... #7 85.02 Setting up libbz2-dev:amd64 (1.0.8-2) ... #7 85.04 Setting up libwayland-client0:amd64 (1.18.0-1ubuntu0.1) ... #7 85.06 Setting up x11proto-dev (2019.2-1ubuntu1) ... #7 85.08 Setting up libnspr4-dev (2:4.35-0ubuntu0.20.04.1) ... #7 85.11 Setting up libz3-dev:amd64 (4.8.7-4build1) ... #7 85.13 Setting up libice6:amd64 (2:1.0.10-0ubuntu1) ... #7 85.15 Setting up tcl8.6 (8.6.10+dfsg-1) ... #7 85.18 Setting up libxft2:amd64 (2.3.3-0ubuntu1) ... #7 85.20 Setting up libgmp-dev:amd64 (2:6.2.0+dfsg-4ubuntu0.1) ... #7 85.22 Setting up libxau-dev:amd64 (1:1.0.9-0ubuntu1) ... #7 85.24 Setting up libxi6:amd64 (2:1.7.10-0ubuntu1) ... #7 85.27 Setting up nettle-dev:amd64 (3.5.1+really3.5.1-2ubuntu0.2) ... #7 85.29 Setting up libice-dev:amd64 (2:1.0.10-0ubuntu1) ... #7 85.31 Setting up libxmlsec1-openssl:amd64 (1.2.28-2) ... #7 85.34 Setting up libxtst6:amd64 (2:1.2.3-1) ... #7 85.36 Setting up tcl8.6-dev:amd64 (8.6.10+dfsg-1) ... #7 85.38 Setting up libtk8.6:amd64 (8.6.10-1) ... #7 85.40 Setting up libreadline8:amd64 (8.0-4) ... #7 85.42 Setting up libpng-dev:amd64 (1.6.37-2) ... #7 85.45 Setting up libxxf86dga1:amd64 (2:1.1.5-0ubuntu1) ... #7 85.47 Setting up libnss3:amd64 (2:3.98-0ubuntu0.20.04.2) ... #7 85.49 Setting up libreadline-dev:amd64 (8.0-4) ... #7 85.51 Setting up libclang-cpp10 (1:10.0.0-4ubuntu1) ... #7 85.54 Setting up llvm-10-runtime (1:10.0.0-4ubuntu1) ... #7 85.56 Setting up libfreetype-dev:amd64 (2.10.1-2ubuntu0.3) ... #7 85.59 Setting up libxdmcp-dev:amd64 (1:1.1.3-0ubuntu1) ... #7 85.61 Setting up libxml2-dev:amd64 (2.9.10+dfsg-5ubuntu0.20.04.7) ... #7 85.65 Setting up libunbound8:amd64 (1.9.4-2ubuntu1.5) ... #7 85.67 Setting up libgcrypt20-dev (1.8.5-5ubuntu1.1) ... #7 85.69 Setting up x11proto-core-dev (2019.2-1ubuntu1) ... #7 85.71 Setting up llvm-runtime (1:10.0-50~exp1) ... #7 85.74 Setting up libxmlsec1-gcrypt:amd64 (1.2.28-2) ... #7 85.76 Setting up libxmlsec1-nss:amd64 (1.2.28-2) ... #7 85.78 Setting up x11proto-xext-dev (2019.2-1ubuntu1) ... #7 85.80 Setting up libdrm2:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 85.82 Setting up libnss3-dev:amd64 (2:3.98-0ubuntu0.20.04.2) ... #7 85.85 Setting up tcl (8.6.9+1) ... #7 85.87 Setting up libpython3.8-stdlib:amd64 (3.8.10-0ubuntu1~20.04.9) ... #7 85.89 Setting up python3.8 (3.8.10-0ubuntu1~20.04.9) ... #7 86.58 Setting up libxmlsec1-gnutls:amd64 (1.2.28-2) ... #7 86.61 Setting up x11proto-scrnsaver-dev (2019.2-1ubuntu1) ... #7 86.63 Setting up libsm6:amd64 (2:1.2.3-1) ... #7 86.65 Setting up libpython3-stdlib:amd64 (3.8.2-0ubuntu2) ... #7 86.67 Setting up libdrm-amdgpu1:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.69 Setting up tk8.6 (8.6.10-1) ... #7 86.72 Setting up mesa-vulkan-drivers:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 86.75 Setting up libgnutls-dane0:amd64 (3.6.13-2ubuntu1.11) ... #7 86.77 Setting up libdrm-nouveau2:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.79 Setting up libxcb1-dev:amd64 (1.14-2) ... #7 86.81 Setting up libxslt1-dev:amd64 (1.1.34-4ubuntu0.20.04.1) ... #7 86.84 Setting up libsm-dev:amd64 (2:1.2.3-1) ... #7 86.86 Setting up libdrm-radeon1:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.88 Setting up llvm-10 (1:10.0.0-4ubuntu1) ... #7 86.90 Setting up libdrm-intel1:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.93 Setting up libgl1-mesa-dri:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 86.96 Setting up libx11-dev:amd64 (2:1.6.9-2ubuntu1.6) ... #7 86.99 Setting up python3 (3.8.2-0ubuntu2) ... #7 87.18 Setting up libfreetype6-dev:amd64 (2.10.1-2ubuntu0.3) ... #7 87.21 Setting up python3-pygments (2.3.1+dfsg-1ubuntu2.2) ... #7 87.83 Setting up llvm (1:10.0-50~exp1) ... #7 87.85 Setting up libxt6:amd64 (1:1.1.5-1) ... #7 87.87 Setting up tcl-dev:amd64 (8.6.9+1) ... #7 87.90 Setting up libxext-dev:amd64 (2:1.3.4-0ubuntu1) ... #7 87.92 Setting up tk (8.6.9+1) ... #7 87.94 Setting up libxrender-dev:amd64 (1:0.9.10-1) ... #7 87.96 Setting up python3-pkg-resources (45.2.0-1ubuntu0.1) ... #7 88.28 Setting up libgnutls28-dev:amd64 (3.6.13-2ubuntu1.11) ... #7 88.31 Setting up libxmu6:amd64 (2:1.1.3-0ubuntu1) ... #7 88.33 Setting up libfontconfig1-dev:amd64 (2.13.1-2ubuntu3) ... #7 88.35 Setting up libglx-mesa0:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 88.37 Setting up libglx0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 88.40 Setting up python3-yaml (5.3.1-1ubuntu0.1) ... #7 88.69 Setting up libxaw7:amd64 (2:1.0.13-1) ... #7 88.73 Setting up libxss-dev:amd64 (1:1.2.3-1) ... #7 88.75 Setting up libgl1:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 88.77 Setting up libxt-dev:amd64 (1:1.1.5-1) ... #7 88.79 Setting up libxmlsec1-dev (1.2.28-2) ... #7 88.82 Setting up x11-utils (7.7+5) ... #7 88.93 Setting up xterm (353-1ubuntu1.20.04.2) ... #7 89.00 update-alternatives: using /usr/bin/xterm to provide /usr/bin/x-terminal-emulator (x-terminal-emulator) in auto mode #7 89.00 update-alternatives: warning: skip creation of /usr/share/man/man1/x-terminal-emulator.1.gz because associated file /usr/share/man/man1/xterm.1.gz (of link group x-terminal-emulator) doesn't exist #7 89.02 update-alternatives: using /usr/bin/lxterm to provide /usr/bin/x-terminal-emulator (x-terminal-emulator) in auto mode #7 89.02 update-alternatives: warning: skip creation of /usr/share/man/man1/x-terminal-emulator.1.gz because associated file /usr/share/man/man1/lxterm.1.gz (of link group x-terminal-emulator) doesn't exist #7 89.04 Setting up libxft-dev:amd64 (2.3.3-0ubuntu1) ... #7 89.06 Setting up llvm-10-tools (1:10.0.0-4ubuntu1) ... #7 89.08 Setting up llvm-10-dev (1:10.0.0-4ubuntu1) ... #7 89.10 Setting up tk8.6-dev:amd64 (8.6.10-1) ... #7 89.12 Setting up tk-dev:amd64 (8.6.9+1) ... #7 89.14 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #7 DONE 90.5s #8 [ 4/17] RUN git clone --depth 1 --branch $(git ls-remote --tags --sort=v:refname https://github.com/pyenv/pyenv.git | grep -o -E 'v[1-9]+(\\.[1-9]+)+$' | tail -1) https://github.com/pyenv/pyenv.git /root/.pyenv #8 0.857 Cloning into '/root/.pyenv'... #8 1.882 Note: switching to 'aabaa892438d05d96775042a129b1580fcd60105'. #8 1.882 #8 1.882 You are in 'detached HEAD' state. You can look around, make experimental #8 1.882 changes and commit them, and you can discard any commits you make in this #8 1.882 state without impacting any branches by switching back to a branch. #8 1.882 #8 1.882 If you want to create a new branch to retain commits you create, you may #8 1.882 do so (now or later) by using -c with the switch command. Example: #8 1.882 #8 1.882 git switch -c #8 1.882 #8 1.882 Or undo this operation with: #8 1.882 #8 1.882 git switch - #8 1.882 #8 1.882 Turn off this advice by setting config variable advice.detachedHead to false #8 1.882 #8 DONE 2.0s #9 [ 5/17] RUN apt install -y python3.8 python3.8-distutils && ln -s -f $(which python3.8) /usr/bin/python && wget https://bootstrap.pypa.io/get-pip.py -O /tmp/get-pip.py && python /tmp/get-pip.py #9 0.502 #9 0.502 WARNING: apt does not have a stable CLI interface. Use with caution in scripts. #9 0.502 #9 0.563 Reading package lists... #9 1.644 Building dependency tree... #9 1.808 Reading state information... #9 2.060 python3.8 is already the newest version (3.8.10-0ubuntu1~20.04.9). #9 2.060 python3.8 set to manually installed. #9 2.060 The following NEW packages will be installed: #9 2.060 python3-distutils python3-lib2to3 #9 2.331 0 upgraded, 2 newly installed, 0 to remove and 0 not upgraded. #9 2.331 Need to get 218 kB of archives. #9 2.331 After this operation, 2115 kB of additional disk space will be used. #9 2.331 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-lib2to3 all 3.8.10-0ubuntu1~20.04 [76.3 kB] #9 2.755 Get:2 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-distutils all 3.8.10-0ubuntu1~20.04 [141 kB] #9 3.105 debconf: delaying package configuration, since apt-utils is not installed #9 3.173 Fetched 218 kB in 1s (254 kB/s) #9 3.227 Selecting previously unselected package python3-lib2to3. #9 3.227 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 24296 files and directories currently installed.) #9 3.256 Preparing to unpack .../python3-lib2to3_3.8.10-0ubuntu1~20.04_all.deb ... #9 3.263 Unpacking python3-lib2to3 (3.8.10-0ubuntu1~20.04) ... #9 3.357 Selecting previously unselected package python3-distutils. #9 3.358 Preparing to unpack .../python3-distutils_3.8.10-0ubuntu1~20.04_all.deb ... #9 3.365 Unpacking python3-distutils (3.8.10-0ubuntu1~20.04) ... #9 3.486 Setting up python3-lib2to3 (3.8.10-0ubuntu1~20.04) ... #9 3.636 Setting up python3-distutils (3.8.10-0ubuntu1~20.04) ... #9 3.862 --2024-05-16 20:23:48-- https://bootstrap.pypa.io/get-pip.py #9 3.866 Resolving bootstrap.pypa.io (bootstrap.pypa.io)... 151.101.64.175, 151.101.128.175, 151.101.0.175, ... #9 3.930 Connecting to bootstrap.pypa.io (bootstrap.pypa.io)|151.101.64.175|:443... connected. #9 4.180 HTTP request sent, awaiting response... 200 OK #9 4.192 Length: 2635835 (2.5M) [text/x-python] #9 4.192 Saving to: '/tmp/get-pip.py' #9 4.206 #9 4.206 0K .......... .......... .......... .......... .......... 1% 968K 3s #9 4.244 50K .......... .......... .......... .......... .......... 3% 4.78M 2s #9 4.254 100K .......... .......... .......... .......... .......... 5% 7.08M 1s #9 4.261 150K .......... .......... .......... .......... .......... 7% 7.48M 1s #9 4.267 200K .......... .......... .......... .......... .......... 9% 6.58M 1s #9 4.275 250K .......... .......... .......... .......... .......... 11% 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Collecting setuptools #9 6.909 Downloading setuptools-69.5.1-py3-none-any.whl.metadata (6.2 kB) #9 6.992 Collecting wheel #9 7.007 Downloading wheel-0.43.0-py3-none-any.whl.metadata (2.2 kB) #9 7.077 Downloading pip-24.0-py3-none-any.whl (2.1 MB) #9 7.224 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 2.1/2.1 MB 15.0 MB/s eta 0:00:00 #9 7.243 Downloading setuptools-69.5.1-py3-none-any.whl (894 kB) #9 7.297 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 894.6/894.6 kB 18.5 MB/s eta 0:00:00 #9 7.315 Downloading wheel-0.43.0-py3-none-any.whl (65 kB) #9 7.331 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 65.8/65.8 kB 4.8 MB/s eta 0:00:00 #9 7.407 Installing collected packages: wheel, setuptools, pip #9 9.087 Successfully installed pip-24.0 setuptools-69.5.1 wheel-0.43.0 #9 9.088 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #9 DONE 9.6s #10 [ 6/17] RUN pip install virtualenv #10 1.047 Collecting virtualenv #10 1.095 Downloading virtualenv-20.26.2-py3-none-any.whl.metadata (4.4 kB) #10 1.182 Collecting distlib < 1,>=0.3.7 (from virtualenv) #10 1.197 Downloading distlib-0.3.8-py2.py3-none-any.whl.metadata (5.1 kB) #10 1.276 Collecting filelock < 4,>=3.12.2 (from virtualenv) #10 1.291 Downloading filelock-3.14.0-py3-none-any.whl.metadata (2.8 kB) #10 1.368 Collecting platformdirs < 5,>=3.9.1 (from virtualenv) #10 1.383 Downloading platformdirs-4.2.2-py3-none-any.whl.metadata (11 kB) #10 1.439 Downloading virtualenv-20.26.2-py3-none-any.whl (3.9 MB) #10 1.656 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.9/3.9 MB 18.3 MB/s eta 0:00:00 #10 1.673 Downloading distlib-0.3.8-py2.py3-none-any.whl (468 kB) #10 1.706 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 468.9/468.9 kB 17.0 MB/s eta 0:00:00 #10 1.724 Downloading filelock-3.14.0-py3-none-any.whl (12 kB) #10 1.752 Downloading platformdirs-4.2.2-py3-none-any.whl (18 kB) #10 1.871 Installing collected packages: distlib, platformdirs, filelock, virtualenv #10 2.123 Successfully installed distlib-0.3.8 filelock-3.14.0 platformdirs-4.2.2 virtualenv-20.26.2 #10 2.123 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #10 DONE 2.3s #11 [ 7/17] RUN apt-get install -y --no-install-recommends openjdk-11-jdk maven #11 0.605 Reading package lists... #11 1.699 Building dependency tree... #11 1.870 Reading state information... #11 2.070 The following additional packages will be installed: #11 2.070 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.070 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.070 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.070 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.070 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.070 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.070 libguice-java libharfbuzz0b libhawtjni-runtime-java libjansi-java #11 2.070 libjansi-native-java libjsr305-java liblcms2-2 libmaven-parent-java #11 2.070 libmaven-resolver-java libmaven-shared-utils-java libmaven3-core-java #11 2.070 libpcsclite1 libplexus-cipher-java libplexus-classworlds-java #11 2.070 libplexus-component-annotations-java libplexus-interpolation-java #11 2.070 libplexus-sec-dispatcher-java libplexus-utils2-java libsisu-inject-java #11 2.070 libsisu-plexus-java libslf4j-java libwagon-file-java #11 2.071 libwagon-http-shaded-java libwagon-provider-api-java openjdk-11-jdk-headless #11 2.071 openjdk-11-jre openjdk-11-jre-headless #11 2.072 Suggested packages: #11 2.072 default-jre libaopalliance-java-doc libasound2-plugins alsa-utils #11 2.072 libatinject-jsr330-api-java-doc libservlet3.1-java libcommons-io-java-doc #11 2.072 libcommons-lang3-java-doc cups-common libasm-java libcglib-java #11 2.072 libjsr305-java-doc liblcms2-utils libmaven-shared-utils-java-doc #11 2.072 liblogback-java pcscd libplexus-cipher-java-doc #11 2.072 libplexus-classworlds-java-doc libplexus-sec-dispatcher-java-doc #11 2.072 libplexus-utils2-java-doc junit4 testng libcommons-logging-java #11 2.072 liblog4j1.2-java openjdk-11-demo openjdk-11-source visualvm libnss-mdns #11 2.072 fonts-dejavu-extra fonts-ipafont-gothic fonts-ipafont-mincho #11 2.072 fonts-wqy-microhei | fonts-wqy-zenhei fonts-indic #11 2.072 Recommended packages: #11 2.072 alsa-ucm-conf alsa-topology-conf dbus libatk-wrapper-java-jni #11 2.072 fonts-dejavu-extra #11 2.267 The following NEW packages will be installed: #11 2.268 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.268 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.268 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.268 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.268 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.268 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.268 libguice-java libharfbuzz0b libhawtjni-runtime-java libjansi-java #11 2.268 libjansi-native-java libjsr305-java liblcms2-2 libmaven-parent-java #11 2.268 libmaven-resolver-java libmaven-shared-utils-java libmaven3-core-java #11 2.268 libpcsclite1 libplexus-cipher-java libplexus-classworlds-java #11 2.268 libplexus-component-annotations-java libplexus-interpolation-java #11 2.268 libplexus-sec-dispatcher-java libplexus-utils2-java libsisu-inject-java #11 2.268 libsisu-plexus-java libslf4j-java libwagon-file-java #11 2.269 libwagon-http-shaded-java libwagon-provider-api-java maven openjdk-11-jdk #11 2.269 openjdk-11-jdk-headless openjdk-11-jre openjdk-11-jre-headless #11 2.386 0 upgraded, 51 newly installed, 0 to remove and 0 not upgraded. #11 2.386 Need to get 125 MB of archives. #11 2.386 After this operation, 277 MB of additional disk space will be used. #11 2.386 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdbus-1-3 amd64 1.12.16-2ubuntu2.3 [179 kB] #11 2.602 Get:2 http://archive.ubuntu.com/ubuntu focal/main amd64 java-common all 0.72 [6816 B] #11 2.603 Get:3 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libavahi-common-data amd64 0.7-4ubuntu7.3 [21.4 kB] #11 2.607 Get:4 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libavahi-common3 amd64 0.7-4ubuntu7.3 [21.9 kB] #11 2.610 Get:5 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libavahi-client3 amd64 0.7-4ubuntu7.3 [25.5 kB] #11 2.615 Get:6 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcups2 amd64 2.3.1-9ubuntu1.6 [233 kB] #11 2.667 Get:7 http://archive.ubuntu.com/ubuntu focal/main amd64 liblcms2-2 amd64 2.9-4 [140 kB] #11 2.690 Get:8 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libasound2-data all 1.2.2-2.1ubuntu2.5 [20.1 kB] #11 2.694 Get:9 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libasound2 amd64 1.2.2-2.1ubuntu2.5 [335 kB] #11 2.753 Get:10 http://archive.ubuntu.com/ubuntu focal/main amd64 libgraphite2-3 amd64 1.3.13-11build1 [73.5 kB] #11 2.765 Get:11 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libharfbuzz0b amd64 2.6.4-1ubuntu4.2 [391 kB] #11 2.928 Get:12 http://archive.ubuntu.com/ubuntu focal/main amd64 libpcsclite1 amd64 1.8.26-3 [22.0 kB] #11 3.024 Get:13 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jre-headless amd64 11.0.22+7-0ubuntu2~20.04.1 [38.3 MB] #11 10.09 Get:14 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 ca-certificates-java all 20190405ubuntu1.1 [12.4 kB] #11 10.10 Get:15 http://archive.ubuntu.com/ubuntu focal/universe amd64 libapache-pom-java all 18-1 [4720 B] #11 10.10 Get:16 http://archive.ubuntu.com/ubuntu focal/universe amd64 libatinject-jsr330-api-java all 1.0+ds1-5 [5348 B] #11 10.10 Get:17 http://archive.ubuntu.com/ubuntu focal/universe amd64 libgeronimo-interceptor-3.0-spec-java all 1.0.1-4fakesync [8616 B] #11 10.10 Get:18 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcdi-api-java all 1.2-2 [54.5 kB] #11 10.10 Get:19 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcommons-cli-java all 1.4-1 [53.8 kB] #11 10.10 Get:20 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcommons-parent-java all 43-1 [10.8 kB] #11 10.10 Get:21 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libcommons-io-java all 2.6-2ubuntu0.20.04.1 [198 kB] #11 10.11 Get:22 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcommons-lang3-java all 3.8-2 [458 kB] #11 10.28 Get:23 http://archive.ubuntu.com/ubuntu focal/universe amd64 libgeronimo-annotation-1.3-spec-java all 1.0-1 [10.7 kB] #11 10.33 Get:24 http://archive.ubuntu.com/ubuntu focal/main amd64 libgif7 amd64 5.1.9-1 [32.2 kB] #11 10.38 Get:25 http://archive.ubuntu.com/ubuntu focal/universe amd64 libjsr305-java all 0.1~+svn49-11 [27.0 kB] #11 10.41 Get:26 http://archive.ubuntu.com/ubuntu focal/universe amd64 libguava-java all 19.0-1 [2028 kB] #11 10.83 Get:27 http://archive.ubuntu.com/ubuntu focal/universe amd64 libaopalliance-java all 20070526-6 [9084 B] #11 10.83 Get:28 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libguice-java all 4.2.1-1ubuntu0.2 [1410 kB] #11 11.08 Get:29 http://archive.ubuntu.com/ubuntu focal/universe amd64 libhawtjni-runtime-java all 1.17-1 [28.8 kB] #11 11.08 Get:30 http://archive.ubuntu.com/ubuntu focal/universe amd64 libjansi-native-java all 1.8-1 [23.8 kB] #11 11.08 Get:31 http://archive.ubuntu.com/ubuntu focal/universe amd64 libjansi-java all 1.18-1 [56.8 kB] #11 11.09 Get:32 http://archive.ubuntu.com/ubuntu focal/universe amd64 libmaven-parent-java all 31-2 [5140 B] #11 11.09 Get:33 http://archive.ubuntu.com/ubuntu focal/universe amd64 libplexus-utils2-java all 3.3.0-1 [250 kB] #11 11.23 Get:34 http://archive.ubuntu.com/ubuntu focal/universe amd64 libwagon-provider-api-java all 3.3.4-1 [48.5 kB] #11 11.36 Get:35 http://archive.ubuntu.com/ubuntu focal/universe amd64 libmaven-resolver-java all 1.4.1-1 [555 kB] #11 11.52 Get:36 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libmaven-shared-utils-java all 3.3.0-1ubuntu0.20.04.1 [149 kB] #11 11.55 Get:37 http://archive.ubuntu.com/ubuntu focal/universe amd64 libplexus-cipher-java all 1.7-3 [15.1 kB] #11 11.55 Get:38 http://archive.ubuntu.com/ubuntu focal/universe amd64 libplexus-classworlds-java all 2.6.0-1 [49.4 kB] #11 11.56 Get:39 http://archive.ubuntu.com/ubuntu focal/universe amd64 libplexus-component-annotations-java all 2.1.0-1 [6564 B] #11 11.56 Get:40 http://archive.ubuntu.com/ubuntu focal/universe amd64 libplexus-interpolation-java all 1.26-1 [76.8 kB] #11 11.58 Get:41 http://archive.ubuntu.com/ubuntu focal/universe amd64 libplexus-sec-dispatcher-java all 1.4-4 [28.1 kB] #11 11.58 Get:42 http://archive.ubuntu.com/ubuntu focal/universe amd64 libslf4j-java all 1.7.25-3 [141 kB] #11 11.61 Get:43 http://archive.ubuntu.com/ubuntu focal/universe amd64 libsisu-inject-java all 0.3.3-1 [347 kB] #11 11.67 Get:44 http://archive.ubuntu.com/ubuntu focal/universe amd64 libsisu-plexus-java all 0.3.3-3 [182 kB] #11 11.79 Get:45 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libmaven3-core-java all 3.6.3-1ubuntu0.1 [1535 kB] #11 12.26 Get:46 http://archive.ubuntu.com/ubuntu focal/universe amd64 libwagon-file-java all 3.3.4-1 [7928 B] #11 12.26 Get:47 http://archive.ubuntu.com/ubuntu focal/universe amd64 libwagon-http-shaded-java all 3.3.4-1 [1855 kB] #11 12.59 Get:48 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 maven all 3.6.3-1ubuntu0.1 [17.8 kB] #11 12.60 Get:49 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jre amd64 11.0.22+7-0ubuntu2~20.04.1 [194 kB] #11 12.63 Get:50 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jdk-headless amd64 11.0.22+7-0ubuntu2~20.04.1 [73.7 MB] #11 25.68 Get:51 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jdk amd64 11.0.22+7-0ubuntu2~20.04.1 [1338 kB] #11 26.04 debconf: delaying package configuration, since apt-utils is not installed #11 26.11 Fetched 125 MB in 24s (5286 kB/s) #11 26.18 Selecting previously unselected package libdbus-1-3:amd64. #11 26.18 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 24567 files and directories currently installed.) #11 26.21 Preparing to unpack .../00-libdbus-1-3_1.12.16-2ubuntu2.3_amd64.deb ... #11 26.22 Unpacking libdbus-1-3:amd64 (1.12.16-2ubuntu2.3) ... #11 26.33 Selecting previously unselected package java-common. #11 26.33 Preparing to unpack .../01-java-common_0.72_all.deb ... #11 26.34 Unpacking java-common (0.72) ... #11 26.40 Selecting previously unselected package libavahi-common-data:amd64. #11 26.41 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previously unselected package libasound2-data. #11 26.91 Preparing to unpack .../07-libasound2-data_1.2.2-2.1ubuntu2.5_all.deb ... #11 26.91 Unpacking libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 27.01 Selecting previously unselected package libasound2:amd64. #11 27.01 Preparing to unpack .../08-libasound2_1.2.2-2.1ubuntu2.5_amd64.deb ... #11 27.02 Unpacking libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 27.14 Selecting previously unselected package libgraphite2-3:amd64. #11 27.14 Preparing to unpack .../09-libgraphite2-3_1.3.13-11build1_amd64.deb ... #11 27.15 Unpacking libgraphite2-3:amd64 (1.3.13-11build1) ... #11 27.24 Selecting previously unselected package libharfbuzz0b:amd64. #11 27.24 Preparing to unpack .../10-libharfbuzz0b_2.6.4-1ubuntu4.2_amd64.deb ... #11 27.25 Unpacking libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 27.37 Selecting previously unselected package libpcsclite1:amd64. #11 27.38 Preparing to unpack .../11-libpcsclite1_1.8.26-3_amd64.deb ... #11 27.38 Unpacking 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libgeronimo-interceptor-3.0-spec-java. #11 31.24 Preparing to unpack .../16-libgeronimo-interceptor-3.0-spec-java_1.0.1-4fakesync_all.deb ... #11 31.25 Unpacking libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 31.31 Selecting previously unselected package libcdi-api-java. #11 31.31 Preparing to unpack .../17-libcdi-api-java_1.2-2_all.deb ... #11 31.32 Unpacking libcdi-api-java (1.2-2) ... #11 31.39 Selecting previously unselected package libcommons-cli-java. #11 31.39 Preparing to unpack .../18-libcommons-cli-java_1.4-1_all.deb ... #11 31.40 Unpacking libcommons-cli-java (1.4-1) ... #11 31.47 Selecting previously unselected package libcommons-parent-java. #11 31.48 Preparing to unpack .../19-libcommons-parent-java_43-1_all.deb ... #11 31.49 Unpacking libcommons-parent-java (43-1) ... #11 31.55 Selecting previously unselected package libcommons-io-java. #11 31.56 Preparing to unpack .../20-libcommons-io-java_2.6-2ubuntu0.20.04.1_all.deb ... #11 31.56 Unpacking libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 31.66 Selecting previously unselected package libcommons-lang3-java. #11 31.66 Preparing to unpack .../21-libcommons-lang3-java_3.8-2_all.deb ... #11 31.67 Unpacking libcommons-lang3-java (3.8-2) ... #11 31.78 Selecting previously unselected package libgeronimo-annotation-1.3-spec-java. #11 31.78 Preparing to unpack .../22-libgeronimo-annotation-1.3-spec-java_1.0-1_all.deb ... #11 31.79 Unpacking libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 31.86 Selecting previously unselected package libgif7:amd64. #11 31.87 Preparing to unpack .../23-libgif7_5.1.9-1_amd64.deb ... #11 31.88 Unpacking libgif7:amd64 (5.1.9-1) ... #11 31.95 Selecting previously unselected package libjsr305-java. #11 31.95 Preparing to unpack .../24-libjsr305-java_0.1~+svn49-11_all.deb ... #11 31.96 Unpacking libjsr305-java (0.1~+svn49-11) ... #11 32.03 Selecting previously unselected package libguava-java. #11 32.03 Preparing to unpack .../25-libguava-java_19.0-1_all.deb ... #11 32.04 Unpacking libguava-java (19.0-1) ... #11 32.29 Selecting previously unselected package libaopalliance-java. #11 32.29 Preparing to unpack .../26-libaopalliance-java_20070526-6_all.deb ... #11 32.30 Unpacking libaopalliance-java (20070526-6) ... #11 32.36 Selecting previously unselected package libguice-java. #11 32.37 Preparing to unpack .../27-libguice-java_4.2.1-1ubuntu0.2_all.deb ... #11 32.37 Unpacking libguice-java (4.2.1-1ubuntu0.2) ... #11 32.59 Selecting previously unselected package libhawtjni-runtime-java. #11 32.60 Preparing to unpack .../28-libhawtjni-runtime-java_1.17-1_all.deb ... #11 32.60 Unpacking libhawtjni-runtime-java (1.17-1) ... #11 32.67 Selecting previously unselected package libjansi-native-java. #11 32.68 Preparing to unpack .../29-libjansi-native-java_1.8-1_all.deb ... #11 32.68 Unpacking libjansi-native-java (1.8-1) ... #11 32.75 Selecting previously unselected package libjansi-java. #11 32.75 Preparing to unpack .../30-libjansi-java_1.18-1_all.deb ... #11 32.76 Unpacking libjansi-java (1.18-1) ... #11 32.83 Selecting previously unselected package libmaven-parent-java. #11 32.84 Preparing to unpack .../31-libmaven-parent-java_31-2_all.deb ... #11 32.85 Unpacking libmaven-parent-java (31-2) ... #11 32.91 Selecting previously unselected package libplexus-utils2-java. #11 32.91 Preparing to unpack .../32-libplexus-utils2-java_3.3.0-1_all.deb ... #11 32.92 Unpacking libplexus-utils2-java (3.3.0-1) ... #11 33.02 Selecting previously unselected package libwagon-provider-api-java. #11 33.02 Preparing to unpack .../33-libwagon-provider-api-java_3.3.4-1_all.deb ... #11 33.03 Unpacking libwagon-provider-api-java (3.3.4-1) ... #11 33.10 Selecting previously unselected package libmaven-resolver-java. #11 33.11 Preparing to unpack .../34-libmaven-resolver-java_1.4.1-1_all.deb ... #11 33.12 Unpacking libmaven-resolver-java (1.4.1-1) ... #11 33.26 Selecting previously unselected package libmaven-shared-utils-java. #11 33.27 Preparing to unpack .../35-libmaven-shared-utils-java_3.3.0-1ubuntu0.20.04.1_all.deb ... #11 33.27 Unpacking libmaven-shared-utils-java (3.3.0-1ubuntu0.20.04.1) ... #11 33.36 Selecting previously unselected package libplexus-cipher-java. #11 33.37 Preparing to unpack .../36-libplexus-cipher-java_1.7-3_all.deb ... #11 33.37 Unpacking libplexus-cipher-java (1.7-3) ... #11 33.44 Selecting previously unselected package libplexus-classworlds-java. #11 33.45 Preparing to unpack .../37-libplexus-classworlds-java_2.6.0-1_all.deb ... #11 33.45 Unpacking libplexus-classworlds-java (2.6.0-1) ... #11 33.53 Selecting previously unselected package libplexus-component-annotations-java. #11 33.53 Preparing to unpack .../38-libplexus-component-annotations-java_2.1.0-1_all.deb ... #11 33.54 Unpacking libplexus-component-annotations-java (2.1.0-1) ... #11 33.60 Selecting previously unselected package libplexus-interpolation-java. #11 33.60 Preparing to unpack .../39-libplexus-interpolation-java_1.26-1_all.deb ... #11 33.61 Unpacking libplexus-interpolation-java (1.26-1) ... #11 33.69 Selecting previously unselected package libplexus-sec-dispatcher-java. #11 33.70 Preparing to unpack .../40-libplexus-sec-dispatcher-java_1.4-4_all.deb ... #11 33.70 Unpacking libplexus-sec-dispatcher-java (1.4-4) ... #11 33.77 Selecting previously unselected package libslf4j-java. #11 33.77 Preparing to unpack .../41-libslf4j-java_1.7.25-3_all.deb ... #11 33.78 Unpacking libslf4j-java (1.7.25-3) ... #11 33.89 Selecting previously unselected package libsisu-inject-java. #11 33.89 Preparing to unpack .../42-libsisu-inject-java_0.3.3-1_all.deb ... #11 33.90 Unpacking libsisu-inject-java (0.3.3-1) ... #11 34.01 Selecting previously unselected package libsisu-plexus-java. #11 34.02 Preparing to unpack .../43-libsisu-plexus-java_0.3.3-3_all.deb ... #11 34.02 Unpacking libsisu-plexus-java (0.3.3-3) ... #11 34.12 Selecting previously unselected package libmaven3-core-java. #11 34.12 Preparing to unpack .../44-libmaven3-core-java_3.6.3-1ubuntu0.1_all.deb ... #11 34.13 Unpacking libmaven3-core-java (3.6.3-1ubuntu0.1) ... #11 34.35 Selecting previously unselected package libwagon-file-java. #11 34.36 Preparing to unpack .../45-libwagon-file-java_3.3.4-1_all.deb ... #11 34.36 Unpacking libwagon-file-java (3.3.4-1) ... #11 34.42 Selecting previously unselected package libwagon-http-shaded-java. #11 34.43 Preparing to unpack .../46-libwagon-http-shaded-java_3.3.4-1_all.deb ... #11 34.43 Unpacking libwagon-http-shaded-java (3.3.4-1) ... #11 34.68 Selecting previously unselected package maven. #11 34.68 Preparing to unpack .../47-maven_3.6.3-1ubuntu0.1_all.deb ... #11 34.69 Unpacking maven (3.6.3-1ubuntu0.1) ... #11 34.76 Selecting previously unselected package openjdk-11-jre:amd64. #11 34.76 Preparing to unpack .../48-openjdk-11-jre_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 34.77 Unpacking openjdk-11-jre:amd64 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libcommons-cli-java (1.4-1) ... #11 40.34 Setting up libplexus-component-annotations-java (2.1.0-1) ... #11 40.38 Setting up libplexus-cipher-java (1.7-3) ... #11 40.40 Setting up libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 40.42 Setting up libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 40.44 Setting up libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 40.46 Setting up libapache-pom-java (18-1) ... #11 40.48 Setting up libavahi-common-data:amd64 (0.7-4ubuntu7.3) ... #11 40.50 Setting up libatinject-jsr330-api-java (1.0+ds1-5) ... #11 40.53 Setting up libdbus-1-3:amd64 (1.12.16-2ubuntu2.3) ... #11 40.55 Setting up libplexus-interpolation-java (1.26-1) ... #11 40.57 Setting up libpcsclite1:amd64 (1.8.26-3) ... #11 40.59 Setting up libgif7:amd64 (5.1.9-1) ... #11 40.61 Setting up libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 40.63 Setting up libplexus-sec-dispatcher-java (1.4-4) ... #11 40.65 Setting up libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 40.67 Setting up libwagon-http-shaded-java (3.3.4-1) ... #11 40.69 Setting up libcdi-api-java (1.2-2) ... #11 40.71 Setting up libhawtjni-runtime-java (1.17-1) ... #11 40.73 Setting up libwagon-provider-api-java (3.3.4-1) ... #11 40.75 Setting up libmaven-parent-java (31-2) ... #11 40.77 Setting up libcommons-parent-java (43-1) ... #11 40.79 Setting up libavahi-common3:amd64 (0.7-4ubuntu7.3) ... #11 40.81 Setting up libmaven-resolver-java (1.4.1-1) ... #11 40.84 Setting up libguava-java (19.0-1) ... #11 40.86 Setting up libcommons-lang3-java (3.8-2) ... #11 40.88 Setting up libjansi-native-java (1.8-1) ... #11 40.90 Setting up libwagon-file-java (3.3.4-1) ... #11 40.92 Setting up libavahi-client3:amd64 (0.7-4ubuntu7.3) ... #11 40.94 Setting up libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 40.96 Setting up libguice-java (4.2.1-1ubuntu0.2) ... #11 40.98 Setting up libjansi-java (1.18-1) ... #11 41.00 Setting up libcups2:amd64 (2.3.1-9ubuntu1.6) ... #11 41.02 Setting up libmaven-shared-utils-java 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/usr/lib/jvm/java-11-openjdk-amd64/bin/pack200 to provide /usr/bin/pack200 (pack200) in auto mode #11 41.36 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/unpack200 to provide /usr/bin/unpack200 (unpack200) in auto mode #11 41.37 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/lib/jexec to provide /usr/bin/jexec (jexec) in auto mode #11 41.92 Setting up openjdk-11-jre:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.95 Setting up openjdk-11-jdk-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.98 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jar to provide /usr/bin/jar (jar) in auto mode #11 41.99 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jarsigner to provide /usr/bin/jarsigner (jarsigner) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javac to provide /usr/bin/javac (javac) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javadoc to provide /usr/bin/javadoc (javadoc) in auto mode #11 42.01 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javap to provide /usr/bin/javap (javap) in auto mode #11 42.02 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jcmd to provide /usr/bin/jcmd (jcmd) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdb to provide /usr/bin/jdb (jdb) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeprscan to provide /usr/bin/jdeprscan (jdeprscan) in auto mode #11 42.04 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeps to provide /usr/bin/jdeps (jdeps) in auto mode #11 42.05 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jfr to provide /usr/bin/jfr (jfr) in auto mode #11 42.06 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jimage to provide /usr/bin/jimage (jimage) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jinfo to provide /usr/bin/jinfo (jinfo) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jlink to provide /usr/bin/jlink (jlink) in auto mode #11 42.08 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmap to provide /usr/bin/jmap (jmap) in auto mode #11 42.09 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmod to provide /usr/bin/jmod (jmod) in auto mode #11 42.10 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jps to provide /usr/bin/jps (jps) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jrunscript to provide /usr/bin/jrunscript (jrunscript) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jshell to provide /usr/bin/jshell (jshell) in auto mode #11 42.12 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstack to provide /usr/bin/jstack (jstack) in auto mode #11 42.13 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstat to provide /usr/bin/jstat (jstat) in auto mode #11 42.14 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstatd to provide /usr/bin/jstatd (jstatd) in auto mode #11 42.15 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmic to provide /usr/bin/rmic (rmic) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/serialver to provide /usr/bin/serialver (serialver) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jaotc to provide /usr/bin/jaotc (jaotc) in auto mode #11 42.17 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jhsdb to provide /usr/bin/jhsdb (jhsdb) in auto mode #11 42.19 Setting up maven (3.6.3-1ubuntu0.1) ... #11 42.23 update-alternatives: using /usr/share/maven/bin/mvn to provide /usr/bin/mvn (mvn) in auto mode #11 42.25 Setting up openjdk-11-jdk:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 42.28 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jconsole to provide /usr/bin/jconsole (jconsole) in auto mode #11 42.29 Setting up ca-certificates-java (20190405ubuntu1.1) ... #11 42.35 head: cannot open '/etc/ssl/certs/java/cacerts' for reading: No such file or directory #11 42.65 Adding debian:COMODO_RSA_Certification_Authority.pem #11 42.66 Adding debian:Hongkong_Post_Root_CA_1.pem #11 42.67 Adding debian:Entrust_Root_Certification_Authority_-_EC1.pem #11 42.67 Adding debian:Certainly_Root_R1.pem #11 42.68 Adding debian:Amazon_Root_CA_2.pem #11 42.68 Adding debian:Certainly_Root_E1.pem #11 42.68 Adding debian:AffirmTrust_Premium_ECC.pem #11 42.68 Adding debian:ANF_Secure_Server_Root_CA.pem #11 42.68 Adding debian:GlobalSign_Root_R46.pem #11 42.69 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068.pem #11 42.69 Adding debian:Certum_Trusted_Root_CA.pem #11 42.69 Adding debian:Go_Daddy_Root_Certificate_Authority_-_G2.pem #11 42.69 Adding debian:SecureSign_RootCA11.pem #11 42.69 Adding debian:Amazon_Root_CA_4.pem #11 42.70 Adding debian:GDCA_TrustAUTH_R5_ROOT.pem #11 42.70 Adding debian:T-TeleSec_GlobalRoot_Class_2.pem #11 42.70 Adding debian:Trustwave_Global_ECC_P256_Certification_Authority.pem #11 42.70 Adding debian:GTS_Root_R1.pem #11 42.71 Adding debian:Izenpe.com.pem #11 42.71 Adding debian:CFCA_EV_ROOT.pem #11 42.71 Adding debian:DigiCert_Global_Root_G2.pem #11 42.71 Adding debian:XRamp_Global_CA_Root.pem #11 42.72 Adding debian:SSL.com_EV_Root_Certification_Authority_RSA_R2.pem #11 42.72 Adding debian:Security_Communication_RootCA3.pem #11 42.72 Adding debian:Starfield_Services_Root_Certificate_Authority_-_G2.pem #11 42.72 Adding debian:Amazon_Root_CA_1.pem #11 42.72 Adding debian:DigiCert_Assured_ID_Root_CA.pem #11 42.72 Adding debian:SSL.com_Root_Certification_Authority_RSA.pem #11 42.73 Adding debian:Telia_Root_CA_v2.pem #11 42.73 Adding debian:SwissSign_Gold_CA_-_G2.pem #11 42.73 Adding debian:GLOBALTRUST_2020.pem #11 42.73 Adding debian:emSign_ECC_Root_CA_-_G3.pem #11 42.73 Adding debian:Microsoft_RSA_Root_Certificate_Authority_2017.pem #11 42.74 Adding debian:TunTrust_Root_CA.pem #11 42.74 Adding debian:Security_Communication_ECC_RootCA1.pem #11 42.74 Adding debian:DigiCert_TLS_ECC_P384_Root_G5.pem #11 42.74 Adding debian:vTrus_Root_CA.pem #11 42.75 Adding debian:Certum_EC-384_CA.pem #11 42.76 Adding debian:CA_Disig_Root_R2.pem #11 42.76 Adding debian:emSign_Root_CA_-_C1.pem #11 42.77 Adding debian:AffirmTrust_Commercial.pem #11 42.77 Adding debian:Entrust_Root_Certification_Authority_-_G4.pem #11 42.78 Adding debian:Starfield_Class_2_CA.pem #11 42.78 Adding debian:Baltimore_CyberTrust_Root.pem #11 42.79 Adding debian:GTS_Root_R4.pem #11 42.79 Adding debian:Starfield_Root_Certificate_Authority_-_G2.pem #11 42.80 Adding debian:QuoVadis_Root_CA_1_G3.pem #11 42.81 Adding debian:DigiCert_Trusted_Root_G4.pem #11 42.81 Adding debian:DigiCert_High_Assurance_EV_Root_CA.pem #11 42.82 Adding debian:AC_RAIZ_FNMT-RCM.pem #11 42.82 Adding debian:Comodo_AAA_Services_root.pem #11 42.82 Adding debian:emSign_ECC_Root_CA_-_C3.pem #11 42.83 Adding debian:Entrust_Root_Certification_Authority.pem #11 42.84 Adding debian:D-TRUST_EV_Root_CA_1_2020.pem #11 42.85 Adding debian:Certigna_Root_CA.pem #11 42.85 Adding debian:TeliaSonera_Root_CA_v1.pem #11 42.86 Adding debian:Certum_Trusted_Network_CA.pem #11 42.86 Adding debian:HiPKI_Root_CA_-_G1.pem #11 42.87 Adding debian:SZAFIR_ROOT_CA2.pem #11 42.87 Adding debian:USERTrust_RSA_Certification_Authority.pem #11 42.88 Adding debian:OISTE_WISeKey_Global_Root_GC_CA.pem #11 42.88 Adding debian:Buypass_Class_2_Root_CA.pem #11 42.89 Adding debian:AC_RAIZ_FNMT-RCM_SERVIDORES_SEGUROS.pem #11 42.89 Adding debian:e-Szigno_Root_CA_2017.pem #11 42.90 Adding debian:Microsoft_ECC_Root_Certificate_Authority_2017.pem #11 42.91 Adding debian:Security_Communication_Root_CA.pem #11 42.91 Adding debian:GlobalSign_Root_CA.pem #11 42.92 Adding debian:E-Tugra_Certification_Authority.pem #11 42.93 Adding debian:Trustwave_Global_Certification_Authority.pem #11 42.93 Adding debian:QuoVadis_Root_CA_2_G3.pem #11 42.93 Adding debian:SSL.com_EV_Root_Certification_Authority_ECC.pem #11 42.94 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068_2.pem #11 42.95 Adding debian:E-Tugra_Global_Root_CA_ECC_v3.pem #11 42.95 Adding debian:Entrust.net_Premium_2048_Secure_Server_CA.pem #11 42.96 Adding debian:AffirmTrust_Premium.pem #11 42.96 Adding debian:Amazon_Root_CA_3.pem #11 42.97 Adding debian:AffirmTrust_Networking.pem #11 42.98 Adding debian:IdenTrust_Public_Sector_Root_CA_1.pem #11 42.98 Adding debian:GlobalSign_ECC_Root_CA_-_R4.pem #11 42.99 Adding debian:QuoVadis_Root_CA_3_G3.pem #11 42.99 Adding debian:certSIGN_ROOT_CA.pem #11 43.00 Adding debian:IdenTrust_Commercial_Root_CA_1.pem #11 43.00 Adding debian:SecureTrust_CA.pem #11 43.01 Adding debian:DigiCert_Global_Root_G3.pem #11 43.01 Adding debian:GlobalSign_Root_E46.pem #11 43.02 Adding debian:SwissSign_Silver_CA_-_G2.pem #11 43.02 Adding debian:vTrus_ECC_Root_CA.pem #11 43.03 Adding debian:GlobalSign_ECC_Root_CA_-_R5.pem #11 43.04 Adding debian:OISTE_WISeKey_Global_Root_GB_CA.pem #11 43.04 Adding debian:DigiCert_Assured_ID_Root_G3.pem #11 43.05 Adding debian:emSign_Root_CA_-_G1.pem #11 43.05 Adding debian:DigiCert_TLS_RSA4096_Root_G5.pem #11 43.06 Adding debian:COMODO_Certification_Authority.pem #11 43.06 Adding debian:DigiCert_Global_Root_CA.pem #11 43.07 Adding debian:QuoVadis_Root_CA_2.pem #11 43.08 Adding debian:TWCA_Global_Root_CA.pem #11 43.09 Adding debian:ACCVRAIZ1.pem #11 43.09 Adding debian:GTS_Root_R2.pem #11 43.10 Adding debian:HARICA_TLS_RSA_Root_CA_2021.pem #11 43.10 Adding debian:Actalis_Authentication_Root_CA.pem #11 43.11 Adding debian:Certum_Trusted_Network_CA_2.pem #11 43.11 Adding debian:Atos_TrustedRoot_2011.pem #11 43.11 Adding debian:T-TeleSec_GlobalRoot_Class_3.pem #11 43.13 Adding debian:NetLock_Arany_=Class_Gold=_F\u0151tan\u00fas\u00edtv\u00e1ny.pem #11 43.13 Adding debian:Certigna.pem #11 43.14 Adding debian:Microsec_e-Szigno_Root_CA_2009.pem #11 43.14 Adding debian:UCA_Extended_Validation_Root.pem #11 43.14 Adding debian:DigiCert_Assured_ID_Root_G2.pem #11 43.15 Adding debian:QuoVadis_Root_CA_3.pem #11 43.15 Adding debian:NAVER_Global_Root_Certification_Authority.pem #11 43.16 Adding debian:GlobalSign_Root_CA_-_R6.pem #11 43.17 Adding debian:TUBITAK_Kamu_SM_SSL_Kok_Sertifikasi_-_Surum_1.pem #11 43.17 Adding debian:Trustwave_Global_ECC_P384_Certification_Authority.pem #11 43.18 Adding debian:Go_Daddy_Class_2_CA.pem #11 43.18 Adding debian:TWCA_Root_Certification_Authority.pem #11 43.19 Adding debian:certSIGN_Root_CA_G2.pem #11 43.19 Adding debian:COMODO_ECC_Certification_Authority.pem #11 43.20 Adding debian:ISRG_Root_X2.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_EV_2009.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_2009.pem #11 43.22 Adding debian:UCA_Global_G2_Root.pem #11 43.22 Adding debian:D-TRUST_BR_Root_CA_1_2020.pem #11 43.23 Adding debian:Secure_Global_CA.pem #11 43.24 Adding debian:Hongkong_Post_Root_CA_3.pem #11 43.24 Adding debian:Buypass_Class_3_Root_CA.pem #11 43.25 Adding debian:Entrust_Root_Certification_Authority_-_G2.pem #11 43.25 Adding debian:SSL.com_Root_Certification_Authority_ECC.pem #11 43.26 Adding debian:Hellenic_Academic_and_Research_Institutions_RootCA_2015.pem #11 43.27 Adding debian:E-Tugra_Global_Root_CA_RSA_v3.pem #11 43.27 Adding debian:USERTrust_ECC_Certification_Authority.pem #11 43.28 Adding debian:ePKI_Root_Certification_Authority.pem #11 43.28 Adding debian:ISRG_Root_X1.pem #11 43.29 Adding debian:HARICA_TLS_ECC_Root_CA_2021.pem #11 43.29 Adding debian:GTS_Root_R3.pem #11 43.30 Adding debian:Hellenic_Academic_and_Research_Institutions_ECC_RootCA_2015.pem #11 43.30 Adding debian:Security_Communication_RootCA2.pem #11 43.31 Adding debian:GlobalSign_Root_CA_-_R3.pem #11 43.35 done. #11 43.36 Processing triggers for mime-support (3.64ubuntu1) ... #11 43.40 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #11 43.45 Processing triggers for ca-certificates (20230311ubuntu0.20.04.1) ... #11 43.47 Updating certificates in /etc/ssl/certs... #11 44.82 0 added, 0 removed; done. #11 44.82 Running hooks in /etc/ca-certificates/update.d... #11 44.83 #11 45.16 done. #11 45.16 done. #11 DONE 45.9s #12 [ 8/17] WORKDIR /opt/mlflow #12 DONE 0.1s #13 [ 9/17] RUN pip install mlflow==2.12.2 #13 1.009 Collecting mlflow==2.12.2 #13 1.075 Downloading mlflow-2.12.2-py3-none-any.whl.metadata (29 kB) #13 1.207 Collecting Flask < 4 (from mlflow==2.12.2) #13 1.222 Downloading flask-3.0.3-py3-none-any.whl.metadata (3.2 kB) #13 1.309 Collecting alembic!=1.10.0, < 2 (from mlflow==2.12.2) #13 1.324 Downloading alembic-1.13.1-py3-none-any.whl.metadata (7.4 kB) #13 1.395 Collecting click < 9,>=7.0 (from mlflow==2.12.2) #13 1.409 Downloading click-8.1.7-py3-none-any.whl.metadata (3.0 kB) #13 1.467 Collecting cloudpickle < 4 (from mlflow==2.12.2) #13 1.483 Downloading cloudpickle-3.0.0-py3-none-any.whl.metadata (7.0 kB) #13 1.564 Collecting docker < 8,>=4.0.0 (from mlflow==2.12.2) #13 1.579 Downloading docker-7.0.0-py3-none-any.whl.metadata (3.5 kB) #13 1.635 Collecting entrypoints < 1 (from mlflow==2.12.2) #13 1.651 Downloading entrypoints-0.4-py3-none-any.whl.metadata (2.6 kB) #13 1.742 Collecting gitpython < 4,>=3.1.9 (from mlflow==2.12.2) #13 1.757 Downloading GitPython-3.1.43-py3-none-any.whl.metadata (13 kB) #13 1.822 Collecting graphene < 4 (from mlflow==2.12.2) #13 1.837 Downloading graphene-3.3-py2.py3-none-any.whl.metadata (7.7 kB) #13 1.946 Collecting importlib-metadata!=4.7.0, < 8,>=3.7.0 (from mlflow==2.12.2) #13 1.960 Downloading importlib_metadata-7.1.0-py3-none-any.whl.metadata (4.7 kB) #13 2.027 Collecting markdown < 4,>=3.3 (from mlflow==2.12.2) #13 2.079 Downloading Markdown-3.6-py3-none-any.whl.metadata (7.0 kB) #13 2.299 Collecting matplotlib < 4 (from mlflow==2.12.2) #13 2.314 Downloading matplotlib-3.7.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (5.7 kB) #13 2.593 Collecting numpy < 2 (from mlflow==2.12.2) #13 2.612 Downloading numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB) #13 2.685 Collecting packaging < 25 (from mlflow==2.12.2) #13 2.698 Downloading packaging-24.0-py3-none-any.whl.metadata (3.2 kB) #13 2.902 Collecting pandas < 3 (from mlflow==2.12.2) #13 2.916 Downloading pandas-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB) #13 3.172 Collecting protobuf < 5,>=3.12.0 (from mlflow==2.12.2) #13 3.187 Downloading protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes) #13 3.323 Collecting pyarrow < 16,>=4.0.0 (from mlflow==2.12.2) #13 3.338 Downloading pyarrow-15.0.2-cp38-cp38-manylinux_2_28_x86_64.whl.metadata (3.0 kB) #13 3.456 Collecting pytz < 2025 (from mlflow==2.12.2) #13 3.471 Downloading pytz-2024.1-py2.py3-none-any.whl.metadata (22 kB) #13 3.482 Requirement already satisfied: pyyaml < 7,>=5.1 in /usr/lib/python3/dist-packages (from mlflow==2.12.2) (5.3.1) #13 3.672 Collecting querystring-parser < 2 (from mlflow==2.12.2) #13 3.689 Downloading querystring_parser-1.2.4-py2.py3-none-any.whl.metadata (559 bytes) #13 3.780 Collecting requests < 3,>=2.17.3 (from mlflow==2.12.2) #13 3.794 Downloading requests-2.31.0-py3-none-any.whl.metadata (4.6 kB) #13 3.969 Collecting scikit-learn < 2 (from mlflow==2.12.2) #13 3.985 Downloading scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB) #13 4.159 Collecting scipy < 2 (from mlflow==2.12.2) #13 4.175 Downloading scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (58 kB) #13 4.199 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 58.9/58.9 kB 2.7 MB/s eta 0:00:00 #13 4.633 Collecting sqlalchemy < 3,>=1.4.0 (from mlflow==2.12.2) #13 4.648 Downloading SQLAlchemy-2.0.30-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.6 kB) #13 4.709 Collecting sqlparse < 1,>=0.4.0 (from mlflow==2.12.2) #13 4.724 Downloading sqlparse-0.5.0-py3-none-any.whl.metadata (3.9 kB) #13 4.795 Collecting Jinja2 < 4,>=2.11 (from mlflow==2.12.2) #13 4.809 Downloading jinja2-3.1.4-py3-none-any.whl.metadata (2.6 kB) #13 4.929 Collecting gunicorn < 23 (from mlflow==2.12.2) #13 4.944 Downloading gunicorn-22.0.0-py3-none-any.whl.metadata (4.4 kB) #13 5.043 Collecting Mako (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.058 Downloading Mako-1.3.5-py3-none-any.whl.metadata (2.9 kB) #13 5.122 Collecting typing-extensions>=4 (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.136 Downloading typing_extensions-4.11.0-py3-none-any.whl.metadata (3.0 kB) #13 5.225 Collecting importlib-resources (from alembic!=1.10.0, < 2->mlflow==2.12.2) #13 5.241 Downloading importlib_resources-6.4.0-py3-none-any.whl.metadata (3.9 kB) #13 5.348 Collecting urllib3>=1.26.0 (from docker < 8,>=4.0.0->mlflow==2.12.2) #13 5.362 Downloading urllib3-2.2.1-py3-none-any.whl.metadata (6.4 kB) #13 5.445 Collecting Werkzeug>=3.0.0 (from Flask < 4->mlflow==2.12.2) #13 5.460 Downloading werkzeug-3.0.3-py3-none-any.whl.metadata (3.7 kB) #13 5.519 Collecting itsdangerous>=2.1.2 (from Flask < 4->mlflow==2.12.2) #13 5.537 Downloading itsdangerous-2.2.0-py3-none-any.whl.metadata (1.9 kB) #13 5.606 Collecting blinker>=1.6.2 (from Flask < 4->mlflow==2.12.2) #13 5.623 Downloading blinker-1.8.2-py3-none-any.whl.metadata (1.6 kB) #13 5.716 Collecting gitdb < 5,>=4.0.1 (from gitpython < 4,>=3.1.9->mlflow==2.12.2) #13 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fonttools, entrypoints, cycler, cloudpickle, click, charset-normalizer, certifi, blinker, Werkzeug, sqlalchemy, scipy, requests, querystring-parser, python-dateutil, pyarrow, Mako, Jinja2, importlib-resources, importlib-metadata, gunicorn, graphql-relay, gitdb, contourpy, scikit-learn, pandas, matplotlib, markdown, graphene, gitpython, Flask, docker, alembic, mlflow #13 34.72 Successfully installed Flask-3.0.3 Jinja2-3.1.4 Mako-1.3.5 MarkupSafe-2.1.5 Werkzeug-3.0.3 alembic-1.13.1 aniso8601-9.0.1 blinker-1.8.2 certifi-2024.2.2 charset-normalizer-3.3.2 click-8.1.7 cloudpickle-3.0.0 contourpy-1.1.1 cycler-0.12.1 docker-7.0.0 entrypoints-0.4 fonttools-4.51.0 gitdb-4.0.11 gitpython-3.1.43 graphene-3.3 graphql-core-3.2.3 graphql-relay-3.2.0 greenlet-3.0.3 gunicorn-22.0.0 idna-3.7 importlib-metadata-7.1.0 importlib-resources-6.4.0 itsdangerous-2.2.0 joblib-1.4.2 kiwisolver-1.4.5 markdown-3.6 matplotlib-3.7.5 mlflow-2.12.2 numpy-1.24.4 packaging-24.0 pandas-2.0.3 pillow-10.3.0 protobuf-4.25.3 pyarrow-15.0.2 pyparsing-3.1.2 python-dateutil-2.9.0.post0 pytz-2024.1 querystring-parser-1.2.4 requests-2.31.0 scikit-learn-1.3.2 scipy-1.10.1 six-1.16.0 smmap-5.0.1 sqlalchemy-2.0.30 sqlparse-0.5.0 threadpoolctl-3.5.0 typing-extensions-4.11.0 tzdata-2024.1 urllib3-2.2.1 zipp-3.18.2 #13 34.72 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. 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12.86 [INFO] Finished at: 2024-05-16T20:25:32Z #14 12.86 [INFO] ------------------------------------------------------------------------ #14 DONE 13.0s #15 [11/17] RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.12.2:jar -DoutputDirectory=/opt/java/jars #15 1.696 [INFO] Scanning for projects... #15 1.897 [INFO] #15 1.897 [INFO] ------------------ < org.apache.maven:standalone-pom >------------------- #15 1.898 [INFO] Building Maven Stub Project (No POM) 1 #15 1.898 [INFO] --------------------------------[ pom ]--------------------------------- #15 1.905 [INFO] #15 1.905 [INFO] --- maven-dependency-plugin:2.8:copy (default-cli) @ standalone-pom --- #15 2.594 [INFO] Configured Artifact: org.mlflow:mlflow-scoring:2.12.2:jar #15 2.804 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/mlflow/mlflow-scoring/2.12.2/mlflow-scoring-2.12.2.jar #15 3.207 [INFO] Downloaded from central: 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maven-plugin-registry-2.2.1.jar to /opt/java/jars/maven-plugin-registry-2.2.1.jar #17 134.5 [INFO] Copying backport-util-concurrent-3.1.jar to /opt/java/jars/backport-util-concurrent-3.1.jar #17 134.5 [INFO] Copying wagon-http-shared-1.0-beta-6.jar to /opt/java/jars/wagon-http-shared-1.0-beta-6.jar #17 134.5 [INFO] Copying commons-httpclient-3.1.jar to /opt/java/jars/commons-httpclient-3.1.jar #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] BUILD SUCCESS #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] Total time: 02:12 min #17 134.5 [INFO] Finished at: 2024-05-16T20:27:50Z #17 134.5 [INFO] ------------------------------------------------------------------------ #17 DONE 134.8s #18 [14/17] COPY model_dir/success6g_model /opt/ml/model #18 DONE 0.1s #19 [15/17] RUN python -c \"from mlflow.models import container as C; C._install_pyfunc_deps('/opt/ml/model', install_mlflow=False, enable_mlserver=True, env_manager='virtualenv');\" #19 1.285 2024/05/16 20:27:52 INFO mlflow.models.container: creating and activating custom environment #19 1.289 2024/05/16 20:27:52 INFO mlflow.utils.virtualenv: Installing python 3.10.11 if it does not exist #19 1.673 Downloading Python-3.10.11.tar.xz... #19 1.673 -> https://www.python.org/ftp/python/3.10.11/Python-3.10.11.tar.xz #19 4.792 Installing Python-3.10.11... #19 108.0 Installed Python-3.10.11 to /root/.pyenv/versions/3.10.11 #19 108.4 2024/05/16 20:29:39 INFO mlflow.utils.virtualenv: Creating a new environment in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b with /root/.pyenv/versions/3.10.11/bin/python #19 109.3 created virtual environment CPython3.10.11.final.0-64 in 719ms #19 109.3 creator CPython3Posix(dest=/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b, clear=False, no_vcs_ignore=False, global=False) #19 109.3 seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/.local/share/virtualenv) #19 109.3 added seed packages: pip==24.0, setuptools==69.5.1, wheel==0.43.0 #19 109.3 activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator #19 109.4 2024/05/16 20:29:40 INFO mlflow.utils.virtualenv: Installing dependencies #19 168.4 WARNING: There was an error checking the latest version of pip. #19 169.3 Collecting mlserver!=1.3.1, < 1.4.0,>=1.2.0 #19 169.4 Downloading mlserver-1.3.5-py3-none-any.whl (113 kB) #19 169.4 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 113.2/113.2 kB 3.4 MB/s eta 0:00:00 #19 169.6 Collecting mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0 #19 169.6 Downloading mlserver_mlflow-1.3.5-py3-none-any.whl (10 kB) #19 169.7 Requirement already satisfied: click in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) (8.1.3) #19 169.8 Collecting fastapi!=0.89.0, < =0.89.1,>=0.88.0 (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.8 Downloading fastapi-0.89.1-py3-none-any.whl (55 kB) #19 169.8 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 55.8/55.8 kB 7.4 MB/s eta 0:00:00 #19 169.9 Collecting python-dotenv (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.9 Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB) #19 171.0 Collecting grpcio (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.0 Downloading grpcio-1.63.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB) #19 171.3 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1.4.0,>=1.2.0) (4.25.3) #19 171.5 Collecting uvicorn (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.5 Downloading uvicorn-0.29.0-py3-none-any.whl (60 kB) #19 171.6 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 60.8/60.8 kB 8.7 MB/s eta 0:00:00 #19 171.7 Collecting starlette-exporter (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.7 Downloading starlette_exporter-0.21.0-py3-none-any.whl (14 kB) #19 171.9 Collecting py-grpc-prometheus (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.9 Downloading py_grpc_prometheus-0.8.0-py3-none-any.whl (12 kB) #19 172.1 Collecting aiokafka (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 172.1 Downloading aiokafka-0.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB) #19 172.2 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orjson-3.10.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (142 kB) #19 173.6 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 142.5/142.5 kB 19.4 MB/s eta 0:00:00 #19 173.7 Collecting uvloop (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 173.7 Downloading uvloop-0.19.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.4 MB) #19 173.8 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.4/3.4 MB 19.8 MB/s eta 0:00:00 #19 173.9 Requirement already satisfied: mlflow in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (2.12.2) #19 174.2 Collecting pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1, < 2.0.0,>=1.6.2 (from fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 174.2 Downloading pydantic-1.10.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB) #19 174.4 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.1/3.1 MB 19.8 MB/s eta 0:00:00 #19 174.5 Collecting starlette==0.22.0 (from fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 174.5 Downloading starlette-0.22.0-py3-none-any.whl (64 kB) #19 174.5 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0:00:00 #19 176.1 Collecting geventhttpclient < =2.0.2,>=1.4.4 (from tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.2 Downloading geventhttpclient-2.0.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (100 kB) #19 176.2 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.3/100.3 kB 10.5 MB/s eta 0:00:00 #19 176.3 Requirement already satisfied: async-timeout in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from aiokafka->mlserver!=1.3.1, < 1.4.0,>=1.2.0) (4.0.3) #19 176.3 Requirement already satisfied: packaging in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from aiokafka->mlserver!=1.3.1, < 1.4.0,>=1.2.0) (23.1) #19 176.4 Requirement 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mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (2.0.30) #19 176.4 Requirement already satisfied: sqlparse < 1,>=0.4.0 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (0.5.0) #19 176.4 Requirement already satisfied: Jinja2 < 4,>=2.11 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.1.2) #19 176.4 Requirement already satisfied: gunicorn < 23 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (22.0.0) #19 176.5 Requirement already satisfied: python-dateutil>=2.8.2 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from pandas->mlserver!=1.3.1, < 1.4.0,>=1.2.0) (2.9.0.post0) #19 176.5 Requirement already satisfied: 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requirements. This could take a while. #19 176.6 Collecting starlette-exporter (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.7 Downloading starlette_exporter-0.20.0-py3-none-any.whl (14 kB) #19 176.7 Downloading starlette_exporter-0.19.0-py3-none-any.whl (14 kB) #19 176.7 Downloading starlette_exporter-0.18.0-py3-none-any.whl (14 kB) #19 176.8 Downloading starlette_exporter-0.17.1-py3-none-any.whl (14 kB) #19 176.8 Collecting h11>=0.8 (from uvicorn->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.8 Downloading h11-0.14.0-py3-none-any.whl (58 kB) #19 176.9 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 58.3/58.3 kB 8.0 MB/s eta 0:00:00 #19 176.9 Requirement already satisfied: typing-extensions>=4.0 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from uvicorn->mlserver!=1.3.1, < 1.4.0,>=1.2.0) (4.11.0) #19 176.9 Collecting aiosignal>=1.1.2 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.9 Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB) #19 177.0 Collecting attrs>=17.3.0 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 177.0 Downloading attrs-23.2.0-py3-none-any.whl (60 kB) #19 177.0 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 60.8/60.8 kB 8.7 MB/s eta 0:00:00 #19 177.2 Collecting frozenlist>=1.1.1 (from aiohttp < 4.0.0,>=3.8.1->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 177.2 Downloading frozenlist-1.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (239 kB) #19 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yarl-1.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (301 kB) #19 178.0 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 301.6/301.6 kB 16.3 MB/s eta 0:00:00 #19 178.0 Requirement already satisfied: Mako in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from alembic!=1.10.0, < 2->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (1.3.5) #19 178.1 Requirement already satisfied: Werkzeug>=3.0.0 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from Flask < 4->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.0.3) #19 178.1 Requirement already satisfied: itsdangerous>=2.1.2 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from Flask < 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/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from importlib-metadata!=4.7.0, < 8,>=3.7.0->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.18.2) #19 179.2 Requirement already satisfied: MarkupSafe>=2.0 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from Jinja2 < 4,>=2.11->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (2.1.5) #19 179.2 Requirement already satisfied: contourpy>=1.0.1 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from matplotlib < 4->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (1.2.1) #19 179.2 Requirement already satisfied: cycler>=0.10 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from matplotlib < 4->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (0.12.1) #19 179.2 Requirement already satisfied: fonttools>=4.22.0 in 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/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from sqlalchemy < 3,>=1.4.0->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.0.3) #19 179.6 Collecting sniffio>=1.1 (from anyio < 5,>=3.4.0->starlette==0.22.0->fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 179.6 Downloading sniffio-1.3.1-py3-none-any.whl (10 kB) #19 179.7 Collecting exceptiongroup>=1.0.2 (from anyio < 5,>=3.4.0->starlette==0.22.0->fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 179.7 Downloading exceptiongroup-1.2.1-py3-none-any.whl (16 kB) #19 179.9 Collecting zope.event (from gevent>=0.13->geventhttpclient < =2.0.2,>=1.4.4->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 180.0 Downloading zope.event-5.0-py3-none-any.whl (6.8 kB) #19 180.2 Collecting zope.interface (from gevent>=0.13->geventhttpclient < =2.0.2,>=1.4.4->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 180.2 Downloading zope.interface-6.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (247 kB) #19 180.2 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 247.5/247.5 kB 31.3 MB/s eta 0:00:00 #19 180.2 Requirement already satisfied: smmap < 6,>=3.0.1 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from gitdb < 5,>=4.0.1->gitpython < 4,>=3.1.9->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (5.0.1) #19 181.2 Installing collected packages: brotli, zope.interface, zope.event, uvloop, sniffio, python-rapidjson, python-dotenv, pydantic, prometheus-client, orjson, multidict, importlib-resources, h11, grpcio, frozenlist, exceptiongroup, attrs, aiokafka, aiofiles, yarl, uvicorn, tritonclient, py-grpc-prometheus, gevent, anyio, aiosignal, starlette, geventhttpclient, aiohttp, starlette-exporter, fastapi, mlserver, mlserver-mlflow #19 184.4 Successfully installed aiofiles-23.2.1 aiohttp-3.9.5 aiokafka-0.10.0 aiosignal-1.3.1 anyio-4.3.0 attrs-23.2.0 brotli-1.1.0 exceptiongroup-1.2.1 fastapi-0.89.1 frozenlist-1.4.1 gevent-24.2.1 geventhttpclient-2.0.2 grpcio-1.63.0 h11-0.14.0 importlib-resources-6.4.0 mlserver-1.3.5 mlserver-mlflow-1.3.5 multidict-6.0.5 orjson-3.10.3 prometheus-client-0.20.0 py-grpc-prometheus-0.8.0 pydantic-1.10.15 python-dotenv-1.0.1 python-rapidjson-1.16 sniffio-1.3.1 starlette-0.22.0 starlette-exporter-0.17.1 tritonclient-2.45.0 uvicorn-0.29.0 uvloop-0.19.0 yarl-1.9.4 zope.event-5.0 zope.interface-6.4 #19 184.4 WARNING: There was an error checking the latest version of pip. #19 DONE 186.7s #20 [16/17] RUN chmod o+rwX /opt/mlflow/ #20 DONE 0.4s #21 [17/17] RUN rm -rf /var/lib/apt/lists/* #21 DONE 0.6s #22 exporting to image #22 exporting layers #22 exporting layers 12.0s done #22 writing image sha256:29875270957bd037ee2b833cf9201a001d353e4fef34b1d8c4923b278ef8ac02 done #22 naming to docker.io/5uperpalo/mlflow-success6g done #22 DONE 12.1s ```","title":"04_model_training_pipeline"},{"location":"notebooks/04_model_training_pipeline.html#example-usage-notebook","text":"Objects and functions in this notebook are listed with all paramaters to ilustrate their capabillities. Most of the paramaters have default values in the implementation","title":"Example usage notebook"},{"location":"notebooks/04_model_training_pipeline.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd import mlflow pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) os . environ [ \"AWS_ACCESS_KEY_ID\" ] = \"minioadmin\" os . environ [ \"AWS_SECRET_ACCESS_KEY\" ] = \"minioadmin\" os . environ [ \"MLFLOW_S3_ENDPOINT_URL\" ] = f \"http://10.152.183.156:9000\" import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd import mlflow pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\" In [2]: Copied! import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump , dill_load from inference_model.training.utils import flatten_dict , get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast import os import json from typing import Literal import pandas as pd from inference_model.preprocessing.preprocess import PreprocessData from inference_model.training.trainer import Trainer from inference_model.training.optuna_optimizer import LGBOptunaOptimizer import dill import numpy as np from inference_model.utils import dill_dump, dill_load from inference_model.training.utils import flatten_dict, get_or_create_experiment from sklearn.model_selection import train_test_split from pprint import pprint import ast","title":"Imports"},{"location":"notebooks/04_model_training_pipeline.html#train-a-testing-model","text":"In [3]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 df_pd . head () # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 df_pd.head() Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc 2 100.0 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 1 123abc 3 100.0 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 1 123abc 4 100.0 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 1 123abc In [4]: Copied! target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) In [6]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) In [7]: Copied! prepare_data = PreprocessData ( id_cols = id_cols , target_col = target_col , cat_cols = cat_cols , cont_cols = cont_cols , ) # this should be fitted only on training data _ = prepare_data . fit ( df = df_pd ) prepare_data = PreprocessData( id_cols=id_cols, target_col=target_col, cat_cols=cat_cols, cont_cols=cont_cols, ) # this should be fitted only on training data _ = prepare_data.fit(df=df_pd) In [8]: Copied! optimizer = LGBOptunaOptimizer ( objective = \"binary\" , n_class = 2 , ) trainer = Trainer ( cat_cols = prepare_data . cat_cols , target_col = prepare_data . target_col , id_cols = id_cols , objective = \"binary\" , n_class = 2 , optimizer = optimizer , preprocessors = [ prepare_data ], ) metrics_dict = trainer . fit ( df_train = df_train , df_valid = df_valid , df_test = df_test , ) optimizer = LGBOptunaOptimizer( objective=\"binary\", n_class=2, ) trainer = Trainer( cat_cols=prepare_data.cat_cols, target_col=prepare_data.target_col, id_cols=id_cols, objective=\"binary\", n_class=2, optimizer=optimizer, preprocessors=[prepare_data], ) metrics_dict = trainer.fit( df_train=df_train, df_valid=df_valid, df_test=df_test, ) [I 2024-05-22 13:01:18,589] A new study created in memory with name: LightGBMTuner feature_fraction, val_score: inf: 0%| | 0/7 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000002: 14%|#4 | 1/7 [00:03<00:21, 3.62s/it][I 2024-05-22 13:01:22,217] Trial 0 finished with value: 1.6789085046471396e-06 and parameters: {'feature_fraction': 0.4}. Best is trial 0 with value: 1.6789085046471396e-06. feature_fraction, val_score: 0.000002: 14%|#4 | 1/7 [00:03<00:21, 3.62s/it] Early stopping, best iteration is: [588] valid_0's binary_logloss: 1.67891e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000002: 29%|##8 | 2/7 [00:05<00:13, 2.72s/it][I 2024-05-22 13:01:24,308] Trial 1 finished with value: 1.9702431647601935e-06 and parameters: {'feature_fraction': 0.8999999999999999}. Best is trial 0 with value: 1.6789085046471396e-06. feature_fraction, val_score: 0.000002: 29%|##8 | 2/7 [00:05<00:13, 2.72s/it] Early stopping, best iteration is: [380] valid_0's binary_logloss: 1.97024e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 43%|####2 | 3/7 [00:06<00:06, 1.71s/it][I 2024-05-22 13:01:24,812] Trial 2 finished with value: 1.3616097829410516e-06 and parameters: {'feature_fraction': 1.0}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 43%|####2 | 3/7 [00:06<00:06, 1.71s/it] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 57%|#####7 | 4/7 [00:07<00:04, 1.40s/it][I 2024-05-22 13:01:25,731] Trial 3 finished with value: 2.2594885710041037e-06 and parameters: {'feature_fraction': 0.6}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 57%|#####7 | 4/7 [00:07<00:04, 1.40s/it] Early stopping, best iteration is: [794] valid_0's binary_logloss: 2.25949e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 71%|#######1 | 5/7 [00:08<00:02, 1.21s/it][I 2024-05-22 13:01:26,610] Trial 4 finished with value: 2.5490428264265304e-06 and parameters: {'feature_fraction': 0.8}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 71%|#######1 | 5/7 [00:08<00:02, 1.21s/it] Early stopping, best iteration is: [485] valid_0's binary_logloss: 2.54904e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 86%|########5 | 6/7 [00:09<00:01, 1.14s/it][I 2024-05-22 13:01:27,606] Trial 5 finished with value: 2.2594885710041037e-06 and parameters: {'feature_fraction': 0.5}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 86%|########5 | 6/7 [00:09<00:01, 1.14s/it] Early stopping, best iteration is: [794] valid_0's binary_logloss: 2.25949e-06 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.000001: 100%|##########| 7/7 [00:10<00:00, 1.10s/it][I 2024-05-22 13:01:28,615] Trial 6 finished with value: 2.5249718256638313e-06 and parameters: {'feature_fraction': 0.7}. Best is trial 2 with value: 1.3616097829410516e-06. feature_fraction, val_score: 0.000001: 100%|##########| 7/7 [00:10<00:00, 1.43s/it] Early stopping, best iteration is: [715] valid_0's binary_logloss: 2.52497e-06 num_leaves, val_score: 0.000001: 0%| | 0/20 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 5%|5 | 1/20 [00:00<00:09, 1.99it/s][I 2024-05-22 13:01:29,127] Trial 7 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 227}. Best is trial 7 with value: 1.3616097829410516e-06. num_leaves, val_score: 0.000001: 5%|5 | 1/20 [00:00<00:09, 1.99it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 10%|# | 2/20 [00:00<00:08, 2.01it/s][I 2024-05-22 13:01:29,623] Trial 8 finished with value: 1.361609811862523e-06 and parameters: {'num_leaves': 14}. Best is trial 7 with value: 1.3616097829410516e-06. num_leaves, val_score: 0.000001: 10%|# | 2/20 [00:01<00:08, 2.01it/s] Early stopping, best iteration is: [390] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 15%|#5 | 3/20 [00:01<00:08, 2.00it/s][I 2024-05-22 13:01:30,125] Trial 9 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 90}. Best is trial 7 with value: 1.3616097829410516e-06. num_leaves, val_score: 0.000001: 15%|#5 | 3/20 [00:01<00:08, 2.00it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 20%|## | 4/20 [00:02<00:08, 1.88it/s][I 2024-05-22 13:01:30,707] Trial 10 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 255}. Best is trial 10 with value: 1.3616097829410512e-06. num_leaves, val_score: 0.000001: 20%|## | 4/20 [00:02<00:08, 1.88it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 25%|##5 | 5/20 [00:02<00:07, 1.89it/s][I 2024-05-22 13:01:31,227] Trial 11 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 252}. Best is trial 10 with value: 1.3616097829410512e-06. num_leaves, val_score: 0.000001: 25%|##5 | 5/20 [00:02<00:07, 1.89it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 30%|### | 6/20 [00:03<00:08, 1.70it/s][I 2024-05-22 13:01:31,929] Trial 12 finished with value: 1.361609782941051e-06 and parameters: {'num_leaves': 161}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 30%|### | 6/20 [00:03<00:08, 1.70it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 35%|###5 | 7/20 [00:03<00:07, 1.70it/s][I 2024-05-22 13:01:32,519] Trial 13 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 169}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 35%|###5 | 7/20 [00:03<00:07, 1.70it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 40%|#### | 8/20 [00:04<00:06, 1.77it/s][I 2024-05-22 13:01:33,097] Trial 14 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 156}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 40%|#### | 8/20 [00:04<00:06, 1.77it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 45%|####5 | 9/20 [00:05<00:06, 1.67it/s][I 2024-05-22 13:01:33,710] Trial 15 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 196}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 45%|####5 | 9/20 [00:05<00:06, 1.67it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 50%|##### | 10/20 [00:05<00:05, 1.75it/s][I 2024-05-22 13:01:34,220] Trial 16 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 111}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 50%|##### | 10/20 [00:05<00:05, 1.75it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 55%|#####5 | 11/20 [00:06<00:04, 1.82it/s][I 2024-05-22 13:01:34,717] Trial 17 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 57}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 55%|#####5 | 11/20 [00:06<00:04, 1.82it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 60%|###### | 12/20 [00:06<00:04, 1.87it/s][I 2024-05-22 13:01:35,221] Trial 18 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 212}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 60%|###### | 12/20 [00:06<00:04, 1.87it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 65%|######5 | 13/20 [00:07<00:03, 1.90it/s][I 2024-05-22 13:01:35,730] Trial 19 finished with value: 1.3616097829410518e-06 and parameters: {'num_leaves': 255}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 65%|######5 | 13/20 [00:07<00:03, 1.90it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 70%|####### | 14/20 [00:07<00:03, 1.85it/s][I 2024-05-22 13:01:36,303] Trial 20 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 150}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 70%|####### | 14/20 [00:07<00:03, 1.85it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 75%|#######5 | 15/20 [00:08<00:02, 1.86it/s][I 2024-05-22 13:01:36,827] Trial 21 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 186}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 75%|#######5 | 15/20 [00:08<00:02, 1.86it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 80%|######## | 16/20 [00:08<00:02, 1.82it/s][I 2024-05-22 13:01:37,407] Trial 22 finished with value: 1.3616097829410516e-06 and parameters: {'num_leaves': 171}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 80%|######## | 16/20 [00:08<00:02, 1.82it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 85%|########5 | 17/20 [00:09<00:01, 1.86it/s][I 2024-05-22 13:01:37,920] Trial 23 finished with value: 1.3616097829410518e-06 and parameters: {'num_leaves': 127}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 85%|########5 | 17/20 [00:09<00:01, 1.86it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 90%|######### | 18/20 [00:09<00:01, 1.88it/s][I 2024-05-22 13:01:38,433] Trial 24 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 220}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 90%|######### | 18/20 [00:09<00:01, 1.88it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 95%|#########5| 19/20 [00:10<00:00, 1.83it/s][I 2024-05-22 13:01:39,016] Trial 25 finished with value: 1.3616097829410512e-06 and parameters: {'num_leaves': 85}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 95%|#########5| 19/20 [00:10<00:00, 1.83it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.000001: 100%|##########| 20/20 [00:10<00:00, 1.77it/s][I 2024-05-22 13:01:39,624] Trial 26 finished with value: 1.361609782941051e-06 and parameters: {'num_leaves': 140}. Best is trial 12 with value: 1.361609782941051e-06. num_leaves, val_score: 0.000001: 100%|##########| 20/20 [00:11<00:00, 1.82it/s] Early stopping, best iteration is: [344] valid_0's binary_logloss: 1.36161e-06 bagging, val_score: 0.000001: 0%| | 0/10 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 10%|# | 1/10 [00:00<00:05, 1.65it/s][I 2024-05-22 13:01:40,309] Trial 27 finished with value: 3.449205755838957e-07 and parameters: {'bagging_fraction': 0.4418842108373299, 'bagging_freq': 5}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 10%|# | 1/10 [00:00<00:05, 1.65it/s] Early stopping, best iteration is: [266] valid_0's binary_logloss: 3.44921e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 20%|## | 2/10 [00:00<00:03, 2.63it/s][I 2024-05-22 13:01:40,531] Trial 28 finished with value: 0.003198735879283877 and parameters: {'bagging_fraction': 0.40010886623598485, 'bagging_freq': 5}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 20%|## | 2/10 [00:00<00:03, 2.63it/s] Early stopping, best iteration is: [49] valid_0's binary_logloss: 0.00319874 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 30%|### | 3/10 [00:01<00:03, 2.29it/s][I 2024-05-22 13:01:41,034] Trial 29 finished with value: 2.3253951344156617e-06 and parameters: {'bagging_fraction': 0.5615890028222774, 'bagging_freq': 3}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 30%|### | 3/10 [00:01<00:03, 2.29it/s] Early stopping, best iteration is: [216] valid_0's binary_logloss: 2.3254e-06 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 40%|#### | 4/10 [00:02<00:03, 1.89it/s][I 2024-05-22 13:01:41,710] Trial 30 finished with value: 1.6847441854237672e-06 and parameters: {'bagging_fraction': 0.9729155031966343, 'bagging_freq': 7}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 40%|#### | 4/10 [00:02<00:03, 1.89it/s] Early stopping, best iteration is: [253] valid_0's binary_logloss: 1.68474e-06 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 50%|##### | 5/10 [00:02<00:02, 1.90it/s][I 2024-05-22 13:01:42,229] Trial 31 finished with value: 2.3371325286333586e-06 and parameters: {'bagging_fraction': 0.7517048260537027, 'bagging_freq': 1}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 50%|##### | 5/10 [00:02<00:02, 1.90it/s] Early stopping, best iteration is: [197] valid_0's binary_logloss: 2.33713e-06 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 60%|###### | 6/10 [00:02<00:01, 2.23it/s][I 2024-05-22 13:01:42,525] Trial 32 finished with value: 0.0031987358794118973 and parameters: {'bagging_fraction': 0.40032139731038635, 'bagging_freq': 5}. Best is trial 27 with value: 3.449205755838957e-07. bagging, val_score: 0.000000: 60%|###### | 6/10 [00:02<00:01, 2.23it/s] Early stopping, best iteration is: [49] valid_0's binary_logloss: 0.00319874 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 70%|####### | 7/10 [00:03<00:01, 2.17it/s][I 2024-05-22 13:01:43,013] Trial 33 finished with value: 2.9743999611270926e-07 and parameters: {'bagging_fraction': 0.6425832769598463, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 70%|####### | 7/10 [00:03<00:01, 2.17it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.9744e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 80%|######## | 8/10 [00:03<00:00, 2.24it/s][I 2024-05-22 13:01:43,427] Trial 34 finished with value: 3.040896374710074e-07 and parameters: {'bagging_fraction': 0.6280540860504722, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 80%|######## | 8/10 [00:03<00:00, 2.24it/s] Early stopping, best iteration is: [208] valid_0's binary_logloss: 3.0409e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 90%|######### | 9/10 [00:04<00:00, 2.19it/s][I 2024-05-22 13:01:43,909] Trial 35 finished with value: 7.219543528343957e-07 and parameters: {'bagging_fraction': 0.6210765540576059, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 90%|######### | 9/10 [00:04<00:00, 2.19it/s] Early stopping, best iteration is: [187] valid_0's binary_logloss: 7.21954e-07 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.000000: 100%|##########| 10/10 [00:04<00:00, 2.25it/s][I 2024-05-22 13:01:44,327] Trial 36 finished with value: 3.404675474216927e-07 and parameters: {'bagging_fraction': 0.6658803881381716, 'bagging_freq': 7}. Best is trial 33 with value: 2.9743999611270926e-07. bagging, val_score: 0.000000: 100%|##########| 10/10 [00:04<00:00, 2.16it/s] Early stopping, best iteration is: [251] valid_0's binary_logloss: 3.40468e-07 feature_fraction_stage2, val_score: 0.000000: 0%| | 0/3 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.000000: 33%|###3 | 1/3 [00:00<00:00, 2.51it/s][I 2024-05-22 13:01:44,809] Trial 37 finished with value: 2.9743999611270936e-07 and parameters: {'feature_fraction': 0.9520000000000001}. Best is trial 37 with value: 2.9743999611270936e-07. feature_fraction_stage2, val_score: 0.000000: 33%|###3 | 1/3 [00:00<00:00, 2.51it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.9744e-07 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.000000: 67%|######6 | 2/3 [00:00<00:00, 2.80it/s][I 2024-05-22 13:01:45,199] Trial 38 finished with value: 2.9743999611270936e-07 and parameters: {'feature_fraction': 0.9840000000000001}. Best is trial 37 with value: 2.9743999611270936e-07. feature_fraction_stage2, val_score: 0.000000: 67%|######6 | 2/3 [00:00<00:00, 2.80it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.9744e-07 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.000000: 100%|##########| 3/3 [00:01<00:00, 2.20it/s][I 2024-05-22 13:01:45,710] Trial 39 finished with value: 4.498413698090123e-07 and parameters: {'feature_fraction': 0.92}. Best is trial 37 with value: 2.9743999611270936e-07. feature_fraction_stage2, val_score: 0.000000: 100%|##########| 3/3 [00:01<00:00, 2.29it/s] Early stopping, best iteration is: [256] valid_0's binary_logloss: 4.49841e-07 regularization_factors, val_score: 0.000000: 0%| | 0/20 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds Early stopping, best iteration is: [227] valid_0's binary_logloss: 0.00690416 regularization_factors, val_score: 0.000000: 5%|5 | 1/20 [00:00<00:05, 3.40it/s][I 2024-05-22 13:01:46,021] Trial 40 finished with value: 0.006904162868543424 and parameters: {'lambda_l1': 2.2800500671305803, 'lambda_l2': 0.0011641838798314252}. Best is trial 40 with value: 0.006904162868543424. regularization_factors, val_score: 0.000000: 5%|5 | 1/20 [00:00<00:05, 3.40it/s] Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 10%|# | 2/20 [00:00<00:06, 2.87it/s][I 2024-05-22 13:01:46,408] Trial 41 finished with value: 2.974565285658327e-07 and parameters: {'lambda_l1': 3.2596973482462435e-08, 'lambda_l2': 1.061451695187828e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 10%|# | 2/20 [00:00<00:06, 2.87it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97457e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 15%|#5 | 3/20 [00:01<00:06, 2.68it/s][I 2024-05-22 13:01:46,811] Trial 42 finished with value: 2.974705202215368e-07 and parameters: {'lambda_l1': 2.241952369984708e-08, 'lambda_l2': 1.8880584030540923e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 15%|#5 | 3/20 [00:01<00:06, 2.68it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97471e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 20%|## | 4/20 [00:01<00:05, 2.83it/s][I 2024-05-22 13:01:47,131] Trial 43 finished with value: 2.9748702210105415e-07 and parameters: {'lambda_l1': 1.0199507520333984e-08, 'lambda_l2': 2.130651303908614e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 20%|## | 4/20 [00:01<00:05, 2.83it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97487e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 25%|##5 | 5/20 [00:01<00:05, 2.72it/s][I 2024-05-22 13:01:47,526] Trial 44 finished with value: 2.974846523730242e-07 and parameters: {'lambda_l1': 1.6736475077289633e-08, 'lambda_l2': 1.0112045153335179e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 25%|##5 | 5/20 [00:01<00:05, 2.72it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97485e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 30%|### | 6/20 [00:02<00:04, 2.89it/s][I 2024-05-22 13:01:47,829] Trial 45 finished with value: 3.176935636511711e-07 and parameters: {'lambda_l1': 5.882861681810232e-06, 'lambda_l2': 1.1233467846223274e-06}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 30%|### | 6/20 [00:02<00:04, 2.89it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 3.17694e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 35%|###5 | 7/20 [00:03<00:08, 1.61it/s][I 2024-05-22 13:01:49,021] Trial 46 finished with value: 0.00013806811413863104 and parameters: {'lambda_l1': 1.0772166664037463e-05, 'lambda_l2': 3.5907966551315282}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 35%|###5 | 7/20 [00:03<00:08, 1.61it/s] Did not meet early stopping. Best iteration is: [1000] valid_0's binary_logloss: 0.000138068 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 40%|#### | 8/20 [00:03<00:06, 1.91it/s][I 2024-05-22 13:01:49,331] Trial 47 finished with value: 3.3174632310732203e-07 and parameters: {'lambda_l1': 9.228838671620809e-07, 'lambda_l2': 1.2357091396245195e-05}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 40%|#### | 8/20 [00:03<00:06, 1.91it/s] Early stopping, best iteration is: [209] valid_0's binary_logloss: 3.31746e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 45%|####5 | 9/20 [00:03<00:05, 2.10it/s][I 2024-05-22 13:01:49,706] Trial 48 finished with value: 2.8970612681555003e-06 and parameters: {'lambda_l1': 0.004116041688790151, 'lambda_l2': 4.91159030541583e-07}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 45%|####5 | 9/20 [00:03<00:05, 2.10it/s] Early stopping, best iteration is: [206] valid_0's binary_logloss: 2.89706e-06 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 50%|##### | 10/20 [00:04<00:04, 2.33it/s][I 2024-05-22 13:01:50,028] Trial 49 finished with value: 5.975862396554039e-07 and parameters: {'lambda_l1': 2.4738617566052645e-07, 'lambda_l2': 0.003148588239925189}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 50%|##### | 10/20 [00:04<00:04, 2.33it/s] Early stopping, best iteration is: [283] valid_0's binary_logloss: 5.97586e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 55%|#####5 | 11/20 [00:04<00:03, 2.41it/s][I 2024-05-22 13:01:50,411] Trial 50 finished with value: 5.528528493352603e-07 and parameters: {'lambda_l1': 0.0005147056784853866, 'lambda_l2': 1.3373340341538605e-05}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 55%|#####5 | 11/20 [00:04<00:03, 2.41it/s] Early stopping, best iteration is: [204] valid_0's binary_logloss: 5.52853e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 60%|###### | 12/20 [00:05<00:03, 2.45it/s][I 2024-05-22 13:01:50,806] Trial 51 finished with value: 2.97483914403635e-07 and parameters: {'lambda_l1': 1.1161313264875577e-08, 'lambda_l2': 1.3457934495742431e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 60%|###### | 12/20 [00:05<00:03, 2.45it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97484e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 65%|######5 | 13/20 [00:05<00:02, 2.63it/s][I 2024-05-22 13:01:51,122] Trial 52 finished with value: 2.974834213929394e-07 and parameters: {'lambda_l1': 1.1851264993602946e-08, 'lambda_l2': 1.1579633227443254e-08}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 65%|######5 | 13/20 [00:05<00:02, 2.63it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97483e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 70%|####### | 14/20 [00:05<00:02, 2.59it/s][I 2024-05-22 13:01:51,521] Trial 53 finished with value: 3.0224491028537356e-07 and parameters: {'lambda_l1': 1.8444434562217248e-07, 'lambda_l2': 1.904342905861566e-07}. Best is trial 41 with value: 2.974565285658327e-07. regularization_factors, val_score: 0.000000: 70%|####### | 14/20 [00:05<00:02, 2.59it/s] Early stopping, best iteration is: [204] valid_0's binary_logloss: 3.02245e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 75%|#######5 | 15/20 [00:06<00:02, 2.38it/s][I 2024-05-22 13:01:52,020] Trial 54 finished with value: 2.97111604544352e-07 and parameters: {'lambda_l1': 1.8697547616086913e-07, 'lambda_l2': 1.4195276461779107e-07}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 75%|#######5 | 15/20 [00:06<00:02, 2.38it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 2.97112e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 80%|######## | 16/20 [00:06<00:01, 2.61it/s][I 2024-05-22 13:01:52,319] Trial 55 finished with value: 2.7975386625160922e-05 and parameters: {'lambda_l1': 9.1209069884061e-06, 'lambda_l2': 2.832296835692325e-07}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 80%|######## | 16/20 [00:06<00:01, 2.61it/s] Early stopping, best iteration is: [150] valid_0's binary_logloss: 2.79754e-05 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 85%|########5 | 17/20 [00:06<00:01, 2.77it/s][I 2024-05-22 13:01:52,623] Trial 56 finished with value: 3.012783257895089e-07 and parameters: {'lambda_l1': 5.249197497865728e-07, 'lambda_l2': 8.723247474648092e-06}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 85%|########5 | 17/20 [00:06<00:01, 2.77it/s] Early stopping, best iteration is: [205] valid_0's binary_logloss: 3.01278e-07 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 90%|######### | 18/20 [00:07<00:00, 2.31it/s][I 2024-05-22 13:01:53,223] Trial 57 finished with value: 6.216923691382048e-06 and parameters: {'lambda_l1': 1.2654511369988902e-07, 'lambda_l2': 0.026856741790607278}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 90%|######### | 18/20 [00:07<00:00, 2.31it/s] Early stopping, best iteration is: [494] valid_0's binary_logloss: 6.21692e-06 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 95%|#########5| 19/20 [00:07<00:00, 2.57it/s][I 2024-05-22 13:01:53,511] Trial 58 finished with value: 4.6829312861054814e-05 and parameters: {'lambda_l1': 0.02854037816163236, 'lambda_l2': 1.2681271167280724e-07}. Best is trial 54 with value: 2.97111604544352e-07. regularization_factors, val_score: 0.000000: 95%|#########5| 19/20 [00:07<00:00, 2.57it/s] Early stopping, best iteration is: [182] valid_0's binary_logloss: 4.68293e-05 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.000000: 100%|##########| 20/20 [00:08<00:00, 2.74it/s][I 2024-05-22 13:01:53,821] Trial 59 finished with value: 2.468284408525897e-07 and parameters: {'lambda_l1': 0.0001184573048303984, 'lambda_l2': 4.182514336496212e-06}. Best is trial 59 with value: 2.468284408525897e-07. regularization_factors, val_score: 0.000000: 100%|##########| 20/20 [00:08<00:00, 2.47it/s] Early stopping, best iteration is: [240] valid_0's binary_logloss: 2.46828e-07 min_child_samples, val_score: 0.000000: 0%| | 0/5 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 20%|## | 1/5 [00:00<00:01, 3.44it/s][I 2024-05-22 13:01:54,119] Trial 60 finished with value: 5.156782324432179e-06 and parameters: {'min_child_samples': 10}. Best is trial 60 with value: 5.156782324432179e-06. min_child_samples, val_score: 0.000000: 20%|## | 1/5 [00:00<00:01, 3.44it/s] Early stopping, best iteration is: [199] valid_0's binary_logloss: 5.15678e-06 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 40%|#### | 2/5 [00:00<00:00, 3.32it/s][I 2024-05-22 13:01:54,427] Trial 61 finished with value: 3.282327081150373e-07 and parameters: {'min_child_samples': 100}. Best is trial 61 with value: 3.282327081150373e-07. min_child_samples, val_score: 0.000000: 40%|#### | 2/5 [00:00<00:00, 3.32it/s] Early stopping, best iteration is: [269] valid_0's binary_logloss: 3.28233e-07 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 60%|###### | 3/5 [00:00<00:00, 3.41it/s][I 2024-05-22 13:01:54,712] Trial 62 finished with value: 3.6291058037098226e-06 and parameters: {'min_child_samples': 5}. Best is trial 61 with value: 3.282327081150373e-07. min_child_samples, val_score: 0.000000: 60%|###### | 3/5 [00:00<00:00, 3.41it/s] Early stopping, best iteration is: [176] valid_0's binary_logloss: 3.62911e-06 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 80%|######## | 4/5 [00:01<00:00, 3.78it/s][I 2024-05-22 13:01:54,932] Trial 63 finished with value: 3.8920361508245125e-07 and parameters: {'min_child_samples': 25}. Best is trial 61 with value: 3.282327081150373e-07. min_child_samples, val_score: 0.000000: 80%|######## | 4/5 [00:01<00:00, 3.78it/s] Early stopping, best iteration is: [204] valid_0's binary_logloss: 3.89204e-07 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.000000: 100%|##########| 5/5 [00:01<00:00, 3.62it/s][I 2024-05-22 13:01:55,228] Trial 64 finished with value: 2.1603725624802887e-07 and parameters: {'min_child_samples': 50}. Best is trial 64 with value: 2.1603725624802887e-07. min_child_samples, val_score: 0.000000: 100%|##########| 5/5 [00:01<00:00, 3.56it/s] Early stopping, best iteration is: [252] valid_0's binary_logloss: 2.16037e-07 In [12]: Copied! # save/load trainer dill_dump ( \"lgbm_trainer.dill\" , trainer ) trainer = dill_load ( \"lgbm_trainer.dill\" ) # save/load trainer dill_dump(\"lgbm_trainer.dill\", trainer) trainer = dill_load(\"lgbm_trainer.dill\") In [13]: Copied! pprint ( metrics_dict ) pprint(metrics_dict) {'cls_report': {'0': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 94}, '1': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 10}, 'accuracy': 1.0, 'macro avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}, 'weighted avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}}, 'cm': [[94, 0], [0, 10]], 'prec_rec_curve': [[0.09615384615384616, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 1.0]]} In [14]: Copied! pprint ( trainer . compute_metrics ( df_test , with_dynamic_binary_threshold = True )) pprint(trainer.compute_metrics(df_test, with_dynamic_binary_threshold=True)) {'cls_report': {'0': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 94}, '1': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 10}, 'accuracy': 1.0, 'macro avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}, 'weighted avg': {'f1-score': 1.0, 'precision': 1.0, 'recall': 1.0, 'support': 104}}, 'cm': [[94, 0], [0, 10]], 'prec_rec_curve': [[0.09615384615384616, 1.0, 1.0], [1.0, 1.0, 0.0], [0.0, 1.0]]}","title":"Train a testing model"},{"location":"notebooks/04_model_training_pipeline.html#mlflow","text":"tracking model registration in Minio permanent experiment deletion # in python mlflow.get_experiment_by_name(\"success6g_lgbm\").experiment_id # in cmd mlflow experiments delete -x 1 In [26]: Copied! mlflow_host = \"10.152.183.54\" mlflow_host_url = \"mlflow.mlflow.svc.cluster.local\" mlflow_port = \"5000\" os . environ [ \"AWS_ACCESS_KEY_ID\" ] = \"minioadmin\" os . environ [ \"AWS_SECRET_ACCESS_KEY\" ] = \"minioadmin\" os . environ [ \"MLFLOW_S3_ENDPOINT_URL\" ] = f \"http://10.152.183.156:9000\" mlflow . set_tracking_uri ( \"http://\" + mlflow_host + \":\" + mlflow_port ) experiment_id = get_or_create_experiment ( \"success6g\" ) mlflow . set_experiment ( experiment_id = experiment_id ) mlflow_host = \"10.152.183.54\" mlflow_host_url = \"mlflow.mlflow.svc.cluster.local\" mlflow_port = \"5000\" os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\" mlflow.set_tracking_uri(\"http://\" + mlflow_host + \":\" + mlflow_port) experiment_id = get_or_create_experiment(\"success6g\") mlflow.set_experiment(experiment_id=experiment_id) Out[26]: <Experiment: artifact_location='s3://mlflow/3', creation_time=1716388845676, experiment_id='3', last_update_time=1716388845676, lifecycle_stage='active', name='success6g', tags={}> In [27]: Copied! metrics_dict_flattened = flatten_dict ( metrics_dict ) # mlflow metrics can be only int, float not list del metrics_dict_flattened [ \"cm\" ] del metrics_dict_flattened [ \"prec_rec_curve\" ] metrics_dict_flattened = flatten_dict(metrics_dict) # mlflow metrics can be only int, float not list del metrics_dict_flattened[\"cm\"] del metrics_dict_flattened[\"prec_rec_curve\"] In [29]: Copied! run_name = \"test\" with mlflow . start_run ( experiment_id = experiment_id , run_name = run_name , nested = True ) as run : mlflow . log_params ( trainer . optimizer . best ) mlflow . log_metrics ( metrics_dict_flattened ) # Log tags mlflow . set_tags ( tags = { \"project\" : \"SUCCESS6G\" , \"optimizer_engine\" : \"optuna\" , \"model_family\" : \"ligtgbm\" , \"feature_set_version\" : 1 , } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow . pyfunc . log_model ( python_model = trainer , artifact_path = artifact_path , registered_model_name = registered_model_name , ) model_uri = mlflow . get_artifact_uri ( artifact_path ) print ( f \"Run ID: \\n { run . info . run_id } \\n Model uri: \\n { model_uri } \" ) run_name = \"test\" with mlflow.start_run( experiment_id=experiment_id, run_name=run_name, nested=True ) as run: mlflow.log_params(trainer.optimizer.best) mlflow.log_metrics(metrics_dict_flattened) # Log tags mlflow.set_tags( tags={ \"project\": \"SUCCESS6G\", \"optimizer_engine\": \"optuna\", \"model_family\": \"ligtgbm\", \"feature_set_version\": 1, } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"success6g_model\" registered_model_name = \"success6g_model\" mlflow.pyfunc.log_model( python_model=trainer, artifact_path=artifact_path, registered_model_name=registered_model_name, ) model_uri = mlflow.get_artifact_uri(artifact_path) print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\") 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: typing.Union[pandas.core.frame.DataFrame, dict], skipping schema inference 2024/05/22 14:44:19 INFO mlflow.types.utils: Unsupported type hint: <class 'pandas.core.frame.DataFrame'>, skipping schema inference Successfully registered model 'success6g_model'. 2024/05/22 14:44:22 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: success6g_model, version 1 Run ID: 24fedb96c5004767800fa32a3220c8dd Model uri: s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model Created version '1' of model 'success6g_model'.","title":"Mlflow"},{"location":"notebooks/04_model_training_pipeline.html#testing-of-predictions","text":"","title":"Testing of predictions"},{"location":"notebooks/04_model_training_pipeline.html#example-data","text":"In [30]: Copied! df_pd . iloc [: 2 ] df_pd.iloc[:2] Out[30]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 0 100.0 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 123abc 1 100.0 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 1 123abc In [31]: Copied! # for testing using mlflow inference service request_dict = { \"engine_load\" : [ 100.0 , 100.0 ], \"engine_coolant_temp\" : [ 17.0 , 17.0 ], \"engine_speed\" : [ 904.5 , 906.0 ], \"vehicle_speed\" : [ 0.0 , 0.0 ], \"intake_air_temp\" : [ 10.0 , 11.0 ], \"maf\" : [ 12.55 , 12.36 ], \"throttle_position\" : [ 83.14 , 83.14 ], \"fuel_rg_pressure\" : [ 37270.0 , 37800.0 ], \"barometric_pressure\" : [ 101.0 , 101.0 ], \"control_voltage\" : [ 0.06 , 14.56 ], \"class\" : [ 1 , 1 ], \"car_id\" : [ \"123abc\" , \"123abc\" ], } # for testing using mlflow inference service request_dict = { \"engine_load\": [100.0, 100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"class\": [1, 1], \"car_id\": [\"123abc\", \"123abc\"], }","title":"Example data"},{"location":"notebooks/04_model_training_pipeline.html#trainer","text":"In [32]: Copied! trainer . predict ( df = df_pd . iloc [: 2 ] . drop ( columns = [ \"class\" ]), context = {}) trainer.predict(df=df_pd.iloc[:2].drop(columns=[\"class\"]), context={}) Out[32]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 0 14.269467 1 14.269467","title":"Trainer"},{"location":"notebooks/04_model_training_pipeline.html#downloaded-trainer","text":"In [33]: Copied! model_uri model_uri Out[33]: 's3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model' In [34]: Copied! loaded_trainer = mlflow . pyfunc . load_model ( model_uri ) loaded_trainer = mlflow.pyfunc.load_model(model_uri) Downloading artifacts: 0%| | 0/9 [00:00<?, ?it/s] In [35]: Copied! loaded_trainer . predict ( df_pd . iloc [: 2 ] . drop ( columns = [ \"class\" ])) loaded_trainer.predict(df_pd.iloc[:2].drop(columns=[\"class\"])) Out[35]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 0 14.269467 1 14.269467","title":"Downloaded Trainer"},{"location":"notebooks/04_model_training_pipeline.html#downloadedserved-trainer","text":"i.e. testing model locally In [36]: Copied! model_uri model_uri Out[36]: 's3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model' In [ ]: Copied! ! mlflow models serve - m \"s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model\" -- env - manager local - p 5000 ! mlflow models serve -m \"s3://mlflow/3/24fedb96c5004767800fa32a3220c8dd/artifacts/success6g_model\" --env-manager local -p 5000 Downloading artifacts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 2088.80it/s] 2024/05/22 14:45:07 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function' Downloading artifacts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 9/9 [00:00<00:00, 69.72it/s] 2024/05/22 14:45:07 INFO mlflow.pyfunc.backend: === Running command 'exec gunicorn --timeout=60 -b 127.0.0.1:5000 -w 1 ${GUNICORN_CMD_ARGS} -- mlflow.pyfunc.scoring_server.wsgi:app' [2024-05-22 14:45:07 +0000] [17971] [INFO] Starting gunicorn 22.0.0 [2024-05-22 14:45:07 +0000] [17971] [INFO] Listening at: http://127.0.0.1:5000 (17971) [2024-05-22 14:45:07 +0000] [17971] [INFO] Using worker: sync [2024-05-22 14:45:07 +0000] [17972] [INFO] Booting worker with pid: 17972 root@jupyter-5uperpalo:~/assignment# curl -X POST -H \"Content-Type:application/json\" --data '{\"inputs\": {\"engine_load\": [100.0 ,100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"car_id\": [\"123abc\", \"123abc\"]}}' http://127.0.0.1:5000/invocations {\"predictions\": [{\"class\": 14.26946728558632}, {\"class\": 14.26946728558632}]}","title":"Downloaded/Served Trainer"},{"location":"notebooks/04_model_training_pipeline.html#model-deployment-using-kserve","text":"https://mlflow.org/docs/latest/deployment/deploy-model-to-kubernetes/tutorial.html?highlight=kserve#step-7-deploying-the-model-to-kserve","title":"Model deployment using Kserve"},{"location":"notebooks/04_model_training_pipeline.html#using-model_uri","text":"since I configured minio as artifact store I can use model_uri - I was WRONG! procedure: make sure the kserve is able to reach minio * by [defining s3-secret and attaching it to service account](https://kserve.github.io/website/0.7/modelserving/storage/s3/s3/#create-s3-secret) * created ServiceAccount must be in a same namespace as the InferenceService we are going to create, [e.g. stackoverflow here](https://stackoverflow.com/a/70977798) * `If an application does not specify a serviceAccountName, it uses the \"default\" service account.`[e.g. here](https://kubernetes.io/docs/reference/access-authn-authz/rbac/#role-binding-examples) ``` apiVersion: v1 kind: Secret metadata: name: s3creds namespace: mlflow-kserve-success6g annotations: serving.kserve.io/s3-endpoint: 10.152.183.156:9000 # replace with your s3 endpoint e.g minio-service.kubeflow:9000 serving.kserve.io/s3-usehttps: \"0\" # by default 1, if testing with minio you can set to 0 # serving.kserve.io/s3-region: \"us-east-2\" serving.kserve.io/s3-useanoncredential: \"false\" # omitting this is the same as false, if true will ignore provided credential and use anonymous credentials type: Opaque stringData: # use `stringData` for raw credential string or `data` for base64 encoded string AWS_ACCESS_KEY_ID: \"minioadmin\" AWS_SECRET_ACCESS_KEY: \"minioadmin\" ``` `kubectl apply -f create-s3-secret.yaml` create namespace ``` kubectl create namespace mlflow-kserve-success6g namespace/mlflow-kserve-success6g created ``` specify configuration of the deployment in the file `mlflow-kserve-success6g_model_uri.yaml` ``` apiVersion: \"serving.kserve.io/v1beta1\" kind: \"InferenceService\" metadata: name: \"mlflow-success6g\" namespace: \"mlflow-kserve-success6g\" spec: predictor: model: modelFormat: name: mlflow protocolVersion: v2 storageUri: \"s3://mlflow/1/777cf64c922149a4b77c85987865deb0/artifacts/success6g_model\" ``` deploy ``` kubectl apply -f mlflow-kserve-success6g.yaml inferenceservice.serving.kserve.io/mlflow-success6g created ``` check the status of the deployment `kubectl get inferenceservice mlflow-success6g` specify testing json in `test-input.json` ``` { \"inputs\": [ { \"name\": \"input\", \"shape\": [13], \"datatype\": \"FP32\", \"data\": { \"engine_load\": [100.0 ,100.0], \"engine_coolant_temp\": [17.0, 17.0], \"engine_speed\": [904.5, 906.0], \"vehicle_speed\": [0.0, 0.0], \"intake_air_temp\": [10.0, 11.0], \"maf\": [12.55, 12.36], \"throttle_position\": [83.14, 83.14], \"fuel_rg_pressure\": [37270.0, 37800.0], \"barometric_pressure\": [101.0, 101.0], \"control_voltage\": [0.06, 14.56], \"car_id\": [\"123abc\", \"123abc\"], } } ] } ``` test json ``` SERVICE_HOSTNAME=$(kubectl get inferenceservice mlflow-success6g -n mlflow-kserve-success6g -o jsonpath='{.status.url}' | cut -d \"/\" -f 3) curl -v \\ -H \"Host: ${SERVICE_HOSTNAME}\" \\ -H \"Content-Type: application/json\" \\ -d @./test-input.json \\ http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/mlflow-success6g/infer ``` but after all the trouble this can still fail if you developed the model in different python version than kserve is using ``` pmulinka@saiacheron:~/kubernetes/kserve$ kubectl logs -p mlflow-success6g-predictor-00001-deployment-74ccf4fb5d-lktz9 -n mlflow-kserve-success6g Defaulted container \"kserve-container\" out of: kserve-container, queue-proxy, storage-initializer (init) Environment tarball not found at '/mnt/models/environment.tar.gz' Environment not found at './envs/environment' 2024-05-16 15:32:55,293 [mlserver.parallel] DEBUG - Starting response processing loop... 2024-05-16 15:32:55,298 [mlserver.rest] INFO - HTTP server running on http://0.0.0.0:8080 INFO: Started server process [1] INFO: Waiting for application startup. 2024-05-16 15:32:55,411 [mlserver.metrics] INFO - Metrics server running on http://0.0.0.0:8082 2024-05-16 15:32:55,412 [mlserver.metrics] INFO - Prometheus scraping endpoint can be accessed on http://0.0.0.0:8082/metrics INFO: Started server process [1] INFO: Waiting for application startup. INFO: Application startup complete. 2024-05-16 15:32:57,613 [mlserver.grpc] INFO - gRPC server running on http://0.0.0.0:9000 INFO: Application startup complete. INFO: Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit) INFO: Uvicorn running on http://0.0.0.0:8082 (Press CTRL+C to quit) 2024/05/16 15:32:58 WARNING mlflow.pyfunc: Detected one or more mismatches between the model's dependencies and the current Python environment: - mlflow (current: 2.3.1, required: mlflow==2.12.2) - cryptography (current: 38.0.4, required: cryptography==40.0.2) - cytoolz (current: uninstalled, required: cytoolz==0.12.0) - defusedxml (current: uninstalled, required: defusedxml==0.7.1) - distributed (current: uninstalled, required: distributed==2023.5.1) - lightgbm (current: 3.3.5, required: lightgbm==4.3.0) - lz4 (current: uninstalled, required: lz4==4.3.2) - numpy (current: 1.23.5, required: numpy==1.24.3) - optuna-integration (current: uninstalled, required: optuna-integration==3.6.0) - pandas (current: 2.0.1, required: pandas==2.2.2) - pyarrow (current: 11.0.0, required: pyarrow==12.0.0) - redis (current: uninstalled, required: redis==5.0.4) - scipy (current: 1.9.1, required: scipy==1.10.1) To fix the mismatches, call `mlflow.pyfunc.get_model_dependencies(model_uri)` to fetch the model's environment and install dependencies using the resulting environment file. 2024/05/16 15:32:58 WARNING mlflow.pyfunc: The version of Python that the model was saved in, `Python 3.10.11`, differs from the version of Python that is currently running, `Python 3.8.16`, and may be incompatible 2024-05-16 15:32:58,967 [mlserver] INFO - Couldn't load model 'mlflow-success6g'. Model will be removed from registry. 2024-05-16 15:32:58,967 [mlserver.parallel] ERROR - An error occurred processing a model update of type 'Load'. Traceback (most recent call last): File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/worker.py\", line 158, in _process_model_update await self._model_registry.load(model_settings) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 293, in load return await self._models[model_settings.name].load(model_settings) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 148, in load await self._load_model(new_model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 165, in _load_model model.ready = await model.load() File \"/opt/conda/lib/python3.8/site-packages/mlserver_mlflow/runtime.py\", line 155, in load self._model = mlflow.pyfunc.load_model(model_uri) File \"/opt/conda/lib/python3.8/site-packages/mlflow/pyfunc/__init__.py\", line 596, in load_model model_impl = importlib.import_module(conf[MAIN])._load_pyfunc(data_path) File \"/opt/conda/lib/python3.8/site-packages/mlflow/pyfunc/model.py\", line 291, in _load_pyfunc python_model = cloudpickle.load(f) ModuleNotFoundError: No module named 'inference_model' 2024-05-16 15:32:58,970 [mlserver] INFO - Couldn't load model 'mlflow-success6g'. Model will be removed from registry. 2024-05-16 15:32:58,975 [mlserver.parallel] ERROR - An error occurred processing a model update of type 'Unload'. Traceback (most recent call last): File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/worker.py\", line 160, in _process_model_update await self._model_registry.unload_version( File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 302, in unload_version await model_registry.unload_version(version) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 201, in unload_version model = await self.get_model(version) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 237, in get_model raise ModelNotFound(self._name, version) mlserver.errors.ModelNotFound: Model mlflow-success6g not found 2024-05-16 15:32:58,976 [mlserver] ERROR - Some of the models failed to load during startup! Traceback (most recent call last): File \"/opt/conda/lib/python3.8/site-packages/mlserver/server.py\", line 125, in start await asyncio.gather( File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 293, in load return await self._models[model_settings.name].load(model_settings) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 148, in load await self._load_model(new_model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/registry.py\", line 161, in _load_model model = await callback(model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/registry.py\", line 152, in load_model loaded = await pool.load_model(model) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/pool.py\", line 74, in load_model await self._dispatcher.dispatch_update(load_message) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 123, in dispatch_update return await asyncio.gather( File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 138, in _dispatch_update return await self._dispatch(worker_update) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 146, in _dispatch return await self._wait_response(internal_id) File \"/opt/conda/lib/python3.8/site-packages/mlserver/parallel/dispatcher.py\", line 152, in _wait_response inference_response = await async_response mlserver.parallel.errors.WorkerError: builtins.ModuleNotFoundError: No module named 'inference_model' 2024-05-16 15:32:58,978 [mlserver.parallel] INFO - Waiting for shutdown of default inference pool... 2024-05-16 15:32:59,163 [mlserver.parallel] INFO - Shutdown of default inference pool complete 2024-05-16 15:32:59,163 [mlserver.grpc] INFO - Waiting for gRPC server shutdown 2024-05-16 15:32:59,169 [mlserver.grpc] INFO - gRPC server shutdown complete INFO: Shutting down INFO: Shutting down INFO: Waiting for application shutdown. INFO: Application shutdown complete. INFO: Finished server process [1] INFO: Waiting for application shutdown. INFO: Application shutdown complete. INFO: Finished server process [1] ``` and some possible readiness probe issues * apparently I am not alone in this, e.g. [here](https://stackoverflow.com/questions/62569747/ml-model-pod-keeps-restarting-in-seldon-deployment) ``` pmulinka@saiacheron:~/kubernetes/kserve$ kubectl describe pod mlflow-success6g-predictor-00001-deployment-74ccf4fb5d-lktz9 -n mlflow-kserve-success6g Events: Type Reason Age From Message ---- ------ ---- ---- ------- Warning Unhealthy 4m11s (x268 over 34m) kubelet Readiness probe failed: Get \"http://10.1.4.204:8012/\": context deadline exceeded (Client.Timeout exceeded while awaiting headers) ```","title":"Using model_uri"},{"location":"notebooks/04_model_training_pipeline.html#using-docker-image","text":"create docker ``` sudo apt-get install python3.10-venv python3.10 -m venv python310venv source python310venv/bin/activate pip install mlflow pip install boto3 export AWS_ACCESS_KEY_ID=minioadmin export AWS_SECRET_ACCESS_KEY=minioadmin export MLFLOW_S3_ENDPOINT_URL=http://10.152.183.156:9000 mlflow models build-docker -m s3://mlflow/1/777cf64c922149a4b77c85987865deb0/artifacts/success6g_model -n 5uperpalo/mlflow-success6g --enable-mlserver ``` generate token and login to DockerHub, [generate DockerHub token](https://hub.docker.com/settings/security?generateToken=true) and login ``` (python310venv) pmulinka@iesc-gpu3:~/scripts$ docker login -u 5uperpalo Password: WARNING! Your password will be stored unencrypted in /home/pmulinka/.docker/config.json. Configure a credential helper to remove this warning. See https://docs.docker.com/engine/reference/commandline/login/#credentials-store Login Succeeded ``` push the image to dockerhub ``` (python310venv) pmulinka@iesc-gpu3:~/scripts$ docker push 5uperpalo/mlflow-success6g Using default tag: latest The push refers to repository [docker.io/5uperpalo/mlflow-success6g] e5f76b582f6c: Pushed 624821c51019: Pushed 93256a85bb86: Pushed 2f62dcdb5ae7: Pushed 1146ddf03b42: Pushed 639ddc87d13f: Pushed 4f18e59d57c4: Pushed 5761e01a334a: Pushed f31b6bde159a: Pushed c25ef1a18d31: Pushed 40a774d6610c: Pushed a2b215b4258b: Pushed b56a9203d2a0: Pushed 2536dbfb2294: Pushed 67ea405a7d49: Pushed a81c3606ee5c: Pushed 4a1518ebc26e: Mounted from library/ubuntu latest: digest: sha256:57f653a3c8d63519c07dccffc6eb12dba5006db07ebcd869467ce752fb1b0bda size: 3899 ``` specify configuration of the deployment in the file `mlflow-kserve-success6g_model_uri.yaml` ``` apiVersion: \"serving.kserve.io/v1beta1\" kind: \"InferenceService\" metadata: name: \"mlflow-success6g\" namespace: \"mlflow-kserve-success6g\" spec: predictor: containers: - name: \"mlflow-success6g\" image: \"5uperpalo/mlflow-success6g\" ports: - containerPort: 8080 protocol: TCP #env: #- name: PROTOCOL #value: \"v2\" resources: limits: cpu: 2 memory: 8Gi requests: cpu: 2 memory: 8Gi ``` deploy ``` pmulinka@saiacheron:~/kubernetes/kserve$ kubectl apply -f mlflow-kserve-success6g_docker_image.yaml inferenceservice.serving.kserve.io/mlflow-success6g created ``` check status of deployment ``` pmulinka@saiacheron:~$ kubectl get inferenceservice -n mlflow-kserve-success6g NAME URL READY PREV LATEST PREVROLLEDOUTREVISION LATESTREADYREVISION AGE mlflow-success6g http://mlflow-success6g.mlflow-kserve-success6g.svc.cluster.local False 100 mlflow-success6g-predictor-00001 6m4s pmulinka@saiacheron:~$ kubectl get pods -n mlflow-kserve-success6g NAME READY STATUS RESTARTS AGE mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 2/2 Running 1 (2m14s ago) 4m47s pmulinka@saiacheron:~$ kubectl get inferenceservice mlflow-success6g -n mlflow-kserve-success6g -o yaml apiVersion: serving.kserve.io/v1beta1 kind: InferenceService metadata: annotations: kubectl.kubernetes.io/last-applied-configuration: | {\"apiVersion\":\"serving.kserve.io/v1beta1\",\"kind\":\"InferenceService\",\"metadata\":{\"annotations\":{},\"name\":\"mlflow-success6g\",\"namespace\":\"mlflow-kserve-success6g\"},\"spec\":{\"predictor\":{\"containers\":[{\"image\":\"5uperpalo/mlflow-success6g\",\"name\":\"mlflow-success6g\",\"ports\":[{\"containerPort\":8080,\"protocol\":\"TCP\"}],\"resources\":{\"limits\":{\"cpu\":2,\"memory\":\"8Gi\"},\"requests\":{\"cpu\":2,\"memory\":\"8Gi\"}}}]}}} creationTimestamp: \"2024-05-28T14:33:05Z\" finalizers: - inferenceservice.finalizers generation: 1 name: mlflow-success6g namespace: mlflow-kserve-success6g resourceVersion: \"76010518\" uid: b9573ff3-0f67-4650-a0c7-51941a0fffe2 spec: predictor: containers: - image: 5uperpalo/mlflow-success6g name: kserve-container ports: - containerPort: 8080 protocol: TCP resources: limits: cpu: \"2\" memory: 8Gi requests: cpu: \"2\" memory: 8Gi status: address: url: http://mlflow-success6g.mlflow-kserve-success6g.svc.cluster.local components: predictor: address: url: http://mlflow-success6g-predictor.mlflow-kserve-success6g.svc.cluster.local latestCreatedRevision: mlflow-success6g-predictor-00001 latestReadyRevision: mlflow-success6g-predictor-00001 latestRolledoutRevision: mlflow-success6g-predictor-00001 traffic: - latestRevision: true percent: 100 revisionName: mlflow-success6g-predictor-00001 url: http://mlflow-success6g-predictor.mlflow-kserve-success6g.svc.cluster.local conditions: - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: Predictor ingress not created status: \"False\" type: IngressReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: PredictorConfigurationReady not ready severity: Info status: \"False\" type: LatestDeploymentReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" message: 'Revision \"mlflow-success6g-predictor-00001\" failed with message: Container failed with: container exited with no error.' reason: RevisionFailed severity: Info status: \"False\" type: PredictorConfigurationReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" message: Revision \"mlflow-success6g-predictor-00001\" failed to become ready. reason: RevisionMissing status: \"False\" type: PredictorReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" message: Revision \"mlflow-success6g-predictor-00001\" failed to become ready. reason: RevisionMissing severity: Info status: \"False\" type: PredictorRouteReady - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: Predictor ingress not created status: \"False\" type: Ready - lastTransitionTime: \"2024-05-28T14:36:11Z\" reason: PredictorRouteReady not ready severity: Info status: \"False\" type: RoutesReady modelStatus: copies: failedCopies: 0 totalCopies: 1 states: activeModelState: Loaded targetModelState: Pending transitionStatus: InProgress observedGeneration: 1 url: http://mlflow-success6g.mlflow-kserve-success6g.svc.cluster.local pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get events -n mlflow-kserve-success6g LAST SEEN TYPE REASON OBJECT MESSAGE 2m28s Warning BackOff pod/mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 Back-off restarting failed container kserve-container in pod mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7_mlflow-kserve-success6g(1ad2d6da-3a40-4b14-956a-0faee3ca9b3c) 2m28s Warning InternalError revision/mlflow-success6g-predictor-00001 failed to update PA \"mlflow-success6g-predictor-00001\": Operation cannot be fulfilled on podautoscalers.autoscaling.internal.knative.dev \"mlflow-success6g-predictor-00001\": the object has been modified; please apply your changes to the latest version and try again ``` debugging ``` # https://kserve.github.io/website/0.10/developer/debug/ pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get ksvc -n mlflow-kserve-success6g NAME URL LATESTCREATED LATESTREADY READY REASON mlflow-success6g-predictor http://mlflow-success6g-predictor.mlflow-kserve-success6g.svc.cluster.local mlflow-success6g-predictor-00001 mlflow-success6g-predictor-00001 False RevisionMissing # RevisionMissing Error pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get revision $(kubectl get configuration mlflow-success6g-predictor -n mlflow-kserve-success6g --output jsonpath=\"{.status.latestCreatedRevisionName}\") Error from server (NotFound): revisions.serving.knative.dev \"mlflow-success6g-predictor-00001\" not found pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get pod -l serving.kserve.io/inferenceservice=mlflow-success6g -n mlflow-kserve-success6g NAME READY STATUS RESTARTS AGE mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 0/2 CrashLoopBackOff 8 (4m51s ago) 43m pmulinka@saiacheron:~/kubernetes/kserve$ kubectl get pod -l serving.kserve.io/inferenceservice=mlflow-success6g -n mlflow-kserve-success6g NAME READY STATUS RESTARTS AGE mlflow-success6g-predictor-00001-deployment-8694fffb55-9wnb7 2/2 Running 9 (7m7s ago) 45m pmulinka@saiacheron:~/kubernetes/kserve$ kubectl logs -l model=mlflow-success6g -n mlflow-kserve-success6g -c storage-initializer No resources found in mlflow-kserve-success6g namespace. pmulinka@saiacheron:~/kubernetes/kserve$ kubectl delete inferenceservice mlflow-success6g -n mlflow-kserve-success6g inferenceservice.serving.kserve.io \"mlflow-success6g\" deleted ```","title":"Using docker image"},{"location":"notebooks/04_model_training_pipeline.html#appendix","text":"output from making docker ``` (python310venv) pmulinka@iesc-gpu3:~/scripts$ mlflow models build-docker -m s3://mlflow/1/777cf64c922149a4b77c85987865deb0/artifacts/success6g_model -n 5uperpalo/mlflow-success6g --enable-mlserver 2024/05/16 22:20:25 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function' 2024/05/16 22:20:25 INFO mlflow.pyfunc.backend: Building docker image with name 5uperpalo/mlflow-success6g #0 building with \"default\" instance using docker driver #1 [internal] load build definition from Dockerfile #1 transferring dockerfile: 2.53kB done #1 DONE 0.1s #2 [internal] load metadata for docker.io/library/ubuntu:20.04 #2 DONE 1.6s #3 [internal] load .dockerignore #3 transferring context: 2B done #3 DONE 0.0s #4 [ 1/17] FROM docker.io/library/ubuntu:20.04@sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b #4 resolve docker.io/library/ubuntu:20.04@sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b 0.0s done #4 ... #5 [internal] load build context #5 transferring context: 197.37kB done #5 DONE 0.1s #4 [ 1/17] FROM docker.io/library/ubuntu:20.04@sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b #4 sha256:2abc4dfd83182546da40dfae3e391db0810ad4a130fb5a887c6ceb3df8e1d310 2.29kB / 2.29kB done #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 0B / 27.51MB 0.2s #4 sha256:874aca52f79ae5f8258faff03e10ce99ae836f6e7d2df6ecd3da5c1cad3a912b 1.13kB / 1.13kB done #4 sha256:cc9cc8169c9517ae035cf293b15f06922cb8c6c864d625a72b7b18667f264b70 424B / 424B done #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 3.15MB / 27.51MB 0.4s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 8.39MB / 27.51MB 0.7s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 11.53MB / 27.51MB 0.9s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 16.78MB / 27.51MB 1.2s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 22.02MB / 27.51MB 1.5s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 25.17MB / 27.51MB 1.7s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 27.26MB / 27.51MB 1.8s #4 sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 27.51MB / 27.51MB 1.9s done #4 extracting sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 #4 extracting sha256:d4c3c94e5e10ed15503bda7e145a3652ee935c0b2e9de9b5c98df7ec0a0cd925 1.8s done #4 DONE 4.0s #6 [ 2/17] RUN apt-get -y update && DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get install -y --no-install-recommends wget curl nginx ca-certificates bzip2 build-essential cmake git-core #6 0.681 Get:1 http://security.ubuntu.com/ubuntu focal-security InRelease [114 kB] #6 0.682 Get:2 http://archive.ubuntu.com/ubuntu focal InRelease [265 kB] #6 1.324 Get:3 http://archive.ubuntu.com/ubuntu focal-updates InRelease [114 kB] #6 1.432 Get:4 http://security.ubuntu.com/ubuntu focal-security/universe amd64 Packages [1205 kB] #6 1.476 Get:5 http://archive.ubuntu.com/ubuntu focal-backports InRelease [108 kB] #6 1.690 Get:6 http://archive.ubuntu.com/ubuntu focal/restricted amd64 Packages [33.4 kB] #6 1.838 Get:7 http://archive.ubuntu.com/ubuntu focal/multiverse amd64 Packages [177 kB] #6 1.897 Get:8 http://archive.ubuntu.com/ubuntu focal/main amd64 Packages [1275 kB] #6 2.177 Get:9 http://security.ubuntu.com/ubuntu focal-security/restricted amd64 Packages [3608 kB] #6 2.537 Get:10 http://archive.ubuntu.com/ubuntu focal/universe amd64 Packages [11.3 MB] #6 3.807 Get:11 http://security.ubuntu.com/ubuntu focal-security/main amd64 Packages [3669 kB] #6 5.468 Get:12 http://security.ubuntu.com/ubuntu focal-security/multiverse amd64 Packages [29.8 kB] #6 7.649 Get:13 http://archive.ubuntu.com/ubuntu focal-updates/restricted amd64 Packages [3758 kB] #6 9.304 Get:14 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 Packages [1502 kB] #6 9.973 Get:15 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 Packages [4143 kB] #6 12.14 Get:16 http://archive.ubuntu.com/ubuntu focal-updates/multiverse amd64 Packages [32.5 kB] #6 12.15 Get:17 http://archive.ubuntu.com/ubuntu focal-backports/main amd64 Packages [55.2 kB] #6 12.18 Get:18 http://archive.ubuntu.com/ubuntu focal-backports/universe amd64 Packages [28.6 kB] #6 12.44 Fetched 31.5 MB in 12s (2621 kB/s) #6 12.44 Reading package lists... #6 13.60 Reading package lists... #6 14.67 Building dependency tree... #6 14.83 Reading state information... #6 15.04 bzip2 is already the newest version (1.0.8-2). #6 15.04 The following additional packages will be installed: #6 15.04 binutils binutils-common binutils-x86-64-linux-gnu cmake-data cpp cpp-9 #6 15.04 dpkg-dev fontconfig-config fonts-dejavu-core g++ g++-9 gcc gcc-9 gcc-9-base #6 15.04 git-man iproute2 libarchive13 libasan5 libasn1-8-heimdal libatomic1 #6 15.04 libbinutils libbrotli1 libbsd0 libc-dev-bin libc6-dev libcap2 libcap2-bin #6 15.04 libcc1-0 libcrypt-dev libctf-nobfd0 libctf0 libcurl3-gnutls libcurl4 #6 15.04 libdpkg-perl libelf1 liberror-perl libexpat1 libfontconfig1 libfreetype6 #6 15.04 libgcc-9-dev libgd3 libgdbm-compat4 libgdbm6 libgomp1 libgssapi-krb5-2 #6 15.04 libgssapi3-heimdal libhcrypto4-heimdal libheimbase1-heimdal #6 15.04 libheimntlm0-heimdal libhx509-5-heimdal libicu66 libisl22 libitm1 libjbig0 #6 15.04 libjpeg-turbo8 libjpeg8 libjsoncpp1 libk5crypto3 libkeyutils1 #6 15.04 libkrb5-26-heimdal libkrb5-3 libkrb5support0 libldap-2.4-2 libldap-common #6 15.04 liblsan0 libmnl0 libmpc3 libmpfr6 libnghttp2-14 #6 15.04 libnginx-mod-http-image-filter libnginx-mod-http-xslt-filter #6 15.04 libnginx-mod-mail libnginx-mod-stream libperl5.30 libpng16-16 libpsl5 #6 15.04 libquadmath0 librhash0 libroken18-heimdal librtmp1 libsasl2-2 #6 15.04 libsasl2-modules-db libsqlite3-0 libssh-4 libssl1.1 libstdc++-9-dev libtiff5 #6 15.04 libtsan0 libubsan1 libuv1 libwebp6 libwind0-heimdal libx11-6 libx11-data #6 15.04 libxau6 libxcb1 libxdmcp6 libxml2 libxpm4 libxslt1.1 libxtables12 #6 15.04 linux-libc-dev make nginx-common nginx-core openssl patch perl #6 15.04 perl-modules-5.30 tzdata ucf xz-utils #6 15.04 Suggested packages: #6 15.04 binutils-doc cmake-doc ninja-build cpp-doc gcc-9-locales debian-keyring #6 15.04 g++-multilib g++-9-multilib gcc-9-doc gcc-multilib manpages-dev autoconf #6 15.04 automake libtool flex bison gdb gcc-doc gcc-9-multilib gettext-base #6 15.04 git-daemon-run | git-daemon-sysvinit git-doc git-el git-email git-gui gitk #6 15.04 gitweb git-cvs git-mediawiki git-svn iproute2-doc lrzip glibc-doc gnupg #6 15.04 | gnupg2 bzr libgd-tools gdbm-l10n krb5-doc krb5-user libstdc++-9-doc #6 15.04 make-doc fcgiwrap nginx-doc ssl-cert ed diffutils-doc perl-doc #6 15.04 libterm-readline-gnu-perl | libterm-readline-perl-perl libb-debug-perl #6 15.04 liblocale-codes-perl #6 15.04 Recommended packages: #6 15.04 fakeroot gnupg | gnupg2 libalgorithm-merge-perl less ssh-client libatm1 #6 15.04 manpages manpages-dev libpam-cap libfile-fcntllock-perl #6 15.04 liblocale-gettext-perl krb5-locales publicsuffix libsasl2-modules netbase #6 15.30 The following NEW packages will be installed: #6 15.30 binutils binutils-common binutils-x86-64-linux-gnu build-essential #6 15.30 ca-certificates cmake cmake-data cpp cpp-9 curl dpkg-dev fontconfig-config #6 15.30 fonts-dejavu-core g++ g++-9 gcc gcc-9 gcc-9-base git git-man iproute2 #6 15.30 libarchive13 libasan5 libasn1-8-heimdal libatomic1 libbinutils libbrotli1 #6 15.30 libbsd0 libc-dev-bin libc6-dev libcap2 libcap2-bin libcc1-0 libcrypt-dev #6 15.30 libctf-nobfd0 libctf0 libcurl3-gnutls libcurl4 libdpkg-perl libelf1 #6 15.30 liberror-perl libexpat1 libfontconfig1 libfreetype6 libgcc-9-dev libgd3 #6 15.30 libgdbm-compat4 libgdbm6 libgomp1 libgssapi-krb5-2 libgssapi3-heimdal #6 15.30 libhcrypto4-heimdal libheimbase1-heimdal libheimntlm0-heimdal #6 15.30 libhx509-5-heimdal libicu66 libisl22 libitm1 libjbig0 libjpeg-turbo8 #6 15.30 libjpeg8 libjsoncpp1 libk5crypto3 libkeyutils1 libkrb5-26-heimdal libkrb5-3 #6 15.30 libkrb5support0 libldap-2.4-2 libldap-common liblsan0 libmnl0 libmpc3 #6 15.30 libmpfr6 libnghttp2-14 libnginx-mod-http-image-filter #6 15.30 libnginx-mod-http-xslt-filter libnginx-mod-mail libnginx-mod-stream #6 15.30 libperl5.30 libpng16-16 libpsl5 libquadmath0 librhash0 libroken18-heimdal #6 15.30 librtmp1 libsasl2-2 libsasl2-modules-db libsqlite3-0 libssh-4 libssl1.1 #6 15.30 libstdc++-9-dev libtiff5 libtsan0 libubsan1 libuv1 libwebp6 libwind0-heimdal #6 15.30 libx11-6 libx11-data libxau6 libxcb1 libxdmcp6 libxml2 libxpm4 libxslt1.1 #6 15.30 libxtables12 linux-libc-dev make nginx nginx-common nginx-core openssl patch #6 15.30 perl perl-modules-5.30 tzdata ucf wget xz-utils #6 15.41 0 upgraded, 119 newly installed, 0 to remove and 0 not upgraded. #6 15.41 Need to get 82.1 MB of archives. #6 15.41 After this operation, 389 MB of additional disk space will be used. #6 15.41 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 perl-modules-5.30 all 5.30.0-9ubuntu0.5 [2739 kB] #6 16.07 Get:2 http://archive.ubuntu.com/ubuntu focal/main amd64 libgdbm6 amd64 1.18.1-5 [27.4 kB] #6 16.07 Get:3 http://archive.ubuntu.com/ubuntu focal/main amd64 libgdbm-compat4 amd64 1.18.1-5 [6244 B] #6 16.07 Get:4 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libperl5.30 amd64 5.30.0-9ubuntu0.5 [3941 kB] #6 16.76 Get:5 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 perl amd64 5.30.0-9ubuntu0.5 [224 kB] #6 16.79 Get:6 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libssl1.1 amd64 1.1.1f-1ubuntu2.22 [1323 kB] #6 17.04 Get:7 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openssl amd64 1.1.1f-1ubuntu2.22 [622 kB] #6 17.15 Get:8 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 ca-certificates all 20230311ubuntu0.20.04.1 [152 kB] #6 17.18 Get:9 http://archive.ubuntu.com/ubuntu focal/main amd64 libbsd0 amd64 0.10.0-1 [45.4 kB] #6 17.20 Get:10 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcap2 amd64 1:2.32-1ubuntu0.1 [15.8 kB] #6 17.20 Get:11 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libelf1 amd64 0.176-1.1ubuntu0.1 [44.2 kB] #6 17.33 Get:12 http://archive.ubuntu.com/ubuntu focal/main amd64 libmnl0 amd64 1.0.4-2 [12.3 kB] #6 17.38 Get:13 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxtables12 amd64 1.8.4-3ubuntu2.1 [28.7 kB] #6 17.42 Get:14 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcap2-bin amd64 1:2.32-1ubuntu0.1 [26.2 kB] #6 17.46 Get:15 http://archive.ubuntu.com/ubuntu focal/main amd64 iproute2 amd64 5.5.0-1ubuntu1 [858 kB] #6 20.61 Get:16 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libexpat1 amd64 2.2.9-1ubuntu0.6 [74.6 kB] #6 21.00 Get:17 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 tzdata all 2024a-0ubuntu0.20.04 [301 kB] #6 22.38 Get:18 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libicu66 amd64 66.1-2ubuntu2.1 [8515 kB] #6 51.24 Get:19 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsqlite3-0 amd64 3.31.1-4ubuntu0.6 [549 kB] #6 53.10 Get:20 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxml2 amd64 2.9.10+dfsg-5ubuntu0.20.04.7 [640 kB] #6 55.32 Get:21 http://archive.ubuntu.com/ubuntu focal/main amd64 ucf all 3.0038+nmu1 [51.6 kB] #6 55.50 Get:21 http://archive.ubuntu.com/ubuntu focal/main amd64 ucf all 3.0038+nmu1 [51.6 kB] #6 55.60 Get:22 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 xz-utils amd64 5.2.4-1ubuntu1.1 [82.6 kB] #6 55.66 Get:23 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkrb5support0 amd64 1.17-6ubuntu4.4 [31.0 kB] #6 55.70 Get:24 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libk5crypto3 amd64 1.17-6ubuntu4.4 [79.9 kB] #6 55.72 Get:25 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkeyutils1 amd64 1.6-6ubuntu1.1 [10.3 kB] #6 55.73 Get:26 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkrb5-3 amd64 1.17-6ubuntu4.4 [330 kB] #6 55.80 Get:27 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgssapi-krb5-2 amd64 1.17-6ubuntu4.4 [121 kB] #6 55.82 Get:28 http://archive.ubuntu.com/ubuntu focal/main amd64 libpng16-16 amd64 1.6.37-2 [179 kB] #6 55.86 Get:29 http://archive.ubuntu.com/ubuntu focal/main amd64 libpsl5 amd64 0.21.0-1ubuntu1 [51.5 kB] #6 55.86 Get:30 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libuv1 amd64 1.34.2-1ubuntu1.5 [80.9 kB] #6 55.88 Get:31 http://archive.ubuntu.com/ubuntu focal/main amd64 libxau6 amd64 1:1.0.9-0ubuntu1 [7488 B] #6 55.96 Get:32 http://archive.ubuntu.com/ubuntu focal/main amd64 libxdmcp6 amd64 1:1.1.3-0ubuntu1 [10.6 kB] #6 56.01 Get:33 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb1 amd64 1.14-2 [44.7 kB] #6 56.08 Get:34 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-data all 2:1.6.9-2ubuntu1.6 [114 kB] #6 56.15 Get:35 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-6 amd64 2:1.6.9-2ubuntu1.6 [577 kB] #6 56.26 Get:36 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 wget amd64 1.20.3-1ubuntu2 [348 kB] #6 56.32 Get:37 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 binutils-common amd64 2.34-6ubuntu1.9 [208 kB] #6 56.37 Get:38 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libbinutils amd64 2.34-6ubuntu1.9 [475 kB] #6 56.46 Get:39 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libctf-nobfd0 amd64 2.34-6ubuntu1.9 [48.2 kB] #6 56.47 Get:40 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libctf0 amd64 2.34-6ubuntu1.9 [46.6 kB] #6 56.48 Get:41 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 binutils-x86-64-linux-gnu amd64 2.34-6ubuntu1.9 [1614 kB] #6 56.76 Get:42 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 binutils amd64 2.34-6ubuntu1.9 [3380 B] #6 56.85 Get:43 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libc-dev-bin amd64 2.31-0ubuntu9.15 [71.8 kB] #6 57.00 Get:44 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 linux-libc-dev amd64 5.4.0-182.202 [1134 kB] #6 57.28 Get:45 http://archive.ubuntu.com/ubuntu focal/main amd64 libcrypt-dev amd64 1:4.4.10-10ubuntu4 [104 kB] #6 57.30 Get:46 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libc6-dev amd64 2.31-0ubuntu9.15 [2519 kB] #6 57.75 Get:47 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 gcc-9-base amd64 9.4.0-1ubuntu1~20.04.2 [18.9 kB] #6 57.75 Get:48 http://archive.ubuntu.com/ubuntu focal/main amd64 libisl22 amd64 0.22.1-1 [592 kB] #6 57.87 Get:49 http://archive.ubuntu.com/ubuntu focal/main amd64 libmpfr6 amd64 4.0.2-1 [240 kB] #6 57.91 Get:50 http://archive.ubuntu.com/ubuntu focal/main amd64 libmpc3 amd64 1.1.0-1 [40.8 kB] #6 57.92 Get:51 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 cpp-9 amd64 9.4.0-1ubuntu1~20.04.2 [7502 kB] #6 59.27 Get:52 http://archive.ubuntu.com/ubuntu focal/main amd64 cpp amd64 4:9.3.0-1ubuntu2 [27.6 kB] #6 59.27 Get:53 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcc1-0 amd64 10.5.0-1ubuntu1~20.04 [48.8 kB] #6 59.36 Get:54 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgomp1 amd64 10.5.0-1ubuntu1~20.04 [102 kB] #6 59.54 Get:55 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libitm1 amd64 10.5.0-1ubuntu1~20.04 [26.2 kB] #6 59.55 Get:56 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libatomic1 amd64 10.5.0-1ubuntu1~20.04 [9284 B] #6 59.55 Get:57 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libasan5 amd64 9.4.0-1ubuntu1~20.04.2 [2752 kB] #6 60.09 Get:58 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 liblsan0 amd64 10.5.0-1ubuntu1~20.04 [835 kB] #6 60.24 Get:59 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libtsan0 amd64 10.5.0-1ubuntu1~20.04 [2016 kB] #6 60.61 Get:60 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libubsan1 amd64 10.5.0-1ubuntu1~20.04 [785 kB] #6 60.76 Get:61 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libquadmath0 amd64 10.5.0-1ubuntu1~20.04 [146 kB] #6 60.78 Get:62 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgcc-9-dev amd64 9.4.0-1ubuntu1~20.04.2 [2359 kB] #6 61.28 Get:63 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 gcc-9 amd64 9.4.0-1ubuntu1~20.04.2 [8276 kB] #6 62.82 Get:64 http://archive.ubuntu.com/ubuntu focal/main amd64 gcc amd64 4:9.3.0-1ubuntu2 [5208 B] #6 62.91 Get:65 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libstdc++-9-dev amd64 9.4.0-1ubuntu1~20.04.2 [1722 kB] #6 63.39 Get:66 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 g++-9 amd64 9.4.0-1ubuntu1~20.04.2 [8421 kB] #6 65.04 Get:67 http://archive.ubuntu.com/ubuntu focal/main amd64 g++ amd64 4:9.3.0-1ubuntu2 [1604 B] #6 65.06 Get:68 http://archive.ubuntu.com/ubuntu focal/main amd64 make amd64 4.2.1-1.2 [162 kB] #6 65.07 Get:69 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdpkg-perl all 1.19.7ubuntu3.2 [231 kB] #6 65.16 Get:70 http://archive.ubuntu.com/ubuntu focal/main amd64 patch amd64 2.7.6-6 [105 kB] #6 65.20 Get:71 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 dpkg-dev all 1.19.7ubuntu3.2 [679 kB] #6 65.32 Get:72 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 build-essential amd64 12.8ubuntu1.1 [4664 B] #6 65.32 Get:73 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 cmake-data all 3.16.3-1ubuntu1.20.04.1 [1613 kB] #6 65.61 Get:74 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libarchive13 amd64 3.4.0-2ubuntu1.2 [327 kB] #6 65.66 Get:75 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libbrotli1 amd64 1.0.7-6ubuntu0.1 [267 kB] #6 65.80 Get:76 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libroken18-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [42.5 kB] #6 65.94 Get:77 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libasn1-8-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [181 kB] #6 66.25 Get:78 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libheimbase1-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [30.4 kB] #6 66.29 Get:79 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libhcrypto4-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [88.1 kB] #6 66.55 Get:80 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwind0-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [47.7 kB] #6 66.71 Get:81 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libhx509-5-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [107 kB] #6 67.13 Get:82 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libkrb5-26-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [207 kB] #6 67.89 Get:83 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libheimntlm0-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [15.1 kB] #6 67.92 Get:84 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgssapi3-heimdal amd64 7.7.0+dfsg-1ubuntu1.4 [96.5 kB] #6 68.05 Get:85 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsasl2-modules-db amd64 2.1.27+dfsg-2ubuntu0.1 [14.7 kB] #6 68.06 Get:86 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsasl2-2 amd64 2.1.27+dfsg-2ubuntu0.1 [49.3 kB] #6 68.29 Get:87 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libldap-common all 2.4.49+dfsg-2ubuntu1.10 [16.5 kB] #6 68.38 Get:88 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libldap-2.4-2 amd64 2.4.49+dfsg-2ubuntu1.10 [155 kB] #6 68.50 Get:89 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnghttp2-14 amd64 1.40.0-1ubuntu0.3 [79.9 kB] #6 68.53 Get:90 http://archive.ubuntu.com/ubuntu focal/main amd64 librtmp1 amd64 2.4+20151223.gitfa8646d.1-2build1 [54.9 kB] #6 68.54 Get:91 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libssh-4 amd64 0.9.3-2ubuntu2.5 [171 kB] #6 68.57 Get:92 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcurl4 amd64 7.68.0-1ubuntu2.22 [235 kB] #6 68.62 Get:93 http://archive.ubuntu.com/ubuntu focal/main amd64 libjsoncpp1 amd64 1.7.4-3.1ubuntu2 [75.6 kB] #6 68.63 Get:94 http://archive.ubuntu.com/ubuntu focal/main amd64 librhash0 amd64 1.3.9-1 [113 kB] #6 68.65 Get:95 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 cmake amd64 3.16.3-1ubuntu1.20.04.1 [3668 kB] #6 69.31 Get:96 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 curl amd64 7.68.0-1ubuntu2.22 [161 kB] #6 69.33 Get:97 http://archive.ubuntu.com/ubuntu focal/main amd64 fonts-dejavu-core all 2.37-1 [1041 kB] #6 69.60 Get:98 http://archive.ubuntu.com/ubuntu focal/main amd64 fontconfig-config all 2.13.1-2ubuntu3 [28.8 kB] #6 69.70 Get:99 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcurl3-gnutls amd64 7.68.0-1ubuntu2.22 [233 kB] #6 69.83 Get:100 http://archive.ubuntu.com/ubuntu focal/main amd64 liberror-perl all 0.17029-1 [26.5 kB] #6 69.84 Get:101 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 git-man all 1:2.25.1-1ubuntu3.11 [887 kB] #6 70.00 Get:102 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 git amd64 1:2.25.1-1ubuntu3.11 [4605 kB] #6 70.90 Get:103 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libfreetype6 amd64 2.10.1-2ubuntu0.3 [341 kB] #6 70.96 Get:104 http://archive.ubuntu.com/ubuntu focal/main amd64 libfontconfig1 amd64 2.13.1-2ubuntu3 [114 kB] #6 70.97 Get:105 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libjpeg-turbo8 amd64 2.0.3-0ubuntu1.20.04.3 [118 kB] #6 71.00 Get:106 http://archive.ubuntu.com/ubuntu focal/main amd64 libjpeg8 amd64 8c-2ubuntu8 [2194 B] #6 71.00 Get:107 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libjbig0 amd64 2.1-3.1ubuntu0.20.04.1 [27.3 kB] #6 71.00 Get:108 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwebp6 amd64 0.6.1-2ubuntu0.20.04.3 [185 kB] #6 71.14 Get:109 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libtiff5 amd64 4.1.0+git191117-2ubuntu0.20.04.12 [164 kB] #6 71.36 Get:110 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxpm4 amd64 1:3.5.12-1ubuntu0.20.04.2 [34.9 kB] #6 71.36 Get:111 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgd3 amd64 2.2.5-5.2ubuntu2.1 [118 kB] #6 71.39 Get:112 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 nginx-common all 1.18.0-0ubuntu1.4 [37.7 kB] #6 71.40 Get:113 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnginx-mod-http-image-filter amd64 1.18.0-0ubuntu1.4 [14.8 kB] #6 71.41 Get:114 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxslt1.1 amd64 1.1.34-4ubuntu0.20.04.1 [151 kB] #6 71.43 Get:115 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnginx-mod-http-xslt-filter amd64 1.18.0-0ubuntu1.4 [13.0 kB] #6 71.44 Get:116 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnginx-mod-mail amd64 1.18.0-0ubuntu1.4 [42.9 kB] #6 71.47 Get:117 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnginx-mod-stream amd64 1.18.0-0ubuntu1.4 [67.4 kB] #6 71.49 Get:118 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 nginx-core amd64 1.18.0-0ubuntu1.4 [425 kB] #6 71.58 Get:119 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 nginx all 1.18.0-0ubuntu1.4 [3620 B] #6 71.74 debconf: delaying package configuration, since apt-utils is not installed #6 71.80 Fetched 82.1 MB in 56s (1459 kB/s) #6 71.85 Selecting previously unselected package perl-modules-5.30. #6 71.85 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 4124 files and directories currently installed.) #6 71.86 Preparing to unpack .../000-perl-modules-5.30_5.30.0-9ubuntu0.5_all.deb ... #6 71.87 Unpacking perl-modules-5.30 (5.30.0-9ubuntu0.5) ... #6 72.25 Selecting previously unselected package libgdbm6:amd64. #6 72.25 Preparing to unpack .../001-libgdbm6_1.18.1-5_amd64.deb ... #6 72.26 Unpacking libgdbm6:amd64 (1.18.1-5) ... #6 72.33 Selecting previously unselected package libgdbm-compat4:amd64. #6 72.33 Preparing to unpack .../002-libgdbm-compat4_1.18.1-5_amd64.deb ... #6 72.34 Unpacking libgdbm-compat4:amd64 (1.18.1-5) ... #6 72.41 Selecting previously unselected package libperl5.30:amd64. #6 72.41 Preparing to unpack .../003-libperl5.30_5.30.0-9ubuntu0.5_amd64.deb ... #6 72.42 Unpacking libperl5.30:amd64 (5.30.0-9ubuntu0.5) ... #6 72.92 Selecting previously unselected package perl. #6 72.93 Preparing to unpack .../004-perl_5.30.0-9ubuntu0.5_amd64.deb ... #6 72.95 Unpacking perl (5.30.0-9ubuntu0.5) ... #6 73.07 Selecting previously unselected package libssl1.1:amd64. #6 73.08 Preparing to unpack .../005-libssl1.1_1.1.1f-1ubuntu2.22_amd64.deb ... #6 73.08 Unpacking libssl1.1:amd64 (1.1.1f-1ubuntu2.22) ... #6 73.27 Selecting previously unselected package openssl. #6 73.28 Preparing to unpack .../006-openssl_1.1.1f-1ubuntu2.22_amd64.deb ... #6 73.28 Unpacking openssl (1.1.1f-1ubuntu2.22) ... #6 73.43 Selecting previously unselected package ca-certificates. #6 73.43 Preparing to unpack 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75.93 Preparing to unpack .../022-libkrb5support0_1.17-6ubuntu4.4_amd64.deb ... #6 75.93 Unpacking libkrb5support0:amd64 (1.17-6ubuntu4.4) ... #6 76.00 Selecting previously unselected package libk5crypto3:amd64. #6 76.01 Preparing to unpack .../023-libk5crypto3_1.17-6ubuntu4.4_amd64.deb ... #6 76.01 Unpacking libk5crypto3:amd64 (1.17-6ubuntu4.4) ... #6 76.10 Selecting previously unselected package libkeyutils1:amd64. #6 76.10 Preparing to unpack .../024-libkeyutils1_1.6-6ubuntu1.1_amd64.deb ... #6 76.11 Unpacking libkeyutils1:amd64 (1.6-6ubuntu1.1) ... #6 76.17 Selecting previously unselected package libkrb5-3:amd64. #6 76.18 Preparing to unpack .../025-libkrb5-3_1.17-6ubuntu4.4_amd64.deb ... #6 76.18 Unpacking libkrb5-3:amd64 (1.17-6ubuntu4.4) ... #6 76.31 Selecting previously unselected package libgssapi-krb5-2:amd64. #6 76.31 Preparing to unpack .../026-libgssapi-krb5-2_1.17-6ubuntu4.4_amd64.deb ... #6 76.32 Unpacking libgssapi-krb5-2:amd64 (1.17-6ubuntu4.4) ... #6 76.41 Selecting previously unselected package libpng16-16:amd64. #6 76.41 Preparing to unpack .../027-libpng16-16_1.6.37-2_amd64.deb ... #6 76.42 Unpacking libpng16-16:amd64 (1.6.37-2) ... #6 76.52 Selecting previously unselected package libpsl5:amd64. #6 76.52 Preparing to unpack .../028-libpsl5_0.21.0-1ubuntu1_amd64.deb ... #6 76.53 Unpacking libpsl5:amd64 (0.21.0-1ubuntu1) ... #6 76.61 Selecting previously unselected package libuv1:amd64. #6 76.61 Preparing to unpack .../029-libuv1_1.34.2-1ubuntu1.5_amd64.deb ... #6 76.62 Unpacking libuv1:amd64 (1.34.2-1ubuntu1.5) ... #6 76.70 Selecting previously unselected package libxau6:amd64. #6 76.71 Preparing to unpack .../030-libxau6_1%3a1.0.9-0ubuntu1_amd64.deb ... #6 76.71 Unpacking libxau6:amd64 (1:1.0.9-0ubuntu1) ... #6 76.78 Selecting previously unselected package libxdmcp6:amd64. #6 76.79 Preparing to unpack .../031-libxdmcp6_1%3a1.1.3-0ubuntu1_amd64.deb ... #6 76.79 Unpacking libxdmcp6:amd64 (1:1.1.3-0ubuntu1) ... #6 76.86 Selecting previously unselected package libxcb1:amd64. #6 76.87 Preparing to unpack .../032-libxcb1_1.14-2_amd64.deb ... #6 76.87 Unpacking libxcb1:amd64 (1.14-2) ... #6 76.95 Selecting previously unselected package libx11-data. #6 76.95 Preparing to unpack .../033-libx11-data_2%3a1.6.9-2ubuntu1.6_all.deb ... #6 76.96 Unpacking libx11-data (2:1.6.9-2ubuntu1.6) ... #6 77.08 Selecting previously unselected package libx11-6:amd64. #6 77.08 Preparing to unpack .../034-libx11-6_2%3a1.6.9-2ubuntu1.6_amd64.deb ... #6 77.09 Unpacking libx11-6:amd64 (2:1.6.9-2ubuntu1.6) ... #6 77.22 Selecting previously unselected package wget. #6 77.22 Preparing to unpack .../035-wget_1.20.3-1ubuntu2_amd64.deb ... #6 77.23 Unpacking wget (1.20.3-1ubuntu2) ... #6 77.33 Selecting previously unselected package binutils-common:amd64. #6 77.33 Preparing to unpack .../036-binutils-common_2.34-6ubuntu1.9_amd64.deb ... #6 77.34 Unpacking binutils-common:amd64 (2.34-6ubuntu1.9) ... #6 77.44 Selecting previously unselected package libbinutils:amd64. #6 77.45 Preparing to unpack .../037-libbinutils_2.34-6ubuntu1.9_amd64.deb ... #6 77.45 Unpacking libbinutils:amd64 (2.34-6ubuntu1.9) ... #6 77.59 Selecting previously unselected package libctf-nobfd0:amd64. #6 77.59 Preparing to unpack .../038-libctf-nobfd0_2.34-6ubuntu1.9_amd64.deb ... #6 77.60 Unpacking libctf-nobfd0:amd64 (2.34-6ubuntu1.9) ... #6 77.68 Selecting previously unselected package libctf0:amd64. #6 77.68 Preparing to unpack .../039-libctf0_2.34-6ubuntu1.9_amd64.deb ... #6 77.69 Unpacking libctf0:amd64 (2.34-6ubuntu1.9) ... #6 77.76 Selecting previously unselected package binutils-x86-64-linux-gnu. #6 77.76 Preparing to unpack .../040-binutils-x86-64-linux-gnu_2.34-6ubuntu1.9_amd64.deb ... #6 77.77 Unpacking binutils-x86-64-linux-gnu (2.34-6ubuntu1.9) ... #6 78.04 Selecting previously unselected package binutils. #6 78.05 Preparing to unpack .../041-binutils_2.34-6ubuntu1.9_amd64.deb ... #6 78.05 Unpacking binutils (2.34-6ubuntu1.9) ... #6 78.12 Selecting previously unselected package libc-dev-bin. #6 78.12 Preparing to unpack .../042-libc-dev-bin_2.31-0ubuntu9.15_amd64.deb ... #6 78.13 Unpacking libc-dev-bin (2.31-0ubuntu9.15) ... #6 78.20 Selecting previously unselected package linux-libc-dev:amd64. #6 78.21 Preparing to unpack .../043-linux-libc-dev_5.4.0-182.202_amd64.deb ... #6 78.21 Unpacking linux-libc-dev:amd64 (5.4.0-182.202) ... #6 78.41 Selecting previously unselected package libcrypt-dev:amd64. #6 78.42 Preparing to unpack .../044-libcrypt-dev_1%3a4.4.10-10ubuntu4_amd64.deb ... #6 78.42 Unpacking libcrypt-dev:amd64 (1:4.4.10-10ubuntu4) ... #6 78.50 Selecting previously unselected package libc6-dev:amd64. #6 78.51 Preparing to unpack .../045-libc6-dev_2.31-0ubuntu9.15_amd64.deb ... #6 78.51 Unpacking libc6-dev:amd64 (2.31-0ubuntu9.15) ... #6 78.88 Selecting previously unselected package gcc-9-base:amd64. #6 78.88 Preparing to unpack .../046-gcc-9-base_9.4.0-1ubuntu1~20.04.2_amd64.deb ... #6 78.89 Unpacking gcc-9-base:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 78.97 Selecting previously unselected package libisl22:amd64. #6 78.97 Preparing to unpack .../047-libisl22_0.22.1-1_amd64.deb ... #6 78.98 Unpacking libisl22:amd64 (0.22.1-1) ... #6 79.12 Selecting previously unselected package libmpfr6:amd64. #6 79.12 Preparing to unpack .../048-libmpfr6_4.0.2-1_amd64.deb ... #6 79.13 Unpacking libmpfr6:amd64 (4.0.2-1) ... #6 79.24 Selecting previously unselected package libmpc3:amd64. #6 79.24 Preparing to unpack .../049-libmpc3_1.1.0-1_amd64.deb ... #6 79.25 Unpacking libmpc3:amd64 (1.1.0-1) ... #6 79.32 Selecting previously unselected package cpp-9. #6 79.32 Preparing to unpack .../050-cpp-9_9.4.0-1ubuntu1~20.04.2_amd64.deb ... #6 79.33 Unpacking cpp-9 (9.4.0-1ubuntu1~20.04.2) ... #6 80.06 Selecting previously unselected package cpp. #6 80.06 Preparing to unpack .../051-cpp_4%3a9.3.0-1ubuntu2_amd64.deb ... #6 80.07 Unpacking cpp (4:9.3.0-1ubuntu2) ... #6 80.14 Selecting previously unselected package libcc1-0:amd64. #6 80.14 Preparing to unpack .../052-libcc1-0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.15 Unpacking libcc1-0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.23 Selecting previously unselected package libgomp1:amd64. #6 80.23 Preparing to unpack .../053-libgomp1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.24 Unpacking libgomp1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.34 Selecting previously unselected package libitm1:amd64. #6 80.34 Preparing to unpack .../054-libitm1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.35 Unpacking libitm1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.43 Selecting previously unselected package libatomic1:amd64. #6 80.43 Preparing to unpack .../055-libatomic1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.44 Unpacking libatomic1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 80.51 Selecting previously unselected package libasan5:amd64. #6 80.51 Preparing to unpack .../056-libasan5_9.4.0-1ubuntu1~20.04.2_amd64.deb ... #6 80.52 Unpacking libasan5:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 80.87 Selecting previously unselected package liblsan0:amd64. #6 80.88 Preparing to unpack .../057-liblsan0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 80.88 Unpacking liblsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.05 Selecting previously unselected package libtsan0:amd64. #6 81.05 Preparing to unpack .../058-libtsan0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 81.06 Unpacking libtsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.35 Selecting previously unselected package libubsan1:amd64. #6 81.35 Preparing to unpack .../059-libubsan1_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 81.36 Unpacking libubsan1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.52 Selecting previously unselected package libquadmath0:amd64. #6 81.52 Preparing to unpack .../060-libquadmath0_10.5.0-1ubuntu1~20.04_amd64.deb ... #6 81.53 Unpacking libquadmath0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 81.62 Selecting previously unselected package libgcc-9-dev:amd64. #6 81.62 Preparing to unpack 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Selecting previously unselected package cmake-data. #6 84.52 Preparing to unpack .../072-cmake-data_3.16.3-1ubuntu1.20.04.1_all.deb ... #6 84.54 Unpacking cmake-data (3.16.3-1ubuntu1.20.04.1) ... #6 84.99 Selecting previously unselected package libarchive13:amd64. #6 84.99 Preparing to unpack .../073-libarchive13_3.4.0-2ubuntu1.2_amd64.deb ... #6 85.00 Unpacking libarchive13:amd64 (3.4.0-2ubuntu1.2) ... #6 85.12 Selecting previously unselected package libbrotli1:amd64. #6 85.12 Preparing to unpack .../074-libbrotli1_1.0.7-6ubuntu0.1_amd64.deb ... #6 85.13 Unpacking libbrotli1:amd64 (1.0.7-6ubuntu0.1) ... #6 85.24 Selecting previously unselected package libroken18-heimdal:amd64. #6 85.25 Preparing to unpack .../075-libroken18-heimdal_7.7.0+dfsg-1ubuntu1.4_amd64.deb ... #6 85.25 Unpacking libroken18-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 85.33 Selecting previously unselected package libasn1-8-heimdal:amd64. #6 85.33 Preparing to unpack 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.../084-libsasl2-modules-db_2.1.27+dfsg-2ubuntu0.1_amd64.deb ... #6 86.17 Unpacking libsasl2-modules-db:amd64 (2.1.27+dfsg-2ubuntu0.1) ... #6 86.25 Selecting previously unselected package libsasl2-2:amd64. #6 86.25 Preparing to unpack .../085-libsasl2-2_2.1.27+dfsg-2ubuntu0.1_amd64.deb ... #6 86.26 Unpacking libsasl2-2:amd64 (2.1.27+dfsg-2ubuntu0.1) ... #6 86.33 Selecting previously unselected package libldap-common. #6 86.34 Preparing to unpack .../086-libldap-common_2.4.49+dfsg-2ubuntu1.10_all.deb ... #6 86.34 Unpacking libldap-common (2.4.49+dfsg-2ubuntu1.10) ... #6 86.42 Selecting previously unselected package libldap-2.4-2:amd64. #6 86.42 Preparing to unpack .../087-libldap-2.4-2_2.4.49+dfsg-2ubuntu1.10_amd64.deb ... #6 86.43 Unpacking libldap-2.4-2:amd64 (2.4.49+dfsg-2ubuntu1.10) ... #6 86.53 Selecting previously unselected package libnghttp2-14:amd64. #6 86.54 Preparing to unpack .../088-libnghttp2-14_1.40.0-1ubuntu0.3_amd64.deb ... #6 86.54 Unpacking libnghttp2-14:amd64 (1.40.0-1ubuntu0.3) ... #6 86.63 Selecting previously unselected package librtmp1:amd64. #6 86.64 Preparing to unpack .../089-librtmp1_2.4+20151223.gitfa8646d.1-2build1_amd64.deb ... #6 86.64 Unpacking librtmp1:amd64 (2.4+20151223.gitfa8646d.1-2build1) ... #6 86.73 Selecting previously unselected package libssh-4:amd64. #6 86.74 Preparing to unpack .../090-libssh-4_0.9.3-2ubuntu2.5_amd64.deb ... #6 86.74 Unpacking libssh-4:amd64 (0.9.3-2ubuntu2.5) ... #6 86.85 Selecting previously unselected package libcurl4:amd64. #6 86.85 Preparing to unpack .../091-libcurl4_7.68.0-1ubuntu2.22_amd64.deb ... #6 86.86 Unpacking libcurl4:amd64 (7.68.0-1ubuntu2.22) ... #6 86.96 Selecting previously unselected package libjsoncpp1:amd64. #6 86.97 Preparing to unpack .../092-libjsoncpp1_1.7.4-3.1ubuntu2_amd64.deb ... #6 86.97 Unpacking libjsoncpp1:amd64 (1.7.4-3.1ubuntu2) ... #6 87.07 Selecting previously unselected package librhash0:amd64. #6 87.07 Preparing to unpack .../093-librhash0_1.3.9-1_amd64.deb ... #6 87.08 Unpacking librhash0:amd64 (1.3.9-1) ... #6 87.16 Selecting previously unselected package cmake. #6 87.16 Preparing to unpack .../094-cmake_3.16.3-1ubuntu1.20.04.1_amd64.deb ... #6 87.17 Unpacking cmake (3.16.3-1ubuntu1.20.04.1) ... #6 87.60 Selecting previously unselected package curl. #6 87.60 Preparing to unpack .../095-curl_7.68.0-1ubuntu2.22_amd64.deb ... #6 87.61 Unpacking curl (7.68.0-1ubuntu2.22) ... #6 87.71 Selecting previously unselected package fonts-dejavu-core. #6 87.71 Preparing to unpack .../096-fonts-dejavu-core_2.37-1_all.deb ... #6 87.72 Unpacking fonts-dejavu-core (2.37-1) ... #6 87.90 Selecting previously unselected package fontconfig-config. #6 87.91 Preparing to unpack .../097-fontconfig-config_2.13.1-2ubuntu3_all.deb ... #6 87.92 Unpacking fontconfig-config (2.13.1-2ubuntu3) ... #6 88.00 Selecting previously unselected package libcurl3-gnutls:amd64. #6 88.00 Preparing to unpack .../098-libcurl3-gnutls_7.68.0-1ubuntu2.22_amd64.deb ... #6 88.01 Unpacking libcurl3-gnutls:amd64 (7.68.0-1ubuntu2.22) ... #6 88.11 Selecting previously unselected package liberror-perl. #6 88.12 Preparing to unpack .../099-liberror-perl_0.17029-1_all.deb ... #6 88.12 Unpacking liberror-perl (0.17029-1) ... #6 88.19 Selecting previously unselected package git-man. #6 88.19 Preparing to unpack .../100-git-man_1%3a2.25.1-1ubuntu3.11_all.deb ... #6 88.20 Unpacking git-man (1:2.25.1-1ubuntu3.11) ... #6 88.37 Selecting previously unselected package git. #6 88.38 Preparing to unpack .../101-git_1%3a2.25.1-1ubuntu3.11_amd64.deb ... #6 88.43 Unpacking git (1:2.25.1-1ubuntu3.11) ... #6 88.98 Selecting previously unselected package libfreetype6:amd64. #6 88.99 Preparing to unpack .../102-libfreetype6_2.10.1-2ubuntu0.3_amd64.deb ... #6 89.00 Unpacking libfreetype6:amd64 (2.10.1-2ubuntu0.3) ... #6 89.12 Selecting previously unselected package libfontconfig1:amd64. #6 89.12 Preparing to unpack .../103-libfontconfig1_2.13.1-2ubuntu3_amd64.deb ... #6 89.13 Unpacking libfontconfig1:amd64 (2.13.1-2ubuntu3) ... #6 89.23 Selecting previously unselected package libjpeg-turbo8:amd64. #6 89.23 Preparing to unpack .../104-libjpeg-turbo8_2.0.3-0ubuntu1.20.04.3_amd64.deb ... #6 89.24 Unpacking libjpeg-turbo8:amd64 (2.0.3-0ubuntu1.20.04.3) ... #6 89.32 Selecting previously unselected package libjpeg8:amd64. #6 89.33 Preparing to unpack .../105-libjpeg8_8c-2ubuntu8_amd64.deb ... #6 89.33 Unpacking libjpeg8:amd64 (8c-2ubuntu8) ... #6 89.40 Selecting previously unselected package libjbig0:amd64. #6 89.40 Preparing to unpack .../106-libjbig0_2.1-3.1ubuntu0.20.04.1_amd64.deb ... #6 89.41 Unpacking libjbig0:amd64 (2.1-3.1ubuntu0.20.04.1) ... #6 89.49 Selecting previously unselected package libwebp6:amd64. #6 89.49 Preparing to unpack .../107-libwebp6_0.6.1-2ubuntu0.20.04.3_amd64.deb ... #6 89.50 Unpacking libwebp6:amd64 (0.6.1-2ubuntu0.20.04.3) ... #6 89.60 Selecting previously unselected package libtiff5:amd64. #6 89.60 Preparing to unpack .../108-libtiff5_4.1.0+git191117-2ubuntu0.20.04.12_amd64.deb ... #6 89.61 Unpacking libtiff5:amd64 (4.1.0+git191117-2ubuntu0.20.04.12) ... #6 89.71 Selecting previously unselected package libxpm4:amd64. #6 89.71 Preparing to unpack .../109-libxpm4_1%3a3.5.12-1ubuntu0.20.04.2_amd64.deb ... #6 89.72 Unpacking libxpm4:amd64 (1:3.5.12-1ubuntu0.20.04.2) ... #6 89.80 Selecting previously unselected package libgd3:amd64. #6 89.80 Preparing to unpack .../110-libgd3_2.2.5-5.2ubuntu2.1_amd64.deb ... #6 89.81 Unpacking libgd3:amd64 (2.2.5-5.2ubuntu2.1) ... #6 89.92 Selecting previously unselected package nginx-common. #6 89.92 Preparing to unpack .../111-nginx-common_1.18.0-0ubuntu1.4_all.deb ... #6 89.99 Unpacking nginx-common (1.18.0-0ubuntu1.4) ... #6 90.08 Selecting previously unselected package libnginx-mod-http-image-filter. #6 90.08 Preparing to unpack .../112-libnginx-mod-http-image-filter_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.09 Unpacking libnginx-mod-http-image-filter (1.18.0-0ubuntu1.4) ... #6 90.17 Selecting previously unselected package libxslt1.1:amd64. #6 90.17 Preparing to unpack .../113-libxslt1.1_1.1.34-4ubuntu0.20.04.1_amd64.deb ... #6 90.18 Unpacking libxslt1.1:amd64 (1.1.34-4ubuntu0.20.04.1) ... #6 90.28 Selecting previously unselected package libnginx-mod-http-xslt-filter. #6 90.28 Preparing to unpack .../114-libnginx-mod-http-xslt-filter_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.29 Unpacking libnginx-mod-http-xslt-filter (1.18.0-0ubuntu1.4) ... #6 90.37 Selecting previously unselected package libnginx-mod-mail. #6 90.37 Preparing to unpack .../115-libnginx-mod-mail_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.38 Unpacking libnginx-mod-mail (1.18.0-0ubuntu1.4) ... #6 90.46 Selecting previously unselected package libnginx-mod-stream. #6 90.46 Preparing to unpack .../116-libnginx-mod-stream_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.47 Unpacking libnginx-mod-stream (1.18.0-0ubuntu1.4) ... #6 90.56 Selecting previously unselected package nginx-core. #6 90.56 Preparing to unpack .../117-nginx-core_1.18.0-0ubuntu1.4_amd64.deb ... #6 90.57 Unpacking nginx-core (1.18.0-0ubuntu1.4) ... #6 90.69 Selecting previously unselected package nginx. #6 90.70 Preparing to unpack .../118-nginx_1.18.0-0ubuntu1.4_all.deb ... #6 90.71 Unpacking nginx (1.18.0-0ubuntu1.4) ... #6 90.79 Setting up libexpat1:amd64 (2.2.9-1ubuntu0.6) ... #6 90.81 Setting up libxau6:amd64 (1:1.0.9-0ubuntu1) ... #6 90.84 Setting up libkeyutils1:amd64 (1.6-6ubuntu1.1) ... #6 90.86 Setting up libpsl5:amd64 (0.21.0-1ubuntu1) ... #6 90.88 Setting up perl-modules-5.30 (5.30.0-9ubuntu0.5) ... #6 90.90 Setting up libssl1.1:amd64 (1.1.1f-1ubuntu2.22) ... #6 91.02 Setting up libbrotli1:amd64 (1.0.7-6ubuntu0.1) ... #6 91.04 Setting up libsqlite3-0:amd64 (3.31.1-4ubuntu0.6) ... #6 91.06 Setting up binutils-common:amd64 (2.34-6ubuntu1.9) ... #6 91.08 Setting up libnghttp2-14:amd64 (1.40.0-1ubuntu0.3) ... #6 91.11 Setting up linux-libc-dev:amd64 (5.4.0-182.202) ... #6 91.13 Setting up libctf-nobfd0:amd64 (2.34-6ubuntu1.9) ... #6 91.15 Setting up nginx-common (1.18.0-0ubuntu1.4) ... #6 91.69 Setting up libgomp1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 91.71 Setting up libldap-common (2.4.49+dfsg-2ubuntu1.10) ... #6 91.74 Setting up libjbig0:amd64 (2.1-3.1ubuntu0.20.04.1) ... #6 91.76 Setting up libcap2:amd64 (1:2.32-1ubuntu0.1) ... #6 91.79 Setting up libkrb5support0:amd64 (1.17-6ubuntu4.4) ... #6 91.81 Setting up libsasl2-modules-db:amd64 (2.1.27+dfsg-2ubuntu0.1) ... #6 91.83 Setting up tzdata (2024a-0ubuntu0.20.04) ... #6 91.96 #6 91.96 Current default time zone: 'Etc/UTC' #6 91.96 Local time is now: Thu May 16 20:22:03 UTC 2024. #6 91.96 Universal Time is now: Thu May 16 20:22:03 UTC 2024. #6 91.96 Run 'dpkg-reconfigure tzdata' if you wish to change it. #6 91.96 #6 91.99 Setting up libcap2-bin (1:2.32-1ubuntu0.1) ... #6 92.01 Setting up libuv1:amd64 (1.34.2-1ubuntu1.5) ... #6 92.03 Setting up libx11-data (2:1.6.9-2ubuntu1.6) ... #6 92.05 Setting up make (4.2.1-1.2) ... #6 92.08 Setting up libmpfr6:amd64 (4.0.2-1) ... #6 92.10 Setting up librtmp1:amd64 (2.4+20151223.gitfa8646d.1-2build1) ... #6 92.12 Setting up xz-utils (5.2.4-1ubuntu1.1) ... #6 92.14 update-alternatives: using /usr/bin/xz to provide /usr/bin/lzma (lzma) in auto mode #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzma.1.gz because associated file /usr/share/man/man1/xz.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/unlzma.1.gz because associated file /usr/share/man/man1/unxz.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzcat.1.gz because associated file /usr/share/man/man1/xzcat.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzmore.1.gz because associated file /usr/share/man/man1/xzmore.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzless.1.gz because associated file /usr/share/man/man1/xzless.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzdiff.1.gz because associated file /usr/share/man/man1/xzdiff.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzcmp.1.gz because associated file /usr/share/man/man1/xzcmp.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzgrep.1.gz because associated file /usr/share/man/man1/xzgrep.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzegrep.1.gz because associated file /usr/share/man/man1/xzegrep.1.gz (of link group lzma) doesn't exist #6 92.14 update-alternatives: warning: skip creation of /usr/share/man/man1/lzfgrep.1.gz because associated file /usr/share/man/man1/xzfgrep.1.gz (of link group lzma) doesn't exist #6 92.16 Setting up libquadmath0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.18 Setting up libpng16-16:amd64 (1.6.37-2) ... #6 92.20 Setting up libmpc3:amd64 (1.1.0-1) ... #6 92.22 Setting up libmnl0:amd64 (1.0.4-2) ... #6 92.24 Setting up libatomic1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.26 Setting up patch (2.7.6-6) ... #6 92.28 Setting up libwebp6:amd64 (0.6.1-2ubuntu0.20.04.3) ... #6 92.30 Setting up fonts-dejavu-core (2.37-1) ... #6 92.40 Setting up ucf (3.0038+nmu1) ... #6 92.53 Setting up libk5crypto3:amd64 (1.17-6ubuntu4.4) ... #6 92.55 Setting up libjpeg-turbo8:amd64 (2.0.3-0ubuntu1.20.04.3) ... #6 92.57 Setting up libxtables12:amd64 (1.8.4-3ubuntu2.1) ... #6 92.59 Setting up libsasl2-2:amd64 (2.1.27+dfsg-2ubuntu0.1) ... #6 92.61 Setting up libroken18-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 92.64 Setting up libubsan1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.66 Setting up librhash0:amd64 (1.3.9-1) ... #6 92.68 Setting up libcrypt-dev:amd64 (1:4.4.10-10ubuntu4) ... #6 92.70 Setting up git-man (1:2.25.1-1ubuntu3.11) ... #6 92.72 Setting up libisl22:amd64 (0.22.1-1) ... #6 92.74 Setting up cmake-data (3.16.3-1ubuntu1.20.04.1) ... #6 92.77 Setting up libkrb5-3:amd64 (1.17-6ubuntu4.4) ... #6 92.79 Setting up libbinutils:amd64 (2.34-6ubuntu1.9) ... #6 92.81 Setting up libc-dev-bin (2.31-0ubuntu9.15) ... #6 92.83 Setting up openssl (1.1.1f-1ubuntu2.22) ... #6 92.86 Setting up libbsd0:amd64 (0.10.0-1) ... #6 92.88 Setting up libelf1:amd64 (0.176-1.1ubuntu0.1) ... #6 92.91 Setting up libcc1-0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.93 Setting up liblsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.95 Setting up libitm1:amd64 (10.5.0-1ubuntu1~20.04) ... #6 92.97 Setting up libjsoncpp1:amd64 (1.7.4-3.1ubuntu2) ... #6 92.99 Setting up libheimbase1-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 93.01 Setting up libgdbm6:amd64 (1.18.1-5) ... #6 93.03 Setting up gcc-9-base:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 93.05 Setting up libtsan0:amd64 (10.5.0-1ubuntu1~20.04) ... #6 93.07 Setting up libctf0:amd64 (2.34-6ubuntu1.9) ... #6 93.09 Setting up libjpeg8:amd64 (8c-2ubuntu8) ... #6 93.11 Setting up libnginx-mod-mail (1.18.0-0ubuntu1.4) ... #6 93.16 Setting up libxdmcp6:amd64 (1:1.1.3-0ubuntu1) ... #6 93.18 Setting up libxcb1:amd64 (1.14-2) ... #6 93.20 Setting up wget (1.20.3-1ubuntu2) ... #6 93.22 Setting up fontconfig-config (2.13.1-2ubuntu3) ... #6 93.55 Setting up iproute2 (5.5.0-1ubuntu1) ... #6 93.75 Setting up libicu66:amd64 (66.1-2ubuntu2.1) ... #6 93.77 Setting up libasn1-8-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 93.79 Setting up libnginx-mod-stream (1.18.0-0ubuntu1.4) ... #6 93.84 Setting up libhcrypto4-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 93.86 Setting up ca-certificates (20230311ubuntu0.20.04.1) ... #6 95.13 Updating certificates in /etc/ssl/certs... #6 96.88 137 added, 0 removed; done. #6 96.95 Setting up libasan5:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 96.98 Setting up libwind0-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.00 Setting up libfreetype6:amd64 (2.10.1-2ubuntu0.3) ... #6 97.02 Setting up libgssapi-krb5-2:amd64 (1.17-6ubuntu4.4) ... #6 97.04 Setting up libgdbm-compat4:amd64 (1.18.1-5) ... #6 97.06 Setting up libssh-4:amd64 (0.9.3-2ubuntu2.5) ... #6 97.08 Setting up cpp-9 (9.4.0-1ubuntu1~20.04.2) ... #6 97.10 Setting up libperl5.30:amd64 (5.30.0-9ubuntu0.5) ... #6 97.12 Setting up libc6-dev:amd64 (2.31-0ubuntu9.15) ... #6 97.14 Setting up libx11-6:amd64 (2:1.6.9-2ubuntu1.6) ... #6 97.16 Setting up libtiff5:amd64 (4.1.0+git191117-2ubuntu0.20.04.12) ... #6 97.18 Setting up libfontconfig1:amd64 (2.13.1-2ubuntu3) ... #6 97.20 Setting up libxml2:amd64 (2.9.10+dfsg-5ubuntu0.20.04.7) ... #6 97.22 Setting up binutils-x86-64-linux-gnu (2.34-6ubuntu1.9) ... #6 97.24 Setting up libxpm4:amd64 (1:3.5.12-1ubuntu0.20.04.2) ... #6 97.26 Setting up libarchive13:amd64 (3.4.0-2ubuntu1.2) ... #6 97.28 Setting up libhx509-5-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.30 Setting up binutils (2.34-6ubuntu1.9) ... #6 97.32 Setting up libgcc-9-dev:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 97.34 Setting up perl (5.30.0-9ubuntu0.5) ... #6 97.39 Setting up libgd3:amd64 (2.2.5-5.2ubuntu2.1) ... #6 97.41 Setting up libdpkg-perl (1.19.7ubuntu3.2) ... #6 97.43 Setting up cpp (4:9.3.0-1ubuntu2) ... #6 97.46 Setting up libxslt1.1:amd64 (1.1.34-4ubuntu0.20.04.1) ... #6 97.48 Setting up libkrb5-26-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.50 Setting up gcc-9 (9.4.0-1ubuntu1~20.04.2) ... #6 97.52 Setting up libstdc++-9-dev:amd64 (9.4.0-1ubuntu1~20.04.2) ... #6 97.54 Setting up libnginx-mod-http-image-filter (1.18.0-0ubuntu1.4) ... #6 97.59 Setting up libheimntlm0-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.61 Setting up gcc (4:9.3.0-1ubuntu2) ... #6 97.65 Setting up dpkg-dev (1.19.7ubuntu3.2) ... #6 97.69 Setting up liberror-perl (0.17029-1) ... #6 97.71 Setting up libnginx-mod-http-xslt-filter (1.18.0-0ubuntu1.4) ... #6 97.75 Setting up libgssapi3-heimdal:amd64 (7.7.0+dfsg-1ubuntu1.4) ... #6 97.77 Setting up g++-9 (9.4.0-1ubuntu1~20.04.2) ... #6 97.79 Setting up g++ (4:9.3.0-1ubuntu2) ... #6 97.85 update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode #6 97.85 update-alternatives: warning: skip creation of /usr/share/man/man1/c++.1.gz because associated file /usr/share/man/man1/g++.1.gz (of link group c++) doesn't exist #6 97.87 Setting up build-essential (12.8ubuntu1.1) ... #6 97.89 Setting up nginx-core (1.18.0-0ubuntu1.4) ... #6 97.98 invoke-rc.d: could not determine current runlevel #6 97.99 invoke-rc.d: policy-rc.d denied execution of start. #6 98.00 Setting up nginx (1.18.0-0ubuntu1.4) ... #6 98.02 Setting up libldap-2.4-2:amd64 (2.4.49+dfsg-2ubuntu1.10) ... #6 98.04 Setting up libcurl3-gnutls:amd64 (7.68.0-1ubuntu2.22) ... #6 98.06 Setting up git (1:2.25.1-1ubuntu3.11) ... #6 98.14 Setting up libcurl4:amd64 (7.68.0-1ubuntu2.22) ... #6 98.16 Setting up curl (7.68.0-1ubuntu2.22) ... #6 98.18 Setting up cmake (3.16.3-1ubuntu1.20.04.1) ... #6 98.20 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #6 98.25 Processing triggers for ca-certificates (20230311ubuntu0.20.04.1) ... #6 98.26 Updating certificates in /etc/ssl/certs... #6 99.63 0 added, 0 removed; done. #6 99.63 Running hooks in /etc/ca-certificates/update.d... #6 99.63 done. #6 DONE 100.6s #7 [ 3/17] RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev #7 0.464 Reading package lists... #7 1.538 Building dependency tree... #7 1.711 Reading state information... #7 1.915 curl is already the newest version (7.68.0-1ubuntu2.22). #7 1.915 tzdata is already the newest version (2024a-0ubuntu0.20.04). #7 1.915 tzdata set to manually installed. #7 1.915 wget is already the newest version (1.20.3-1ubuntu2). #7 1.915 xz-utils is already the newest version (5.2.4-1ubuntu1.1). #7 1.915 xz-utils set to manually installed. #7 1.915 The following additional packages will be installed: #7 1.915 binfmt-support bzip2-doc file icu-devtools libclang-cpp10 libdrm-amdgpu1 #7 1.915 libdrm-common libdrm-intel1 libdrm-nouveau2 libdrm-radeon1 libdrm2 libedit2 #7 1.915 libevent-2.1-7 libexpat1-dev libfontconfig1-dev libfontenc1 libfreetype-dev #7 1.915 libfreetype6-dev libgcrypt20-dev libgl1 libgl1-mesa-dri libglapi-mesa #7 1.915 libglib2.0-0 libglib2.0-data libglvnd0 libglx-mesa0 libglx0 libgmp-dev #7 1.915 libgmpxx4ldbl libgnutls-dane0 libgnutls-openssl27 libgnutls28-dev #7 1.915 libgnutlsxx28 libgpg-error-dev libice-dev libice6 libicu-dev libidn2-dev #7 1.915 libllvm10 libllvm12 libmagic-mgc libmagic1 libmpdec2 libncurses-dev libnspr4 #7 1.915 libnspr4-dev libnss3 libnss3-dev libp11-kit-dev libpciaccess0 libpfm4 #7 1.915 libpipeline1 libpng-dev libpng-tools libpthread-stubs0-dev libpython3-stdlib #7 1.915 libpython3.8-minimal libpython3.8-stdlib libreadline8 libsensors-config #7 1.915 libsensors5 libsm-dev libsm6 libtasn1-6-dev libtasn1-doc libtcl8.6 libtk8.6 #7 1.915 libunbound8 libutempter0 libvulkan1 libwayland-client0 libx11-dev #7 1.915 libx11-xcb1 libxau-dev libxaw7 libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 #7 1.915 libxcb-present0 libxcb-randr0 libxcb-shape0 libxcb-shm0 libxcb-sync1 #7 1.915 libxcb-xfixes0 libxcb1-dev libxcomposite1 libxdmcp-dev libxext-dev libxext6 #7 1.915 libxfixes3 libxft-dev libxft2 libxi6 libxinerama1 libxkbfile1 libxmlsec1 #7 1.915 libxmlsec1-gcrypt libxmlsec1-gnutls libxmlsec1-nss libxmlsec1-openssl #7 1.915 libxmu6 libxmuu1 libxrandr2 libxrender-dev libxrender1 libxshmfence1 #7 1.915 libxslt1-dev libxss-dev libxss1 libxt-dev libxt6 libxtst6 libxv1 #7 1.915 libxxf86dga1 libxxf86vm1 libyaml-0-2 libz3-4 libz3-dev llvm-10 llvm-10-dev #7 1.915 llvm-10-runtime llvm-10-tools llvm-runtime mesa-vulkan-drivers mime-support #7 1.915 nettle-dev pkg-config python3 python3-minimal python3-pkg-resources #7 1.915 python3-pygments python3-yaml python3.8 python3.8-minimal readline-common #7 1.915 shared-mime-info tcl tcl-dev tcl8.6 tcl8.6-dev tk tk8.6 tk8.6-dev uuid-dev #7 1.915 x11-common x11-utils x11proto-core-dev x11proto-dev x11proto-scrnsaver-dev #7 1.915 x11proto-xext-dev xbitmaps xdg-user-dirs xorg-sgml-doctools xterm xtrans-dev #7 1.917 Suggested packages: #7 1.917 freetype2-doc libgcrypt20-doc gmp-doc libgmp10-doc libmpfr-dev dns-root-data #7 1.917 gnutls-bin gnutls-doc libice-doc icu-doc liblzma-doc ncurses-doc pciutils #7 1.917 readline-doc lm-sensors libsm-doc sqlite3-doc libssl-doc libx11-doc #7 1.917 libxcb-doc libxext-doc libxt-doc llvm-10-doc python3-doc python3-tk #7 1.917 python3-venv python3-setuptools python-pygments-doc ttf-bitstream-vera #7 1.917 python3.8-venv python3.8-doc tcl-doc tcl-tclreadline tcl8.6-doc tk-doc #7 1.917 tk8.6-doc mesa-utils xfonts-cyrillic #7 2.228 The following NEW packages will be installed: #7 2.228 binfmt-support bzip2-doc file icu-devtools libbz2-dev libclang-cpp10 #7 2.228 libdrm-amdgpu1 libdrm-common libdrm-intel1 libdrm-nouveau2 libdrm-radeon1 #7 2.228 libdrm2 libedit2 libevent-2.1-7 libexpat1-dev libffi-dev libfontconfig1-dev #7 2.228 libfontenc1 libfreetype-dev libfreetype6-dev libgcrypt20-dev libgl1 #7 2.228 libgl1-mesa-dri libglapi-mesa libglib2.0-0 libglib2.0-data libglvnd0 #7 2.228 libglx-mesa0 libglx0 libgmp-dev libgmpxx4ldbl libgnutls-dane0 #7 2.228 libgnutls-openssl27 libgnutls28-dev libgnutlsxx28 libgpg-error-dev #7 2.228 libice-dev libice6 libicu-dev libidn2-dev libllvm10 libllvm12 liblzma-dev #7 2.228 libmagic-mgc libmagic1 libmpdec2 libncurses-dev libncursesw5-dev libnspr4 #7 2.228 libnspr4-dev libnss3 libnss3-dev libp11-kit-dev libpciaccess0 libpfm4 #7 2.228 libpipeline1 libpng-dev libpng-tools libpthread-stubs0-dev libpython3-stdlib #7 2.229 libpython3.8-minimal libpython3.8-stdlib libreadline-dev libreadline8 #7 2.229 libsensors-config libsensors5 libsm-dev libsm6 libsqlite3-dev libssl-dev #7 2.229 libtasn1-6-dev libtasn1-doc libtcl8.6 libtk8.6 libunbound8 libutempter0 #7 2.229 libvulkan1 libwayland-client0 libx11-dev libx11-xcb1 libxau-dev libxaw7 #7 2.229 libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 libxcb-present0 libxcb-randr0 #7 2.229 libxcb-shape0 libxcb-shm0 libxcb-sync1 libxcb-xfixes0 libxcb1-dev #7 2.229 libxcomposite1 libxdmcp-dev libxext-dev libxext6 libxfixes3 libxft-dev #7 2.229 libxft2 libxi6 libxinerama1 libxkbfile1 libxml2-dev libxmlsec1 #7 2.229 libxmlsec1-dev libxmlsec1-gcrypt libxmlsec1-gnutls libxmlsec1-nss #7 2.229 libxmlsec1-openssl libxmu6 libxmuu1 libxrandr2 libxrender-dev libxrender1 #7 2.229 libxshmfence1 libxslt1-dev libxss-dev libxss1 libxt-dev libxt6 libxtst6 #7 2.229 libxv1 libxxf86dga1 libxxf86vm1 libyaml-0-2 libz3-4 libz3-dev llvm llvm-10 #7 2.229 llvm-10-dev llvm-10-runtime llvm-10-tools llvm-runtime mesa-vulkan-drivers #7 2.229 mime-support nettle-dev pkg-config python3 python3-minimal #7 2.229 python3-pkg-resources python3-pygments python3-yaml python3.8 #7 2.229 python3.8-minimal readline-common shared-mime-info tcl tcl-dev tcl8.6 #7 2.229 tcl8.6-dev tk tk-dev tk8.6 tk8.6-dev uuid-dev x11-common x11-utils #7 2.229 x11proto-core-dev x11proto-dev x11proto-scrnsaver-dev x11proto-xext-dev #7 2.229 xbitmaps xdg-user-dirs xorg-sgml-doctools xterm xtrans-dev zlib1g-dev #7 2.493 0 upgraded, 167 newly installed, 0 to remove and 0 not upgraded. #7 2.493 Need to get 138 MB of archives. #7 2.493 After this operation, 1062 MB of additional disk space will be used. #7 2.493 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libpython3.8-minimal amd64 3.8.10-0ubuntu1~20.04.9 [718 kB] #7 3.260 Get:2 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3.8-minimal amd64 3.8.10-0ubuntu1~20.04.9 [1890 kB] #7 4.013 Get:3 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-minimal amd64 3.8.2-0ubuntu2 [23.6 kB] #7 4.022 Get:4 http://archive.ubuntu.com/ubuntu focal/main amd64 mime-support all 3.64ubuntu1 [30.6 kB] #7 4.038 Get:5 http://archive.ubuntu.com/ubuntu focal/main amd64 libmpdec2 amd64 2.4.2-3 [81.1 kB] #7 4.067 Get:6 http://archive.ubuntu.com/ubuntu focal/main amd64 readline-common all 8.0-4 [53.5 kB] #7 4.089 Get:7 http://archive.ubuntu.com/ubuntu focal/main amd64 libreadline8 amd64 8.0-4 [131 kB] #7 4.143 Get:8 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libpython3.8-stdlib amd64 3.8.10-0ubuntu1~20.04.9 [1674 kB] #7 4.808 Get:9 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3.8 amd64 3.8.10-0ubuntu1~20.04.9 [387 kB] #7 4.961 Get:10 http://archive.ubuntu.com/ubuntu focal/main amd64 libpython3-stdlib amd64 3.8.2-0ubuntu2 [7068 B] #7 4.963 Get:11 http://archive.ubuntu.com/ubuntu focal/main amd64 python3 amd64 3.8.2-0ubuntu2 [47.6 kB] #7 4.983 Get:12 http://archive.ubuntu.com/ubuntu focal/main amd64 libmagic-mgc amd64 1:5.38-4 [218 kB] #7 5.068 Get:13 http://archive.ubuntu.com/ubuntu focal/main amd64 libmagic1 amd64 1:5.38-4 [75.9 kB] #7 5.100 Get:14 http://archive.ubuntu.com/ubuntu focal/main amd64 file amd64 1:5.38-4 [23.3 kB] #7 5.108 Get:15 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglib2.0-0 amd64 2.64.6-1~ubuntu20.04.7 [1289 kB] #7 5.621 Get:16 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglib2.0-data all 2.64.6-1~ubuntu20.04.7 [5944 B] #7 5.623 Get:17 http://archive.ubuntu.com/ubuntu focal/main amd64 libyaml-0-2 amd64 0.2.2-1 [48.9 kB] #7 5.640 Get:18 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-pkg-resources all 45.2.0-1ubuntu0.1 [130 kB] #7 5.693 Get:19 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-yaml amd64 5.3.1-1ubuntu0.1 [136 kB] #7 5.752 Get:20 http://archive.ubuntu.com/ubuntu focal/main amd64 shared-mime-info amd64 1.15-1 [430 kB] #7 5.921 Get:21 http://archive.ubuntu.com/ubuntu focal/main amd64 xdg-user-dirs amd64 0.17-2ubuntu1 [48.3 kB] #7 5.940 Get:22 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-common all 2.4.107-8ubuntu1~20.04.2 [5396 B] #7 5.941 Get:23 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm2 amd64 2.4.107-8ubuntu1~20.04.2 [34.1 kB] #7 5.952 Get:24 http://archive.ubuntu.com/ubuntu focal/main amd64 libedit2 amd64 3.1-20191231-1 [87.0 kB] #7 5.991 Get:25 http://archive.ubuntu.com/ubuntu focal/main amd64 libpipeline1 amd64 1.5.2-2build1 [27.7 kB] #7 6.000 Get:26 http://archive.ubuntu.com/ubuntu focal/main amd64 libxext6 amd64 2:1.3.4-0ubuntu1 [29.1 kB] #7 6.016 Get:27 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmuu1 amd64 2:1.1.3-0ubuntu1 [9728 B] #7 6.019 Get:28 http://archive.ubuntu.com/ubuntu focal/universe amd64 binfmt-support amd64 2.2.0-2 [58.2 kB] #7 6.041 Get:29 http://archive.ubuntu.com/ubuntu focal/main amd64 bzip2-doc all 1.0.8-2 [501 kB] #7 6.241 Get:30 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 icu-devtools amd64 66.1-2ubuntu2.1 [189 kB] #7 6.320 Get:31 http://archive.ubuntu.com/ubuntu focal/main amd64 libbz2-dev amd64 1.0.8-2 [30.2 kB] #7 6.329 Get:32 http://archive.ubuntu.com/ubuntu focal/main amd64 libllvm10 amd64 1:10.0.0-4ubuntu1 [15.3 MB] #7 12.57 Get:33 http://archive.ubuntu.com/ubuntu focal/universe amd64 libclang-cpp10 amd64 1:10.0.0-4ubuntu1 [9944 kB] #7 16.83 Get:34 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-amdgpu1 amd64 2.4.107-8ubuntu1~20.04.2 [18.6 kB] #7 16.83 Get:35 http://archive.ubuntu.com/ubuntu focal/main amd64 libpciaccess0 amd64 0.16-0ubuntu1 [17.9 kB] #7 16.84 Get:36 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-intel1 amd64 2.4.107-8ubuntu1~20.04.2 [60.3 kB] #7 16.86 Get:37 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-nouveau2 amd64 2.4.107-8ubuntu1~20.04.2 [16.6 kB] #7 16.87 Get:38 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-radeon1 amd64 2.4.107-8ubuntu1~20.04.2 [19.7 kB] #7 16.87 Get:39 http://archive.ubuntu.com/ubuntu focal/main amd64 libevent-2.1-7 amd64 2.1.11-stable-1 [138 kB] #7 16.93 Get:40 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libexpat1-dev amd64 2.2.9-1ubuntu0.6 [116 kB] #7 16.98 Get:41 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 zlib1g-dev amd64 1:1.2.11.dfsg-2ubuntu1.5 [155 kB] #7 17.04 Get:42 http://archive.ubuntu.com/ubuntu focal/main amd64 libpng-dev amd64 1.6.37-2 [175 kB] #7 17.11 Get:43 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libfreetype-dev amd64 2.10.1-2ubuntu0.3 [493 kB] #7 17.30 Get:44 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libfreetype6-dev amd64 2.10.1-2ubuntu0.3 [9808 B] #7 17.31 Get:45 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 uuid-dev amd64 2.34-0.1ubuntu9.6 [33.6 kB] #7 17.32 Get:46 http://archive.ubuntu.com/ubuntu focal/main amd64 pkg-config amd64 0.29.1-0ubuntu4 [45.5 kB] #7 17.34 Get:47 http://archive.ubuntu.com/ubuntu focal/main amd64 libfontconfig1-dev amd64 2.13.1-2ubuntu3 [731 kB] #7 17.63 Get:48 http://archive.ubuntu.com/ubuntu focal/main amd64 libfontenc1 amd64 1:1.1.4-0ubuntu1 [14.0 kB] #7 17.64 Get:49 http://archive.ubuntu.com/ubuntu focal/main amd64 libgpg-error-dev amd64 1.37-1 [109 kB] #7 17.68 Get:50 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgcrypt20-dev amd64 1.8.5-5ubuntu1.1 [471 kB] #7 17.87 Get:51 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglapi-mesa amd64 21.2.6-0ubuntu0.1~20.04.2 [27.4 kB] #7 17.88 Get:52 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libllvm12 amd64 1:12.0.0-3ubuntu1~20.04.5 [18.8 MB] #7 25.65 Get:53 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsensors-config all 1:3.6.0-2ubuntu1.1 [6052 B] #7 25.67 Get:54 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsensors5 amd64 1:3.6.0-2ubuntu1.1 [27.2 kB] #7 25.67 Get:55 http://archive.ubuntu.com/ubuntu focal/main amd64 libvulkan1 amd64 1.2.131.2-1 [93.3 kB] #7 25.68 Get:56 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl1-mesa-dri amd64 21.2.6-0ubuntu0.1~20.04.2 [11.0 MB] #7 30.46 Get:57 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-xcb1 amd64 2:1.6.9-2ubuntu1.6 [9448 B] #7 30.46 Get:58 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-dri2-0 amd64 1.14-2 [6920 B] #7 30.47 Get:59 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-dri3-0 amd64 1.14-2 [6552 B] #7 30.47 Get:60 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-glx0 amd64 1.14-2 [22.1 kB] #7 30.57 Get:61 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-present0 amd64 1.14-2 [5560 B] #7 30.62 Get:62 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-shm0 amd64 1.14-2 [5584 B] #7 30.67 Get:63 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-sync1 amd64 1.14-2 [8884 B] #7 30.73 Get:64 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-xfixes0 amd64 1.14-2 [9296 B] #7 30.79 Get:65 http://archive.ubuntu.com/ubuntu focal/main amd64 libxfixes3 amd64 1:5.0.3-2 [10.9 kB] #7 30.86 Get:66 http://archive.ubuntu.com/ubuntu focal/main amd64 libxshmfence1 amd64 1.3-1 [5028 B] #7 30.90 Get:67 http://archive.ubuntu.com/ubuntu focal/main amd64 libxxf86vm1 amd64 1:1.1.4-1build1 [10.2 kB] #7 30.96 Get:68 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglx-mesa0 amd64 21.2.6-0ubuntu0.1~20.04.2 [137 kB] #7 31.14 Get:69 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgmpxx4ldbl amd64 2:6.2.0+dfsg-4ubuntu0.1 [9144 B] #7 31.19 Get:70 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgmp-dev amd64 2:6.2.0+dfsg-4ubuntu0.1 [320 kB] #7 31.31 Get:71 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutls-openssl27 amd64 3.6.13-2ubuntu1.11 [29.8 kB] #7 31.40 Get:72 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libunbound8 amd64 1.9.4-2ubuntu1.5 [353 kB] #7 31.66 Get:73 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutls-dane0 amd64 3.6.13-2ubuntu1.11 [29.1 kB] #7 31.71 Get:74 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutlsxx28 amd64 3.6.13-2ubuntu1.11 [14.7 kB] #7 31.76 Get:75 http://archive.ubuntu.com/ubuntu focal/main amd64 libidn2-dev amd64 2.2.0-2 [64.6 kB] #7 31.81 Get:76 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libp11-kit-dev amd64 0.23.20-1ubuntu0.1 [65.2 kB] #7 31.86 Get:77 http://archive.ubuntu.com/ubuntu focal/main amd64 libtasn1-6-dev amd64 4.16.0-2 [85.8 kB] #7 31.92 Get:78 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 nettle-dev amd64 3.5.1+really3.5.1-2ubuntu0.2 [987 kB] #7 32.16 Get:79 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgnutls28-dev amd64 3.6.13-2ubuntu1.11 [873 kB] #7 32.38 Get:80 http://archive.ubuntu.com/ubuntu focal/main amd64 x11-common all 1:7.7+19ubuntu14 [22.3 kB] #7 32.43 Get:81 http://archive.ubuntu.com/ubuntu focal/main amd64 libice6 amd64 2:1.0.10-0ubuntu1 [41.0 kB] #7 32.48 Get:82 http://archive.ubuntu.com/ubuntu focal/main amd64 xorg-sgml-doctools all 1:1.11-1 [12.9 kB] #7 32.57 Get:83 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-dev all 2019.2-1ubuntu1 [594 kB] #7 32.88 Get:84 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-core-dev all 2019.2-1ubuntu1 [2620 B] #7 32.92 Get:85 http://archive.ubuntu.com/ubuntu focal/main amd64 libice-dev amd64 2:1.0.10-0ubuntu1 [47.8 kB] #7 32.97 Get:86 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libicu-dev amd64 66.1-2ubuntu2.1 [9451 kB] #7 34.76 Get:87 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libncurses-dev amd64 6.2-0ubuntu2.1 [340 kB] #7 34.86 Get:88 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libncursesw5-dev amd64 6.2-0ubuntu2.1 [988 B] #7 34.91 Get:89 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnspr4 amd64 2:4.35-0ubuntu0.20.04.1 [108 kB] #7 34.97 Get:90 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnspr4-dev amd64 2:4.35-0ubuntu0.20.04.1 [207 kB] #7 35.05 Get:91 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnss3 amd64 2:3.98-0ubuntu0.20.04.2 [1391 kB] #7 35.35 Get:92 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libnss3-dev amd64 2:3.98-0ubuntu0.20.04.2 [246 kB] #7 35.44 Get:93 http://archive.ubuntu.com/ubuntu focal/main amd64 libpng-tools amd64 1.6.37-2 [26.1 kB] #7 35.53 Get:94 http://archive.ubuntu.com/ubuntu focal/main amd64 libpthread-stubs0-dev amd64 0.4-1 [5384 B] #7 35.57 Get:95 http://archive.ubuntu.com/ubuntu focal/main amd64 libreadline-dev amd64 8.0-4 [141 kB] #7 35.75 Get:96 http://archive.ubuntu.com/ubuntu focal/main amd64 libsm6 amd64 2:1.2.3-1 [16.1 kB] #7 35.80 Get:97 http://archive.ubuntu.com/ubuntu focal/main amd64 libsm-dev amd64 2:1.2.3-1 [17.0 kB] #7 35.85 Get:98 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsqlite3-dev amd64 3.31.1-4ubuntu0.6 [696 kB] #7 36.05 Get:99 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libssl-dev amd64 1.1.1f-1ubuntu2.22 [1586 kB] #7 36.41 Get:100 http://archive.ubuntu.com/ubuntu focal/main amd64 libtcl8.6 amd64 8.6.10+dfsg-1 [902 kB] #7 36.61 Get:101 http://archive.ubuntu.com/ubuntu focal/main amd64 libxrender1 amd64 1:0.9.10-1 [18.7 kB] #7 36.66 Get:102 http://archive.ubuntu.com/ubuntu focal/main amd64 libxft2 amd64 2.3.3-0ubuntu1 [39.2 kB] #7 36.71 Get:103 http://archive.ubuntu.com/ubuntu focal/main amd64 libxss1 amd64 1:1.2.3-1 [8140 B] #7 36.75 Get:104 http://archive.ubuntu.com/ubuntu focal/main amd64 libtk8.6 amd64 8.6.10-1 [714 kB] #7 36.96 Get:105 http://archive.ubuntu.com/ubuntu focal/main amd64 libutempter0 amd64 1.1.6-4 [8256 B] #7 37.01 Get:106 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwayland-client0 amd64 1.18.0-1ubuntu0.1 [23.9 kB] #7 37.10 Get:107 http://archive.ubuntu.com/ubuntu focal/main amd64 libxau-dev amd64 1:1.0.9-0ubuntu1 [9552 B] #7 37.15 Get:108 http://archive.ubuntu.com/ubuntu focal/main amd64 libxdmcp-dev amd64 1:1.1.3-0ubuntu1 [25.3 kB] #7 37.21 Get:109 http://archive.ubuntu.com/ubuntu focal/main amd64 xtrans-dev all 1.4.0-1 [68.9 kB] #7 37.30 Get:110 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb1-dev amd64 1.14-2 [80.5 kB] #7 37.36 Get:111 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-dev amd64 2:1.6.9-2ubuntu1.6 [648 kB] #7 37.54 Get:112 http://archive.ubuntu.com/ubuntu focal/main amd64 libxt6 amd64 1:1.1.5-1 [160 kB] #7 37.61 Get:113 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmu6 amd64 2:1.1.3-0ubuntu1 [45.8 kB] #7 37.66 Get:114 http://archive.ubuntu.com/ubuntu focal/main amd64 libxaw7 amd64 2:1.0.13-1 [173 kB] #7 37.73 Get:115 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-randr0 amd64 1.14-2 [16.3 kB] #7 37.82 Get:116 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-shape0 amd64 1.14-2 [5928 B] #7 37.86 Get:117 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcomposite1 amd64 1:0.4.5-1 [6976 B] #7 37.91 Get:118 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-xext-dev all 2019.2-1ubuntu1 [2616 B] #7 37.95 Get:119 http://archive.ubuntu.com/ubuntu focal/main amd64 libxext-dev amd64 2:1.3.4-0ubuntu1 [82.2 kB] #7 38.11 Get:120 http://archive.ubuntu.com/ubuntu focal/main amd64 libxrender-dev amd64 1:0.9.10-1 [24.9 kB] #7 38.17 Get:121 http://archive.ubuntu.com/ubuntu focal/main amd64 libxft-dev amd64 2.3.3-0ubuntu1 [49.1 kB] #7 38.23 Get:122 http://archive.ubuntu.com/ubuntu focal/main amd64 libxi6 amd64 2:1.7.10-0ubuntu1 [29.9 kB] #7 38.28 Get:123 http://archive.ubuntu.com/ubuntu focal/main amd64 libxinerama1 amd64 2:1.1.4-2 [6904 B] #7 38.33 Get:124 http://archive.ubuntu.com/ubuntu focal/main amd64 libxkbfile1 amd64 1:1.1.0-1 [65.3 kB] #7 38.40 Get:125 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxml2-dev amd64 2.9.10+dfsg-5ubuntu0.20.04.7 [735 kB] #7 38.62 Get:126 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1 amd64 1.2.28-2 [127 kB] #7 38.72 Get:127 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-gcrypt amd64 1.2.28-2 [41.5 kB] #7 38.82 Get:128 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-gnutls amd64 1.2.28-2 [33.4 kB] #7 38.88 Get:129 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-nss amd64 1.2.28-2 [61.8 kB] #7 38.96 Get:130 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-openssl amd64 1.2.28-2 [75.9 kB] #7 39.03 Get:131 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxslt1-dev amd64 1.1.34-4ubuntu0.20.04.1 [219 kB] #7 39.14 Get:132 http://archive.ubuntu.com/ubuntu focal/main amd64 libxmlsec1-dev amd64 1.2.28-2 [387 kB] #7 39.25 Get:133 http://archive.ubuntu.com/ubuntu focal/main amd64 libxrandr2 amd64 2:1.5.2-0ubuntu1 [18.5 kB] #7 39.29 Get:134 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-scrnsaver-dev all 2019.2-1ubuntu1 [2624 B] #7 39.33 Get:135 http://archive.ubuntu.com/ubuntu focal/main amd64 libxss-dev amd64 1:1.2.3-1 [11.9 kB] #7 39.37 Get:136 http://archive.ubuntu.com/ubuntu focal/main amd64 libxt-dev amd64 1:1.1.5-1 [395 kB] #7 39.50 Get:137 http://archive.ubuntu.com/ubuntu focal/main amd64 libxtst6 amd64 2:1.2.3-1 [12.8 kB] #7 39.59 Get:138 http://archive.ubuntu.com/ubuntu focal/main amd64 libxv1 amd64 2:1.0.11-1 [10.7 kB] #7 39.64 Get:139 http://archive.ubuntu.com/ubuntu focal/main amd64 libxxf86dga1 amd64 2:1.1.5-0ubuntu1 [12.0 kB] #7 39.69 Get:140 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10-runtime amd64 1:10.0.0-4ubuntu1 [180 kB] #7 39.88 Get:141 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-runtime amd64 1:10.0-50~exp1 [2916 B] #7 39.93 Get:142 http://archive.ubuntu.com/ubuntu focal/main amd64 libpfm4 amd64 4.10.1+git20-g7700f49-2 [266 kB] #7 40.02 Get:143 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10 amd64 1:10.0.0-4ubuntu1 [5214 kB] #7 41.04 Get:144 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm amd64 1:10.0-50~exp1 [3880 B] #7 41.08 Get:145 http://archive.ubuntu.com/ubuntu focal/main amd64 libffi-dev amd64 3.3-4 [57.0 kB] #7 41.13 Get:146 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-pygments all 2.3.1+dfsg-1ubuntu2.2 [579 kB] #7 41.29 Get:147 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10-tools amd64 1:10.0.0-4ubuntu1 [317 kB] #7 41.38 Get:148 http://archive.ubuntu.com/ubuntu focal/universe amd64 libz3-4 amd64 4.8.7-4build1 [6792 kB] #7 42.66 Get:149 http://archive.ubuntu.com/ubuntu focal/universe amd64 libz3-dev amd64 4.8.7-4build1 [67.5 kB] #7 42.81 Get:150 http://archive.ubuntu.com/ubuntu focal/universe amd64 llvm-10-dev amd64 1:10.0.0-4ubuntu1 [26.0 MB] #7 47.54 Get:151 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 mesa-vulkan-drivers amd64 21.2.6-0ubuntu0.1~20.04.2 [5788 kB] #7 48.60 Get:152 http://archive.ubuntu.com/ubuntu focal/main amd64 tcl8.6 amd64 8.6.10+dfsg-1 [14.8 kB] #7 48.65 Get:153 http://archive.ubuntu.com/ubuntu focal/universe amd64 tcl amd64 8.6.9+1 [5112 B] #7 48.69 Get:154 http://archive.ubuntu.com/ubuntu focal/main amd64 tcl8.6-dev amd64 8.6.10+dfsg-1 [905 kB] #7 48.89 Get:155 http://archive.ubuntu.com/ubuntu focal/universe amd64 tcl-dev amd64 8.6.9+1 [5760 B] #7 48.94 Get:156 http://archive.ubuntu.com/ubuntu focal/main amd64 tk8.6 amd64 8.6.10-1 [12.5 kB] #7 48.98 Get:157 http://archive.ubuntu.com/ubuntu focal/universe amd64 tk amd64 8.6.9+1 [3240 B] #7 49.02 Get:158 http://archive.ubuntu.com/ubuntu focal/main amd64 tk8.6-dev amd64 8.6.10-1 [711 kB] #7 49.20 Get:159 http://archive.ubuntu.com/ubuntu focal/universe amd64 tk-dev amd64 8.6.9+1 [3076 B] #7 49.29 Get:160 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglvnd0 amd64 1.3.2-1~ubuntu0.20.04.2 [48.1 kB] #7 49.42 Get:161 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglx0 amd64 1.3.2-1~ubuntu0.20.04.2 [32.5 kB] #7 49.48 Get:162 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl1 amd64 1.3.2-1~ubuntu0.20.04.2 [85.8 kB] #7 49.57 Get:163 http://archive.ubuntu.com/ubuntu focal/main amd64 x11-utils amd64 7.7+5 [199 kB] #7 49.68 Get:164 http://archive.ubuntu.com/ubuntu focal/main amd64 xbitmaps all 1.1.1-2 [28.1 kB] #7 49.72 Get:165 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 xterm amd64 353-1ubuntu1.20.04.2 [765 kB] #7 49.91 Get:166 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 liblzma-dev amd64 5.2.4-1ubuntu1.1 [147 kB] #7 49.98 Get:167 http://archive.ubuntu.com/ubuntu focal/main amd64 libtasn1-doc all 4.16.0-2 [303 kB] #7 50.23 debconf: delaying package configuration, since apt-utils is not installed #7 50.28 Fetched 138 MB in 48s (2890 kB/s) #7 50.34 Selecting previously unselected package libpython3.8-minimal:amd64. #7 50.34 (Reading database ... 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libfontconfig1-dev:amd64. #7 58.61 Preparing to unpack .../036-libfontconfig1-dev_2.13.1-2ubuntu3_amd64.deb ... #7 58.61 Unpacking libfontconfig1-dev:amd64 (2.13.1-2ubuntu3) ... #7 58.74 Selecting previously unselected package libfontenc1:amd64. #7 58.74 Preparing to unpack .../037-libfontenc1_1%3a1.1.4-0ubuntu1_amd64.deb ... #7 58.75 Unpacking libfontenc1:amd64 (1:1.1.4-0ubuntu1) ... #7 58.82 Selecting previously unselected package libgpg-error-dev. #7 58.82 Preparing to unpack .../038-libgpg-error-dev_1.37-1_amd64.deb ... #7 58.83 Unpacking libgpg-error-dev (1.37-1) ... #7 58.91 Selecting previously unselected package libgcrypt20-dev. #7 58.91 Preparing to unpack .../039-libgcrypt20-dev_1.8.5-5ubuntu1.1_amd64.deb ... #7 58.92 Unpacking libgcrypt20-dev (1.8.5-5ubuntu1.1) ... #7 59.06 Selecting previously unselected package libglapi-mesa:amd64. #7 59.06 Preparing to unpack .../040-libglapi-mesa_21.2.6-0ubuntu0.1~20.04.2_amd64.deb ... #7 59.07 Unpacking libglapi-mesa:amd64 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#7 62.71 Preparing to unpack .../050-libxcb-present0_1.14-2_amd64.deb ... #7 62.71 Unpacking libxcb-present0:amd64 (1.14-2) ... #7 62.79 Selecting previously unselected package libxcb-shm0:amd64. #7 62.79 Preparing to unpack .../051-libxcb-shm0_1.14-2_amd64.deb ... #7 62.80 Unpacking libxcb-shm0:amd64 (1.14-2) ... #7 62.87 Selecting previously unselected package libxcb-sync1:amd64. #7 62.87 Preparing to unpack .../052-libxcb-sync1_1.14-2_amd64.deb ... #7 62.88 Unpacking libxcb-sync1:amd64 (1.14-2) ... #7 62.96 Selecting previously unselected package libxcb-xfixes0:amd64. #7 62.96 Preparing to unpack .../053-libxcb-xfixes0_1.14-2_amd64.deb ... #7 62.97 Unpacking libxcb-xfixes0:amd64 (1.14-2) ... #7 63.04 Selecting previously unselected package libxfixes3:amd64. #7 63.05 Preparing to unpack .../054-libxfixes3_1%3a5.0.3-2_amd64.deb ... #7 63.05 Unpacking libxfixes3:amd64 (1:5.0.3-2) ... #7 63.13 Selecting previously unselected package libxshmfence1:amd64. #7 63.13 Preparing to unpack .../055-libxshmfence1_1.3-1_amd64.deb ... #7 63.14 Unpacking libxshmfence1:amd64 (1.3-1) ... #7 63.21 Selecting previously unselected package libxxf86vm1:amd64. #7 63.21 Preparing to unpack .../056-libxxf86vm1_1%3a1.1.4-1build1_amd64.deb ... #7 63.22 Unpacking libxxf86vm1:amd64 (1:1.1.4-1build1) ... #7 63.30 Selecting previously unselected package libglx-mesa0:amd64. #7 63.30 Preparing to unpack .../057-libglx-mesa0_21.2.6-0ubuntu0.1~20.04.2_amd64.deb ... #7 63.31 Unpacking libglx-mesa0:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 63.41 Selecting previously unselected package libgmpxx4ldbl:amd64. #7 63.41 Preparing to unpack .../058-libgmpxx4ldbl_2%3a6.2.0+dfsg-4ubuntu0.1_amd64.deb ... #7 63.42 Unpacking libgmpxx4ldbl:amd64 (2:6.2.0+dfsg-4ubuntu0.1) ... #7 63.48 Selecting previously unselected package libgmp-dev:amd64. #7 63.49 Preparing to unpack .../059-libgmp-dev_2%3a6.2.0+dfsg-4ubuntu0.1_amd64.deb ... #7 63.50 Unpacking libgmp-dev:amd64 (2:6.2.0+dfsg-4ubuntu0.1) ... #7 63.61 Selecting previously unselected package libgnutls-openssl27:amd64. #7 63.61 Preparing to unpack .../060-libgnutls-openssl27_3.6.13-2ubuntu1.11_amd64.deb ... #7 63.62 Unpacking libgnutls-openssl27:amd64 (3.6.13-2ubuntu1.11) ... #7 63.69 Selecting previously unselected package libunbound8:amd64. #7 63.69 Preparing to unpack .../061-libunbound8_1.9.4-2ubuntu1.5_amd64.deb ... #7 63.70 Unpacking libunbound8:amd64 (1.9.4-2ubuntu1.5) ... #7 63.82 Selecting previously unselected package libgnutls-dane0:amd64. #7 63.83 Preparing to unpack .../062-libgnutls-dane0_3.6.13-2ubuntu1.11_amd64.deb ... #7 63.83 Unpacking libgnutls-dane0:amd64 (3.6.13-2ubuntu1.11) ... #7 63.91 Selecting previously unselected package libgnutlsxx28:amd64. #7 63.91 Preparing to unpack .../063-libgnutlsxx28_3.6.13-2ubuntu1.11_amd64.deb ... #7 63.92 Unpacking libgnutlsxx28:amd64 (3.6.13-2ubuntu1.11) ... #7 63.98 Selecting previously unselected package libidn2-dev:amd64. #7 63.99 Preparing to unpack .../064-libidn2-dev_2.2.0-2_amd64.deb ... #7 63.99 Unpacking libidn2-dev:amd64 (2.2.0-2) ... #7 64.07 Selecting previously unselected package libp11-kit-dev:amd64. #7 64.08 Preparing to unpack .../065-libp11-kit-dev_0.23.20-1ubuntu0.1_amd64.deb ... #7 64.08 Unpacking libp11-kit-dev:amd64 (0.23.20-1ubuntu0.1) ... #7 64.16 Selecting previously unselected package libtasn1-6-dev:amd64. #7 64.17 Preparing to unpack .../066-libtasn1-6-dev_4.16.0-2_amd64.deb ... #7 64.18 Unpacking libtasn1-6-dev:amd64 (4.16.0-2) ... #7 64.26 Selecting previously unselected package nettle-dev:amd64. #7 64.26 Preparing to unpack .../067-nettle-dev_3.5.1+really3.5.1-2ubuntu0.2_amd64.deb ... #7 64.27 Unpacking nettle-dev:amd64 (3.5.1+really3.5.1-2ubuntu0.2) ... #7 64.44 Selecting previously unselected package libgnutls28-dev:amd64. #7 64.44 Preparing to unpack .../068-libgnutls28-dev_3.6.13-2ubuntu1.11_amd64.deb ... #7 64.45 Unpacking libgnutls28-dev:amd64 (3.6.13-2ubuntu1.11) ... #7 64.63 Selecting previously unselected package x11-common. #7 64.64 Preparing to unpack .../069-x11-common_1%3a7.7+19ubuntu14_all.deb ... #7 64.67 dpkg-query: no packages found matching nux-tools #7 64.69 Unpacking x11-common (1:7.7+19ubuntu14) ... #7 64.77 Selecting previously unselected package libice6:amd64. #7 64.78 Preparing to unpack .../070-libice6_2%3a1.0.10-0ubuntu1_amd64.deb ... #7 64.78 Unpacking libice6:amd64 (2:1.0.10-0ubuntu1) ... #7 64.85 Selecting previously unselected package xorg-sgml-doctools. #7 64.86 Preparing to unpack .../071-xorg-sgml-doctools_1%3a1.11-1_all.deb ... #7 64.87 Unpacking xorg-sgml-doctools (1:1.11-1) ... #7 64.93 Selecting previously unselected package x11proto-dev. #7 64.94 Preparing to unpack .../072-x11proto-dev_2019.2-1ubuntu1_all.deb ... #7 64.94 Unpacking x11proto-dev (2019.2-1ubuntu1) ... #7 65.08 Selecting previously unselected package x11proto-core-dev. #7 65.09 Preparing to unpack .../073-x11proto-core-dev_2019.2-1ubuntu1_all.deb ... #7 65.10 Unpacking x11proto-core-dev (2019.2-1ubuntu1) ... #7 65.16 Selecting previously unselected package libice-dev:amd64. #7 65.16 Preparing to unpack .../074-libice-dev_2%3a1.0.10-0ubuntu1_amd64.deb ... #7 65.17 Unpacking libice-dev:amd64 (2:1.0.10-0ubuntu1) ... #7 65.25 Selecting previously unselected package libicu-dev:amd64. #7 65.25 Preparing to unpack .../075-libicu-dev_66.1-2ubuntu2.1_amd64.deb ... #7 65.26 Unpacking libicu-dev:amd64 (66.1-2ubuntu2.1) ... #7 66.22 Selecting previously unselected package libncurses-dev:amd64. #7 66.23 Preparing to unpack .../076-libncurses-dev_6.2-0ubuntu2.1_amd64.deb ... #7 66.24 Unpacking libncurses-dev:amd64 (6.2-0ubuntu2.1) ... #7 66.36 Selecting previously unselected package libncursesw5-dev:amd64. #7 66.36 Preparing to unpack .../077-libncursesw5-dev_6.2-0ubuntu2.1_amd64.deb ... #7 66.37 Unpacking libncursesw5-dev:amd64 (6.2-0ubuntu2.1) ... #7 66.44 Selecting previously unselected package libnspr4:amd64. #7 66.44 Preparing to unpack .../078-libnspr4_2%3a4.35-0ubuntu0.20.04.1_amd64.deb ... #7 66.45 Unpacking libnspr4:amd64 (2:4.35-0ubuntu0.20.04.1) ... #7 66.54 Selecting previously unselected package libnspr4-dev. #7 66.54 Preparing to unpack .../079-libnspr4-dev_2%3a4.35-0ubuntu0.20.04.1_amd64.deb ... #7 66.55 Unpacking libnspr4-dev (2:4.35-0ubuntu0.20.04.1) ... #7 66.66 Selecting previously unselected package libnss3:amd64. #7 66.67 Preparing to unpack .../080-libnss3_2%3a3.98-0ubuntu0.20.04.2_amd64.deb ... #7 66.67 Unpacking libnss3:amd64 (2:3.98-0ubuntu0.20.04.2) ... #7 66.88 Selecting previously unselected package libnss3-dev:amd64. #7 66.88 Preparing to unpack .../081-libnss3-dev_2%3a3.98-0ubuntu0.20.04.2_amd64.deb ... #7 66.89 Unpacking libnss3-dev:amd64 (2:3.98-0ubuntu0.20.04.2) ... #7 67.00 Selecting previously unselected package libpng-tools. #7 67.00 Preparing to unpack .../082-libpng-tools_1.6.37-2_amd64.deb ... #7 67.01 Unpacking libpng-tools (1.6.37-2) ... #7 67.07 Selecting previously unselected package libpthread-stubs0-dev:amd64. #7 67.07 Preparing to unpack .../083-libpthread-stubs0-dev_0.4-1_amd64.deb ... #7 67.08 Unpacking libpthread-stubs0-dev:amd64 (0.4-1) ... #7 67.17 Selecting previously unselected package libreadline-dev:amd64. #7 67.18 Preparing to unpack .../084-libreadline-dev_8.0-4_amd64.deb ... #7 67.19 Unpacking libreadline-dev:amd64 (8.0-4) ... #7 67.29 Selecting previously unselected package libsm6:amd64. #7 67.29 Preparing to unpack .../085-libsm6_2%3a1.2.3-1_amd64.deb ... #7 67.30 Unpacking libsm6:amd64 (2:1.2.3-1) ... #7 67.37 Selecting previously unselected package libsm-dev:amd64. #7 67.37 Preparing to unpack .../086-libsm-dev_2%3a1.2.3-1_amd64.deb ... #7 67.38 Unpacking libsm-dev:amd64 (2:1.2.3-1) ... #7 67.45 Selecting previously unselected package libsqlite3-dev:amd64. #7 67.45 Preparing to unpack .../087-libsqlite3-dev_3.31.1-4ubuntu0.6_amd64.deb ... #7 67.46 Unpacking libsqlite3-dev:amd64 (3.31.1-4ubuntu0.6) ... #7 67.60 Selecting previously unselected package libssl-dev:amd64. #7 67.60 Preparing to unpack .../088-libssl-dev_1.1.1f-1ubuntu2.22_amd64.deb ... #7 67.61 Unpacking libssl-dev:amd64 (1.1.1f-1ubuntu2.22) ... #7 67.84 Selecting previously unselected package libtcl8.6:amd64. #7 67.85 Preparing to unpack .../089-libtcl8.6_8.6.10+dfsg-1_amd64.deb ... #7 67.85 Unpacking libtcl8.6:amd64 (8.6.10+dfsg-1) ... #7 68.05 Selecting previously unselected package libxrender1:amd64. #7 68.05 Preparing to unpack .../090-libxrender1_1%3a0.9.10-1_amd64.deb ... #7 68.06 Unpacking libxrender1:amd64 (1:0.9.10-1) ... #7 68.13 Selecting previously unselected package libxft2:amd64. #7 68.14 Preparing to unpack .../091-libxft2_2.3.3-0ubuntu1_amd64.deb ... #7 68.14 Unpacking libxft2:amd64 (2.3.3-0ubuntu1) ... #7 68.23 Selecting previously unselected package libxss1:amd64. #7 68.23 Preparing to unpack .../092-libxss1_1%3a1.2.3-1_amd64.deb ... #7 68.24 Unpacking libxss1:amd64 (1:1.2.3-1) ... #7 68.31 Selecting previously unselected package libtk8.6:amd64. #7 68.31 Preparing to unpack .../093-libtk8.6_8.6.10-1_amd64.deb ... #7 68.32 Unpacking libtk8.6:amd64 (8.6.10-1) ... #7 68.47 Selecting previously unselected package libutempter0:amd64. #7 68.47 Preparing to unpack .../094-libutempter0_1.1.6-4_amd64.deb ... #7 68.48 Unpacking libutempter0:amd64 (1.1.6-4) ... #7 68.55 Selecting previously unselected package libwayland-client0:amd64. #7 68.56 Preparing to unpack .../095-libwayland-client0_1.18.0-1ubuntu0.1_amd64.deb ... #7 68.56 Unpacking libwayland-client0:amd64 (1.18.0-1ubuntu0.1) ... #7 68.63 Selecting previously unselected package libxau-dev:amd64. #7 68.63 Preparing to unpack .../096-libxau-dev_1%3a1.0.9-0ubuntu1_amd64.deb ... #7 68.64 Unpacking libxau-dev:amd64 (1:1.0.9-0ubuntu1) ... #7 68.70 Selecting previously unselected package libxdmcp-dev:amd64. #7 68.71 Preparing to unpack .../097-libxdmcp-dev_1%3a1.1.3-0ubuntu1_amd64.deb ... #7 68.71 Unpacking libxdmcp-dev:amd64 (1:1.1.3-0ubuntu1) ... #7 68.78 Selecting previously unselected package xtrans-dev. #7 68.78 Preparing to unpack .../098-xtrans-dev_1.4.0-1_all.deb ... #7 68.79 Unpacking xtrans-dev (1.4.0-1) ... #7 68.87 Selecting previously unselected package libxcb1-dev:amd64. #7 68.87 Preparing to unpack .../099-libxcb1-dev_1.14-2_amd64.deb ... #7 68.88 Unpacking libxcb1-dev:amd64 (1.14-2) ... #7 68.96 Selecting previously unselected package libx11-dev:amd64. #7 68.96 Preparing to unpack .../100-libx11-dev_2%3a1.6.9-2ubuntu1.6_amd64.deb ... #7 68.97 Unpacking libx11-dev:amd64 (2:1.6.9-2ubuntu1.6) ... #7 69.12 Selecting previously unselected package libxt6:amd64. #7 69.12 Preparing to unpack .../101-libxt6_1%3a1.1.5-1_amd64.deb ... #7 69.12 Unpacking libxt6:amd64 (1:1.1.5-1) ... #7 69.22 Selecting previously unselected package libxmu6:amd64. #7 69.22 Preparing to unpack .../102-libxmu6_2%3a1.1.3-0ubuntu1_amd64.deb ... #7 69.23 Unpacking libxmu6:amd64 (2:1.1.3-0ubuntu1) ... #7 69.31 Selecting previously unselected package libxaw7:amd64. #7 69.32 Preparing to unpack .../103-libxaw7_2%3a1.0.13-1_amd64.deb ... #7 69.32 Unpacking libxaw7:amd64 (2:1.0.13-1) ... #7 69.43 Selecting previously unselected package libxcb-randr0:amd64. #7 69.43 Preparing to unpack .../104-libxcb-randr0_1.14-2_amd64.deb ... #7 69.44 Unpacking libxcb-randr0:amd64 (1.14-2) ... #7 69.51 Selecting previously unselected package libxcb-shape0:amd64. #7 69.52 Preparing to unpack .../105-libxcb-shape0_1.14-2_amd64.deb ... #7 69.52 Unpacking libxcb-shape0:amd64 (1.14-2) ... #7 69.59 Selecting previously unselected package libxcomposite1:amd64. #7 69.60 Preparing to unpack .../106-libxcomposite1_1%3a0.4.5-1_amd64.deb ... #7 69.61 Unpacking libxcomposite1:amd64 (1:0.4.5-1) ... #7 69.67 Selecting previously unselected package x11proto-xext-dev. #7 69.67 Preparing to unpack .../107-x11proto-xext-dev_2019.2-1ubuntu1_all.deb ... #7 69.68 Unpacking x11proto-xext-dev (2019.2-1ubuntu1) ... #7 69.74 Selecting previously unselected package libxext-dev:amd64. #7 69.75 Preparing to unpack .../108-libxext-dev_2%3a1.3.4-0ubuntu1_amd64.deb ... #7 69.75 Unpacking libxext-dev:amd64 (2:1.3.4-0ubuntu1) ... #7 69.84 Selecting previously unselected package libxrender-dev:amd64. #7 69.84 Preparing to unpack .../109-libxrender-dev_1%3a0.9.10-1_amd64.deb ... #7 69.84 Unpacking libxrender-dev:amd64 (1:0.9.10-1) ... #7 69.90 Selecting previously unselected package libxft-dev:amd64. #7 69.90 Preparing to unpack .../110-libxft-dev_2.3.3-0ubuntu1_amd64.deb ... #7 69.91 Unpacking libxft-dev:amd64 (2.3.3-0ubuntu1) ... #7 69.98 Selecting previously unselected package libxi6:amd64. #7 69.99 Preparing to unpack .../111-libxi6_2%3a1.7.10-0ubuntu1_amd64.deb ... #7 69.99 Unpacking libxi6:amd64 (2:1.7.10-0ubuntu1) ... #7 70.07 Selecting previously unselected package libxinerama1:amd64. #7 70.07 Preparing to unpack .../112-libxinerama1_2%3a1.1.4-2_amd64.deb ... #7 70.08 Unpacking libxinerama1:amd64 (2:1.1.4-2) ... #7 70.15 Selecting previously unselected package libxkbfile1:amd64. #7 70.15 Preparing to unpack .../113-libxkbfile1_1%3a1.1.0-1_amd64.deb ... #7 70.16 Unpacking libxkbfile1:amd64 (1:1.1.0-1) ... #7 70.23 Selecting previously unselected package libxml2-dev:amd64. #7 70.24 Preparing to unpack .../114-libxml2-dev_2.9.10+dfsg-5ubuntu0.20.04.7_amd64.deb ... #7 70.25 Unpacking libxml2-dev:amd64 (2.9.10+dfsg-5ubuntu0.20.04.7) ... #7 70.40 Selecting previously unselected package libxmlsec1:amd64. #7 70.41 Preparing to unpack .../115-libxmlsec1_1.2.28-2_amd64.deb ... #7 70.42 Unpacking libxmlsec1:amd64 (1.2.28-2) ... #7 70.52 Selecting previously unselected package libxmlsec1-gcrypt:amd64. #7 70.52 Preparing to unpack .../116-libxmlsec1-gcrypt_1.2.28-2_amd64.deb ... #7 70.53 Unpacking libxmlsec1-gcrypt:amd64 (1.2.28-2) ... #7 70.61 Selecting previously unselected package libxmlsec1-gnutls:amd64. #7 70.62 Preparing to unpack .../117-libxmlsec1-gnutls_1.2.28-2_amd64.deb ... #7 70.62 Unpacking libxmlsec1-gnutls:amd64 (1.2.28-2) ... #7 70.69 Selecting previously unselected package libxmlsec1-nss:amd64. #7 70.69 Preparing to unpack .../118-libxmlsec1-nss_1.2.28-2_amd64.deb ... #7 70.70 Unpacking libxmlsec1-nss:amd64 (1.2.28-2) ... #7 70.79 Selecting previously unselected package libxmlsec1-openssl:amd64. #7 70.80 Preparing to unpack .../119-libxmlsec1-openssl_1.2.28-2_amd64.deb ... #7 70.80 Unpacking libxmlsec1-openssl:amd64 (1.2.28-2) ... #7 70.89 Selecting previously unselected package libxslt1-dev:amd64. #7 70.89 Preparing to unpack .../120-libxslt1-dev_1.1.34-4ubuntu0.20.04.1_amd64.deb ... #7 70.90 Unpacking libxslt1-dev:amd64 (1.1.34-4ubuntu0.20.04.1) ... #7 71.00 Selecting previously unselected package libxmlsec1-dev. #7 71.01 Preparing to unpack .../121-libxmlsec1-dev_1.2.28-2_amd64.deb ... #7 71.01 Unpacking libxmlsec1-dev (1.2.28-2) ... #7 71.15 Selecting previously unselected package libxrandr2:amd64. #7 71.15 Preparing to unpack .../122-libxrandr2_2%3a1.5.2-0ubuntu1_amd64.deb ... #7 71.15 Unpacking libxrandr2:amd64 (2:1.5.2-0ubuntu1) ... #7 71.21 Selecting previously unselected package x11proto-scrnsaver-dev. #7 71.22 Preparing to unpack .../123-x11proto-scrnsaver-dev_2019.2-1ubuntu1_all.deb ... #7 71.22 Unpacking x11proto-scrnsaver-dev (2019.2-1ubuntu1) ... #7 71.28 Selecting previously unselected package libxss-dev:amd64. #7 71.28 Preparing to unpack .../124-libxss-dev_1%3a1.2.3-1_amd64.deb ... #7 71.29 Unpacking libxss-dev:amd64 (1:1.2.3-1) ... #7 71.36 Selecting previously unselected package libxt-dev:amd64. #7 71.36 Preparing to unpack .../125-libxt-dev_1%3a1.1.5-1_amd64.deb ... #7 71.37 Unpacking libxt-dev:amd64 (1:1.1.5-1) ... #7 71.50 Selecting previously unselected package libxtst6:amd64. #7 71.51 Preparing to unpack .../126-libxtst6_2%3a1.2.3-1_amd64.deb ... #7 71.52 Unpacking libxtst6:amd64 (2:1.2.3-1) ... #7 71.58 Selecting previously unselected package libxv1:amd64. #7 71.59 Preparing to unpack .../127-libxv1_2%3a1.0.11-1_amd64.deb ... #7 71.59 Unpacking libxv1:amd64 (2:1.0.11-1) ... #7 71.66 Selecting previously unselected package libxxf86dga1:amd64. #7 71.66 Preparing to unpack .../128-libxxf86dga1_2%3a1.1.5-0ubuntu1_amd64.deb ... #7 71.66 Unpacking libxxf86dga1:amd64 (2:1.1.5-0ubuntu1) ... #7 71.73 Selecting previously unselected package llvm-10-runtime. #7 71.74 Preparing to unpack .../129-llvm-10-runtime_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 71.74 Unpacking llvm-10-runtime (1:10.0.0-4ubuntu1) ... #7 71.84 Selecting previously unselected package llvm-runtime. #7 71.84 Preparing to unpack .../130-llvm-runtime_1%3a10.0-50~exp1_amd64.deb ... #7 71.85 Unpacking llvm-runtime (1:10.0-50~exp1) ... #7 71.93 Selecting previously unselected package libpfm4:amd64. #7 71.93 Preparing to unpack .../131-libpfm4_4.10.1+git20-g7700f49-2_amd64.deb ... #7 71.94 Unpacking libpfm4:amd64 (4.10.1+git20-g7700f49-2) ... #7 72.05 Selecting previously unselected package llvm-10. #7 72.05 Preparing to unpack .../132-llvm-10_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 72.06 Unpacking llvm-10 (1:10.0.0-4ubuntu1) ... #7 72.62 Selecting previously unselected package llvm. #7 72.62 Preparing to unpack .../133-llvm_1%3a10.0-50~exp1_amd64.deb ... #7 72.63 Unpacking llvm (1:10.0-50~exp1) ... #7 72.71 Selecting previously unselected package libffi-dev:amd64. #7 72.71 Preparing to unpack .../134-libffi-dev_3.3-4_amd64.deb ... #7 72.72 Unpacking libffi-dev:amd64 (3.3-4) ... #7 72.81 Selecting previously unselected package python3-pygments. #7 72.81 Preparing to unpack .../135-python3-pygments_2.3.1+dfsg-1ubuntu2.2_all.deb ... #7 72.82 Unpacking python3-pygments (2.3.1+dfsg-1ubuntu2.2) ... #7 72.95 Selecting previously unselected package llvm-10-tools. #7 72.95 Preparing to unpack .../136-llvm-10-tools_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 72.96 Unpacking llvm-10-tools (1:10.0.0-4ubuntu1) ... #7 73.12 Selecting previously unselected package libz3-4:amd64. #7 73.12 Preparing to unpack .../137-libz3-4_4.8.7-4build1_amd64.deb ... #7 73.13 Unpacking libz3-4:amd64 (4.8.7-4build1) ... #7 73.78 Selecting previously unselected package libz3-dev:amd64. #7 73.78 Preparing to unpack .../138-libz3-dev_4.8.7-4build1_amd64.deb ... #7 73.79 Unpacking libz3-dev:amd64 (4.8.7-4build1) ... #7 73.88 Selecting previously unselected package llvm-10-dev. #7 73.89 Preparing to unpack .../139-llvm-10-dev_1%3a10.0.0-4ubuntu1_amd64.deb ... #7 73.90 Unpacking llvm-10-dev (1:10.0.0-4ubuntu1) ... #7 76.60 Selecting previously unselected package mesa-vulkan-drivers:amd64. #7 76.60 Preparing to unpack .../140-mesa-vulkan-drivers_21.2.6-0ubuntu0.1~20.04.2_amd64.deb ... #7 76.61 Unpacking mesa-vulkan-drivers:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 77.24 Selecting previously unselected package tcl8.6. #7 77.24 Preparing to unpack .../141-tcl8.6_8.6.10+dfsg-1_amd64.deb ... #7 77.25 Unpacking tcl8.6 (8.6.10+dfsg-1) ... #7 77.32 Selecting previously unselected package tcl. #7 77.32 Preparing to unpack .../142-tcl_8.6.9+1_amd64.deb ... #7 77.34 Unpacking tcl (8.6.9+1) ... #7 77.40 Selecting previously unselected package tcl8.6-dev:amd64. #7 77.41 Preparing to unpack .../143-tcl8.6-dev_8.6.10+dfsg-1_amd64.deb ... #7 77.42 Unpacking tcl8.6-dev:amd64 (8.6.10+dfsg-1) ... #7 77.59 Selecting previously unselected package tcl-dev:amd64. #7 77.59 Preparing to unpack .../144-tcl-dev_8.6.9+1_amd64.deb ... #7 77.60 Unpacking tcl-dev:amd64 (8.6.9+1) ... #7 77.68 Selecting previously unselected package tk8.6. #7 77.69 Preparing to unpack .../145-tk8.6_8.6.10-1_amd64.deb ... #7 77.69 Unpacking tk8.6 (8.6.10-1) ... #7 77.76 Selecting previously unselected package tk. #7 77.77 Preparing to unpack .../146-tk_8.6.9+1_amd64.deb ... #7 77.78 Unpacking tk (8.6.9+1) ... #7 77.85 Selecting previously unselected package tk8.6-dev:amd64. #7 77.85 Preparing to unpack .../147-tk8.6-dev_8.6.10-1_amd64.deb ... #7 77.86 Unpacking tk8.6-dev:amd64 (8.6.10-1) ... #7 78.01 Selecting previously unselected package tk-dev:amd64. #7 78.01 Preparing to unpack .../148-tk-dev_8.6.9+1_amd64.deb ... #7 78.02 Unpacking tk-dev:amd64 (8.6.9+1) ... #7 78.10 Selecting previously unselected package libglvnd0:amd64. #7 78.10 Preparing to unpack .../149-libglvnd0_1.3.2-1~ubuntu0.20.04.2_amd64.deb ... #7 78.11 Unpacking libglvnd0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 78.21 Selecting previously unselected package libglx0:amd64. #7 78.21 Preparing to unpack .../150-libglx0_1.3.2-1~ubuntu0.20.04.2_amd64.deb ... #7 78.22 Unpacking libglx0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 78.31 Selecting previously unselected package libgl1:amd64. #7 78.31 Preparing to unpack .../151-libgl1_1.3.2-1~ubuntu0.20.04.2_amd64.deb ... #7 78.32 Unpacking libgl1:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 78.41 Selecting previously unselected package x11-utils. #7 78.42 Preparing to unpack .../152-x11-utils_7.7+5_amd64.deb ... #7 78.42 Unpacking x11-utils (7.7+5) ... #7 78.52 Selecting previously unselected package xbitmaps. #7 78.53 Preparing to unpack .../153-xbitmaps_1.1.1-2_all.deb ... #7 78.54 Unpacking xbitmaps (1.1.1-2) ... #7 78.63 Selecting previously unselected package xterm. #7 78.63 Preparing to unpack .../154-xterm_353-1ubuntu1.20.04.2_amd64.deb ... #7 78.64 Unpacking xterm (353-1ubuntu1.20.04.2) ... #7 78.78 Selecting previously unselected package liblzma-dev:amd64. #7 78.78 Preparing to unpack .../155-liblzma-dev_5.2.4-1ubuntu1.1_amd64.deb ... #7 78.79 Unpacking liblzma-dev:amd64 (5.2.4-1ubuntu1.1) ... #7 78.88 Selecting previously unselected package libtasn1-doc. #7 78.89 Preparing to unpack .../156-libtasn1-doc_4.16.0-2_all.deb ... #7 78.89 Unpacking libtasn1-doc (4.16.0-2) ... #7 79.03 Setting up libpipeline1:amd64 (1.5.2-2build1) ... #7 79.05 Setting up libxcb-dri3-0:amd64 (1.14-2) ... #7 79.08 Setting up bzip2-doc (1.0.8-2) ... #7 79.10 Setting up libx11-xcb1:amd64 (2:1.6.9-2ubuntu1.6) ... #7 79.12 Setting up libpciaccess0:amd64 (0.16-0ubuntu1) ... #7 79.14 Setting up libncurses-dev:amd64 (6.2-0ubuntu2.1) ... #7 79.16 Setting up libgnutls-openssl27:amd64 (3.6.13-2ubuntu1.11) ... #7 79.18 Setting up libxcb-xfixes0:amd64 (1.14-2) ... #7 79.20 Setting up mime-support (3.64ubuntu1) ... #7 79.27 Setting up libncursesw5-dev:amd64 (6.2-0ubuntu2.1) ... #7 79.29 Setting up libxrender1:amd64 (1:0.9.10-1) ... #7 79.31 Setting up xdg-user-dirs (0.17-2ubuntu1) ... #7 79.37 Setting up libpng-tools (1.6.37-2) ... #7 79.39 Setting up libmagic-mgc (1:5.38-4) ... #7 79.41 Setting up libyaml-0-2:amd64 (0.2.2-1) ... #7 79.44 Setting up libglib2.0-0:amd64 (2.64.6-1~ubuntu20.04.7) ... #7 79.46 No schema files found: doing nothing. #7 79.47 Setting up libglvnd0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 79.50 Setting up libxcb-glx0:amd64 (1.14-2) ... #7 79.52 Setting up libtasn1-doc (4.16.0-2) ... #7 79.54 Setting up libedit2:amd64 (3.1-20191231-1) ... #7 79.56 Setting up libxcb-shape0:amd64 (1.14-2) ... #7 79.59 Setting up x11-common (1:7.7+19ubuntu14) ... #7 79.84 update-rc.d: warning: start and stop actions are no longer supported; falling back to defaults #7 79.85 invoke-rc.d: could not determine current runlevel #7 79.86 invoke-rc.d: policy-rc.d denied execution of start. #7 79.87 Setting up libsensors-config (1:3.6.0-2ubuntu1.1) ... #7 79.91 Setting up libmagic1:amd64 (1:5.38-4) ... #7 79.95 Setting up libxext6:amd64 (2:1.3.4-0ubuntu1) ... #7 79.97 Setting up libxcb-shm0:amd64 (1.14-2) ... #7 79.99 Setting up file (1:5.38-4) ... #7 80.01 Setting up libffi-dev:amd64 (3.3-4) ... #7 80.04 Setting up libpthread-stubs0-dev:amd64 (0.4-1) ... #7 80.06 Setting up libxxf86vm1:amd64 (1:1.1.4-1build1) ... #7 80.08 Setting up libllvm10:amd64 (1:10.0.0-4ubuntu1) ... #7 80.10 Setting up libxcb-present0:amd64 (1.14-2) ... #7 80.12 Setting up xtrans-dev (1.4.0-1) ... #7 80.15 Setting up libfontenc1:amd64 (1:1.1.4-0ubuntu1) ... #7 80.17 Setting up libz3-4:amd64 (4.8.7-4build1) ... #7 80.19 Setting up libglib2.0-data (2.64.6-1~ubuntu20.04.7) ... #7 80.21 Setting up libgmpxx4ldbl:amd64 (2:6.2.0+dfsg-4ubuntu0.1) ... #7 80.24 Setting up libpfm4:amd64 (4.10.1+git20-g7700f49-2) ... #7 80.26 Setting up libexpat1-dev:amd64 (2.2.9-1ubuntu0.6) ... #7 80.28 Setting up libsqlite3-dev:amd64 (3.31.1-4ubuntu0.6) ... #7 80.30 Setting up libgpg-error-dev (1.37-1) ... #7 80.32 Setting up libnspr4:amd64 (2:4.35-0ubuntu0.20.04.1) ... #7 80.35 Setting up libxfixes3:amd64 (1:5.0.3-2) ... #7 80.37 Setting up libxcb-sync1:amd64 (1.14-2) ... #7 80.39 Setting up uuid-dev:amd64 (2.34-0.1ubuntu9.6) ... #7 80.41 Setting up libllvm12:amd64 (1:12.0.0-3ubuntu1~20.04.5) ... #7 80.44 Setting up shared-mime-info (1.15-1) ... #7 83.97 Setting up libxinerama1:amd64 (2:1.1.4-2) ... #7 83.99 Setting up libxv1:amd64 (2:1.0.11-1) ... #7 84.01 Setting up libssl-dev:amd64 (1.1.1f-1ubuntu2.22) ... #7 84.03 Setting up libevent-2.1-7:amd64 (2.1.11-stable-1) ... #7 84.06 Setting up libxrandr2:amd64 (2:1.5.2-0ubuntu1) ... #7 84.08 Setting up libtcl8.6:amd64 (8.6.10+dfsg-1) ... #7 84.10 Setting up binfmt-support (2.2.0-2) ... #7 84.34 invoke-rc.d: could not determine current runlevel #7 84.35 invoke-rc.d: policy-rc.d denied execution of start. #7 84.35 Setting up icu-devtools (66.1-2ubuntu2.1) ... #7 84.37 Setting up pkg-config (0.29.1-0ubuntu4) ... #7 84.45 Setting up libgnutlsxx28:amd64 (3.6.13-2ubuntu1.11) ... #7 84.47 Setting up libsensors5:amd64 (1:3.6.0-2ubuntu1.1) ... #7 84.49 Setting up libidn2-dev:amd64 (2.2.0-2) ... #7 84.51 Setting up libglapi-mesa:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 84.54 Setting up libvulkan1:amd64 (1.2.131.2-1) ... #7 84.56 Setting up libutempter0:amd64 (1.1.6-4) ... #7 84.58 Setting up liblzma-dev:amd64 (5.2.4-1ubuntu1.1) ... #7 84.60 Setting up libxcb-dri2-0:amd64 (1.14-2) ... #7 84.63 Setting up zlib1g-dev:amd64 (1:1.2.11.dfsg-2ubuntu1.5) ... #7 84.65 Setting up libxshmfence1:amd64 (1.3-1) ... #7 84.67 Setting up libxcb-randr0:amd64 (1.14-2) ... #7 84.70 Setting up xorg-sgml-doctools (1:1.11-1) ... #7 84.72 Setting up libxss1:amd64 (1:1.2.3-1) ... #7 84.74 Setting up libxkbfile1:amd64 (1:1.1.0-1) ... #7 84.76 Setting up libmpdec2:amd64 (2.4.2-3) ... #7 84.78 Setting up libxmlsec1:amd64 (1.2.28-2) ... #7 84.81 Setting up libtasn1-6-dev:amd64 (4.16.0-2) ... #7 84.83 Setting up libdrm-common (2.4.107-8ubuntu1~20.04.2) ... #7 84.85 Setting up libxcomposite1:amd64 (1:0.4.5-1) ... #7 84.90 Setting up readline-common (8.0-4) ... #7 84.93 Setting up libicu-dev:amd64 (66.1-2ubuntu2.1) ... #7 84.95 Setting up libxmuu1:amd64 (2:1.1.3-0ubuntu1) ... #7 84.98 Setting up xbitmaps (1.1.1-2) ... #7 85.00 Setting up libp11-kit-dev:amd64 (0.23.20-1ubuntu0.1) ... #7 85.02 Setting up libbz2-dev:amd64 (1.0.8-2) ... #7 85.04 Setting up libwayland-client0:amd64 (1.18.0-1ubuntu0.1) ... #7 85.06 Setting up x11proto-dev (2019.2-1ubuntu1) ... #7 85.08 Setting up libnspr4-dev (2:4.35-0ubuntu0.20.04.1) ... #7 85.11 Setting up libz3-dev:amd64 (4.8.7-4build1) ... #7 85.13 Setting up libice6:amd64 (2:1.0.10-0ubuntu1) ... #7 85.15 Setting up tcl8.6 (8.6.10+dfsg-1) ... #7 85.18 Setting up libxft2:amd64 (2.3.3-0ubuntu1) ... #7 85.20 Setting up libgmp-dev:amd64 (2:6.2.0+dfsg-4ubuntu0.1) ... #7 85.22 Setting up libxau-dev:amd64 (1:1.0.9-0ubuntu1) ... #7 85.24 Setting up libxi6:amd64 (2:1.7.10-0ubuntu1) ... #7 85.27 Setting up nettle-dev:amd64 (3.5.1+really3.5.1-2ubuntu0.2) ... #7 85.29 Setting up libice-dev:amd64 (2:1.0.10-0ubuntu1) ... #7 85.31 Setting up libxmlsec1-openssl:amd64 (1.2.28-2) ... #7 85.34 Setting up libxtst6:amd64 (2:1.2.3-1) ... #7 85.36 Setting up tcl8.6-dev:amd64 (8.6.10+dfsg-1) ... #7 85.38 Setting up libtk8.6:amd64 (8.6.10-1) ... #7 85.40 Setting up libreadline8:amd64 (8.0-4) ... #7 85.42 Setting up libpng-dev:amd64 (1.6.37-2) ... #7 85.45 Setting up libxxf86dga1:amd64 (2:1.1.5-0ubuntu1) ... #7 85.47 Setting up libnss3:amd64 (2:3.98-0ubuntu0.20.04.2) ... #7 85.49 Setting up libreadline-dev:amd64 (8.0-4) ... #7 85.51 Setting up libclang-cpp10 (1:10.0.0-4ubuntu1) ... #7 85.54 Setting up llvm-10-runtime (1:10.0.0-4ubuntu1) ... #7 85.56 Setting up libfreetype-dev:amd64 (2.10.1-2ubuntu0.3) ... #7 85.59 Setting up libxdmcp-dev:amd64 (1:1.1.3-0ubuntu1) ... #7 85.61 Setting up libxml2-dev:amd64 (2.9.10+dfsg-5ubuntu0.20.04.7) ... #7 85.65 Setting up libunbound8:amd64 (1.9.4-2ubuntu1.5) ... #7 85.67 Setting up libgcrypt20-dev (1.8.5-5ubuntu1.1) ... #7 85.69 Setting up x11proto-core-dev (2019.2-1ubuntu1) ... #7 85.71 Setting up llvm-runtime (1:10.0-50~exp1) ... #7 85.74 Setting up libxmlsec1-gcrypt:amd64 (1.2.28-2) ... #7 85.76 Setting up libxmlsec1-nss:amd64 (1.2.28-2) ... #7 85.78 Setting up x11proto-xext-dev (2019.2-1ubuntu1) ... #7 85.80 Setting up libdrm2:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 85.82 Setting up libnss3-dev:amd64 (2:3.98-0ubuntu0.20.04.2) ... #7 85.85 Setting up tcl (8.6.9+1) ... #7 85.87 Setting up libpython3.8-stdlib:amd64 (3.8.10-0ubuntu1~20.04.9) ... #7 85.89 Setting up python3.8 (3.8.10-0ubuntu1~20.04.9) ... #7 86.58 Setting up libxmlsec1-gnutls:amd64 (1.2.28-2) ... #7 86.61 Setting up x11proto-scrnsaver-dev (2019.2-1ubuntu1) ... #7 86.63 Setting up libsm6:amd64 (2:1.2.3-1) ... #7 86.65 Setting up libpython3-stdlib:amd64 (3.8.2-0ubuntu2) ... #7 86.67 Setting up libdrm-amdgpu1:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.69 Setting up tk8.6 (8.6.10-1) ... #7 86.72 Setting up mesa-vulkan-drivers:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 86.75 Setting up libgnutls-dane0:amd64 (3.6.13-2ubuntu1.11) ... #7 86.77 Setting up libdrm-nouveau2:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.79 Setting up libxcb1-dev:amd64 (1.14-2) ... #7 86.81 Setting up libxslt1-dev:amd64 (1.1.34-4ubuntu0.20.04.1) ... #7 86.84 Setting up libsm-dev:amd64 (2:1.2.3-1) ... #7 86.86 Setting up libdrm-radeon1:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.88 Setting up llvm-10 (1:10.0.0-4ubuntu1) ... #7 86.90 Setting up libdrm-intel1:amd64 (2.4.107-8ubuntu1~20.04.2) ... #7 86.93 Setting up libgl1-mesa-dri:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 86.96 Setting up libx11-dev:amd64 (2:1.6.9-2ubuntu1.6) ... #7 86.99 Setting up python3 (3.8.2-0ubuntu2) ... #7 87.18 Setting up libfreetype6-dev:amd64 (2.10.1-2ubuntu0.3) ... #7 87.21 Setting up python3-pygments (2.3.1+dfsg-1ubuntu2.2) ... #7 87.83 Setting up llvm (1:10.0-50~exp1) ... #7 87.85 Setting up libxt6:amd64 (1:1.1.5-1) ... #7 87.87 Setting up tcl-dev:amd64 (8.6.9+1) ... #7 87.90 Setting up libxext-dev:amd64 (2:1.3.4-0ubuntu1) ... #7 87.92 Setting up tk (8.6.9+1) ... #7 87.94 Setting up libxrender-dev:amd64 (1:0.9.10-1) ... #7 87.96 Setting up python3-pkg-resources (45.2.0-1ubuntu0.1) ... #7 88.28 Setting up libgnutls28-dev:amd64 (3.6.13-2ubuntu1.11) ... #7 88.31 Setting up libxmu6:amd64 (2:1.1.3-0ubuntu1) ... #7 88.33 Setting up libfontconfig1-dev:amd64 (2.13.1-2ubuntu3) ... #7 88.35 Setting up libglx-mesa0:amd64 (21.2.6-0ubuntu0.1~20.04.2) ... #7 88.37 Setting up libglx0:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 88.40 Setting up python3-yaml (5.3.1-1ubuntu0.1) ... #7 88.69 Setting up libxaw7:amd64 (2:1.0.13-1) ... #7 88.73 Setting up libxss-dev:amd64 (1:1.2.3-1) ... #7 88.75 Setting up libgl1:amd64 (1.3.2-1~ubuntu0.20.04.2) ... #7 88.77 Setting up libxt-dev:amd64 (1:1.1.5-1) ... #7 88.79 Setting up libxmlsec1-dev (1.2.28-2) ... #7 88.82 Setting up x11-utils (7.7+5) ... #7 88.93 Setting up xterm (353-1ubuntu1.20.04.2) ... #7 89.00 update-alternatives: using /usr/bin/xterm to provide /usr/bin/x-terminal-emulator (x-terminal-emulator) in auto mode #7 89.00 update-alternatives: warning: skip creation of /usr/share/man/man1/x-terminal-emulator.1.gz because associated file /usr/share/man/man1/xterm.1.gz (of link group x-terminal-emulator) doesn't exist #7 89.02 update-alternatives: using /usr/bin/lxterm to provide /usr/bin/x-terminal-emulator (x-terminal-emulator) in auto mode #7 89.02 update-alternatives: warning: skip creation of /usr/share/man/man1/x-terminal-emulator.1.gz because associated file /usr/share/man/man1/lxterm.1.gz (of link group x-terminal-emulator) doesn't exist #7 89.04 Setting up libxft-dev:amd64 (2.3.3-0ubuntu1) ... #7 89.06 Setting up llvm-10-tools (1:10.0.0-4ubuntu1) ... #7 89.08 Setting up llvm-10-dev (1:10.0.0-4ubuntu1) ... #7 89.10 Setting up tk8.6-dev:amd64 (8.6.10-1) ... #7 89.12 Setting up tk-dev:amd64 (8.6.9+1) ... #7 89.14 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #7 DONE 90.5s #8 [ 4/17] RUN git clone --depth 1 --branch $(git ls-remote --tags --sort=v:refname https://github.com/pyenv/pyenv.git | grep -o -E 'v[1-9]+(\\.[1-9]+)+$' | tail -1) https://github.com/pyenv/pyenv.git /root/.pyenv #8 0.857 Cloning into '/root/.pyenv'... #8 1.882 Note: switching to 'aabaa892438d05d96775042a129b1580fcd60105'. #8 1.882 #8 1.882 You are in 'detached HEAD' state. You can look around, make experimental #8 1.882 changes and commit them, and you can discard any commits you make in this #8 1.882 state without impacting any branches by switching back to a branch. #8 1.882 #8 1.882 If you want to create a new branch to retain commits you create, you may #8 1.882 do so (now or later) by using -c with the switch command. Example: #8 1.882 #8 1.882 git switch -c #8 1.882 #8 1.882 Or undo this operation with: #8 1.882 #8 1.882 git switch - #8 1.882 #8 1.882 Turn off this advice by setting config variable advice.detachedHead to false #8 1.882 #8 DONE 2.0s #9 [ 5/17] RUN apt install -y python3.8 python3.8-distutils && ln -s -f $(which python3.8) /usr/bin/python && wget https://bootstrap.pypa.io/get-pip.py -O /tmp/get-pip.py && python /tmp/get-pip.py #9 0.502 #9 0.502 WARNING: apt does not have a stable CLI interface. Use with caution in scripts. #9 0.502 #9 0.563 Reading package lists... #9 1.644 Building dependency tree... #9 1.808 Reading state information... #9 2.060 python3.8 is already the newest version (3.8.10-0ubuntu1~20.04.9). #9 2.060 python3.8 set to manually installed. #9 2.060 The following NEW packages will be installed: #9 2.060 python3-distutils python3-lib2to3 #9 2.331 0 upgraded, 2 newly installed, 0 to remove and 0 not upgraded. #9 2.331 Need to get 218 kB of archives. #9 2.331 After this operation, 2115 kB of additional disk space will be used. #9 2.331 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-lib2to3 all 3.8.10-0ubuntu1~20.04 [76.3 kB] #9 2.755 Get:2 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-distutils all 3.8.10-0ubuntu1~20.04 [141 kB] #9 3.105 debconf: delaying package configuration, since apt-utils is not installed #9 3.173 Fetched 218 kB in 1s (254 kB/s) #9 3.227 Selecting previously unselected package python3-lib2to3. #9 3.227 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 24296 files and directories currently installed.) #9 3.256 Preparing to unpack .../python3-lib2to3_3.8.10-0ubuntu1~20.04_all.deb ... #9 3.263 Unpacking python3-lib2to3 (3.8.10-0ubuntu1~20.04) ... #9 3.357 Selecting previously unselected package python3-distutils. #9 3.358 Preparing to unpack .../python3-distutils_3.8.10-0ubuntu1~20.04_all.deb ... #9 3.365 Unpacking python3-distutils (3.8.10-0ubuntu1~20.04) ... #9 3.486 Setting up python3-lib2to3 (3.8.10-0ubuntu1~20.04) ... #9 3.636 Setting up python3-distutils (3.8.10-0ubuntu1~20.04) ... #9 3.862 --2024-05-16 20:23:48-- https://bootstrap.pypa.io/get-pip.py #9 3.866 Resolving bootstrap.pypa.io (bootstrap.pypa.io)... 151.101.64.175, 151.101.128.175, 151.101.0.175, ... #9 3.930 Connecting to bootstrap.pypa.io (bootstrap.pypa.io)|151.101.64.175|:443... connected. #9 4.180 HTTP request sent, awaiting response... 200 OK #9 4.192 Length: 2635835 (2.5M) [text/x-python] #9 4.192 Saving to: '/tmp/get-pip.py' #9 4.206 #9 4.206 0K .......... .......... .......... .......... .......... 1% 968K 3s #9 4.244 50K .......... .......... .......... .......... .......... 3% 4.78M 2s #9 4.254 100K .......... .......... .......... .......... .......... 5% 7.08M 1s #9 4.261 150K .......... .......... .......... .......... .......... 7% 7.48M 1s #9 4.267 200K .......... .......... .......... .......... .......... 9% 6.58M 1s #9 4.275 250K .......... .......... .......... .......... .......... 11% 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Collecting setuptools #9 6.909 Downloading setuptools-69.5.1-py3-none-any.whl.metadata (6.2 kB) #9 6.992 Collecting wheel #9 7.007 Downloading wheel-0.43.0-py3-none-any.whl.metadata (2.2 kB) #9 7.077 Downloading pip-24.0-py3-none-any.whl (2.1 MB) #9 7.224 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 2.1/2.1 MB 15.0 MB/s eta 0:00:00 #9 7.243 Downloading setuptools-69.5.1-py3-none-any.whl (894 kB) #9 7.297 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 894.6/894.6 kB 18.5 MB/s eta 0:00:00 #9 7.315 Downloading wheel-0.43.0-py3-none-any.whl (65 kB) #9 7.331 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 65.8/65.8 kB 4.8 MB/s eta 0:00:00 #9 7.407 Installing collected packages: wheel, setuptools, pip #9 9.087 Successfully installed pip-24.0 setuptools-69.5.1 wheel-0.43.0 #9 9.088 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #9 DONE 9.6s #10 [ 6/17] RUN pip install virtualenv #10 1.047 Collecting virtualenv #10 1.095 Downloading virtualenv-20.26.2-py3-none-any.whl.metadata (4.4 kB) #10 1.182 Collecting distlib < 1,>=0.3.7 (from virtualenv) #10 1.197 Downloading distlib-0.3.8-py2.py3-none-any.whl.metadata (5.1 kB) #10 1.276 Collecting filelock < 4,>=3.12.2 (from virtualenv) #10 1.291 Downloading filelock-3.14.0-py3-none-any.whl.metadata (2.8 kB) #10 1.368 Collecting platformdirs < 5,>=3.9.1 (from virtualenv) #10 1.383 Downloading platformdirs-4.2.2-py3-none-any.whl.metadata (11 kB) #10 1.439 Downloading virtualenv-20.26.2-py3-none-any.whl (3.9 MB) #10 1.656 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.9/3.9 MB 18.3 MB/s eta 0:00:00 #10 1.673 Downloading distlib-0.3.8-py2.py3-none-any.whl (468 kB) #10 1.706 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 468.9/468.9 kB 17.0 MB/s eta 0:00:00 #10 1.724 Downloading filelock-3.14.0-py3-none-any.whl (12 kB) #10 1.752 Downloading platformdirs-4.2.2-py3-none-any.whl (18 kB) #10 1.871 Installing collected packages: distlib, platformdirs, filelock, virtualenv #10 2.123 Successfully installed distlib-0.3.8 filelock-3.14.0 platformdirs-4.2.2 virtualenv-20.26.2 #10 2.123 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #10 DONE 2.3s #11 [ 7/17] RUN apt-get install -y --no-install-recommends openjdk-11-jdk maven #11 0.605 Reading package lists... #11 1.699 Building dependency tree... #11 1.870 Reading state information... #11 2.070 The following additional packages will be installed: #11 2.070 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.070 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.070 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.070 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.070 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.070 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.070 libguice-java libharfbuzz0b libhawtjni-runtime-java libjansi-java #11 2.070 libjansi-native-java libjsr305-java liblcms2-2 libmaven-parent-java #11 2.070 libmaven-resolver-java libmaven-shared-utils-java libmaven3-core-java #11 2.070 libpcsclite1 libplexus-cipher-java libplexus-classworlds-java #11 2.070 libplexus-component-annotations-java libplexus-interpolation-java #11 2.070 libplexus-sec-dispatcher-java libplexus-utils2-java libsisu-inject-java #11 2.070 libsisu-plexus-java libslf4j-java libwagon-file-java #11 2.071 libwagon-http-shaded-java libwagon-provider-api-java openjdk-11-jdk-headless #11 2.071 openjdk-11-jre openjdk-11-jre-headless #11 2.072 Suggested packages: #11 2.072 default-jre libaopalliance-java-doc libasound2-plugins alsa-utils #11 2.072 libatinject-jsr330-api-java-doc libservlet3.1-java libcommons-io-java-doc #11 2.072 libcommons-lang3-java-doc cups-common libasm-java libcglib-java #11 2.072 libjsr305-java-doc liblcms2-utils libmaven-shared-utils-java-doc #11 2.072 liblogback-java pcscd libplexus-cipher-java-doc #11 2.072 libplexus-classworlds-java-doc libplexus-sec-dispatcher-java-doc #11 2.072 libplexus-utils2-java-doc junit4 testng libcommons-logging-java #11 2.072 liblog4j1.2-java openjdk-11-demo openjdk-11-source visualvm libnss-mdns #11 2.072 fonts-dejavu-extra fonts-ipafont-gothic fonts-ipafont-mincho #11 2.072 fonts-wqy-microhei | fonts-wqy-zenhei fonts-indic #11 2.072 Recommended packages: #11 2.072 alsa-ucm-conf alsa-topology-conf dbus libatk-wrapper-java-jni #11 2.072 fonts-dejavu-extra #11 2.267 The following NEW packages will be installed: #11 2.268 ca-certificates-java java-common libaopalliance-java libapache-pom-java #11 2.268 libasound2 libasound2-data libatinject-jsr330-api-java libavahi-client3 #11 2.268 libavahi-common-data libavahi-common3 libcdi-api-java libcommons-cli-java #11 2.268 libcommons-io-java libcommons-lang3-java libcommons-parent-java libcups2 #11 2.268 libdbus-1-3 libgeronimo-annotation-1.3-spec-java #11 2.268 libgeronimo-interceptor-3.0-spec-java libgif7 libgraphite2-3 libguava-java #11 2.268 libguice-java libharfbuzz0b libhawtjni-runtime-java libjansi-java #11 2.268 libjansi-native-java libjsr305-java liblcms2-2 libmaven-parent-java #11 2.268 libmaven-resolver-java libmaven-shared-utils-java libmaven3-core-java #11 2.268 libpcsclite1 libplexus-cipher-java libplexus-classworlds-java #11 2.268 libplexus-component-annotations-java libplexus-interpolation-java #11 2.268 libplexus-sec-dispatcher-java libplexus-utils2-java libsisu-inject-java #11 2.268 libsisu-plexus-java libslf4j-java libwagon-file-java #11 2.269 libwagon-http-shaded-java libwagon-provider-api-java maven openjdk-11-jdk #11 2.269 openjdk-11-jdk-headless openjdk-11-jre openjdk-11-jre-headless #11 2.386 0 upgraded, 51 newly installed, 0 to remove and 0 not upgraded. #11 2.386 Need to get 125 MB of archives. #11 2.386 After this operation, 277 MB of additional disk space will be used. #11 2.386 Get:1 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdbus-1-3 amd64 1.12.16-2ubuntu2.3 [179 kB] #11 2.602 Get:2 http://archive.ubuntu.com/ubuntu focal/main amd64 java-common all 0.72 [6816 B] #11 2.603 Get:3 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libavahi-common-data amd64 0.7-4ubuntu7.3 [21.4 kB] #11 2.607 Get:4 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libavahi-common3 amd64 0.7-4ubuntu7.3 [21.9 kB] #11 2.610 Get:5 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libavahi-client3 amd64 0.7-4ubuntu7.3 [25.5 kB] #11 2.615 Get:6 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libcups2 amd64 2.3.1-9ubuntu1.6 [233 kB] #11 2.667 Get:7 http://archive.ubuntu.com/ubuntu focal/main amd64 liblcms2-2 amd64 2.9-4 [140 kB] #11 2.690 Get:8 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libasound2-data all 1.2.2-2.1ubuntu2.5 [20.1 kB] #11 2.694 Get:9 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libasound2 amd64 1.2.2-2.1ubuntu2.5 [335 kB] #11 2.753 Get:10 http://archive.ubuntu.com/ubuntu focal/main amd64 libgraphite2-3 amd64 1.3.13-11build1 [73.5 kB] #11 2.765 Get:11 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libharfbuzz0b amd64 2.6.4-1ubuntu4.2 [391 kB] #11 2.928 Get:12 http://archive.ubuntu.com/ubuntu focal/main amd64 libpcsclite1 amd64 1.8.26-3 [22.0 kB] #11 3.024 Get:13 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jre-headless amd64 11.0.22+7-0ubuntu2~20.04.1 [38.3 MB] #11 10.09 Get:14 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 ca-certificates-java all 20190405ubuntu1.1 [12.4 kB] #11 10.10 Get:15 http://archive.ubuntu.com/ubuntu focal/universe amd64 libapache-pom-java all 18-1 [4720 B] #11 10.10 Get:16 http://archive.ubuntu.com/ubuntu focal/universe amd64 libatinject-jsr330-api-java all 1.0+ds1-5 [5348 B] #11 10.10 Get:17 http://archive.ubuntu.com/ubuntu focal/universe amd64 libgeronimo-interceptor-3.0-spec-java all 1.0.1-4fakesync [8616 B] #11 10.10 Get:18 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcdi-api-java all 1.2-2 [54.5 kB] #11 10.10 Get:19 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcommons-cli-java all 1.4-1 [53.8 kB] #11 10.10 Get:20 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcommons-parent-java all 43-1 [10.8 kB] #11 10.10 Get:21 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libcommons-io-java all 2.6-2ubuntu0.20.04.1 [198 kB] #11 10.11 Get:22 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcommons-lang3-java all 3.8-2 [458 kB] #11 10.28 Get:23 http://archive.ubuntu.com/ubuntu focal/universe amd64 libgeronimo-annotation-1.3-spec-java all 1.0-1 [10.7 kB] #11 10.33 Get:24 http://archive.ubuntu.com/ubuntu focal/main amd64 libgif7 amd64 5.1.9-1 [32.2 kB] #11 10.38 Get:25 http://archive.ubuntu.com/ubuntu focal/universe amd64 libjsr305-java all 0.1~+svn49-11 [27.0 kB] #11 10.41 Get:26 http://archive.ubuntu.com/ubuntu focal/universe amd64 libguava-java all 19.0-1 [2028 kB] #11 10.83 Get:27 http://archive.ubuntu.com/ubuntu focal/universe amd64 libaopalliance-java all 20070526-6 [9084 B] #11 10.83 Get:28 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libguice-java all 4.2.1-1ubuntu0.2 [1410 kB] #11 11.08 Get:29 http://archive.ubuntu.com/ubuntu focal/universe amd64 libhawtjni-runtime-java all 1.17-1 [28.8 kB] #11 11.08 Get:30 http://archive.ubuntu.com/ubuntu focal/universe amd64 libjansi-native-java all 1.8-1 [23.8 kB] #11 11.08 Get:31 http://archive.ubuntu.com/ubuntu focal/universe amd64 libjansi-java all 1.18-1 [56.8 kB] #11 11.09 Get:32 http://archive.ubuntu.com/ubuntu focal/universe amd64 libmaven-parent-java all 31-2 [5140 B] #11 11.09 Get:33 http://archive.ubuntu.com/ubuntu focal/universe amd64 libplexus-utils2-java all 3.3.0-1 [250 kB] #11 11.23 Get:34 http://archive.ubuntu.com/ubuntu focal/universe amd64 libwagon-provider-api-java all 3.3.4-1 [48.5 kB] #11 11.36 Get:35 http://archive.ubuntu.com/ubuntu focal/universe amd64 libmaven-resolver-java all 1.4.1-1 [555 kB] #11 11.52 Get:36 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focal/universe amd64 libsisu-plexus-java all 0.3.3-3 [182 kB] #11 11.79 Get:45 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libmaven3-core-java all 3.6.3-1ubuntu0.1 [1535 kB] #11 12.26 Get:46 http://archive.ubuntu.com/ubuntu focal/universe amd64 libwagon-file-java all 3.3.4-1 [7928 B] #11 12.26 Get:47 http://archive.ubuntu.com/ubuntu focal/universe amd64 libwagon-http-shaded-java all 3.3.4-1 [1855 kB] #11 12.59 Get:48 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 maven all 3.6.3-1ubuntu0.1 [17.8 kB] #11 12.60 Get:49 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jre amd64 11.0.22+7-0ubuntu2~20.04.1 [194 kB] #11 12.63 Get:50 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jdk-headless amd64 11.0.22+7-0ubuntu2~20.04.1 [73.7 MB] #11 25.68 Get:51 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 openjdk-11-jdk amd64 11.0.22+7-0ubuntu2~20.04.1 [1338 kB] #11 26.04 debconf: delaying package configuration, since apt-utils is not installed #11 26.11 Fetched 125 MB in 24s (5286 kB/s) #11 26.18 Selecting previously unselected package libdbus-1-3:amd64. #11 26.18 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 24567 files and directories currently installed.) #11 26.21 Preparing to unpack .../00-libdbus-1-3_1.12.16-2ubuntu2.3_amd64.deb ... #11 26.22 Unpacking libdbus-1-3:amd64 (1.12.16-2ubuntu2.3) ... #11 26.33 Selecting previously unselected package java-common. #11 26.33 Preparing to unpack .../01-java-common_0.72_all.deb ... #11 26.34 Unpacking java-common (0.72) ... #11 26.40 Selecting previously unselected package libavahi-common-data:amd64. #11 26.41 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previously unselected package libasound2-data. #11 26.91 Preparing to unpack .../07-libasound2-data_1.2.2-2.1ubuntu2.5_all.deb ... #11 26.91 Unpacking libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 27.01 Selecting previously unselected package libasound2:amd64. #11 27.01 Preparing to unpack .../08-libasound2_1.2.2-2.1ubuntu2.5_amd64.deb ... #11 27.02 Unpacking libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 27.14 Selecting previously unselected package libgraphite2-3:amd64. #11 27.14 Preparing to unpack .../09-libgraphite2-3_1.3.13-11build1_amd64.deb ... #11 27.15 Unpacking libgraphite2-3:amd64 (1.3.13-11build1) ... #11 27.24 Selecting previously unselected package libharfbuzz0b:amd64. #11 27.24 Preparing to unpack .../10-libharfbuzz0b_2.6.4-1ubuntu4.2_amd64.deb ... #11 27.25 Unpacking libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 27.37 Selecting previously unselected package libpcsclite1:amd64. #11 27.38 Preparing to unpack .../11-libpcsclite1_1.8.26-3_amd64.deb ... #11 27.38 Unpacking libpcsclite1:amd64 (1.8.26-3) ... #11 27.47 Selecting previously unselected package openjdk-11-jre-headless:amd64. #11 27.47 Preparing to unpack .../12-openjdk-11-jre-headless_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 27.48 Unpacking openjdk-11-jre-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 31.00 Selecting previously unselected package ca-certificates-java. #11 31.00 Preparing to unpack .../13-ca-certificates-java_20190405ubuntu1.1_all.deb ... #11 31.02 Unpacking ca-certificates-java (20190405ubuntu1.1) ... #11 31.08 Selecting previously unselected package libapache-pom-java. #11 31.09 Preparing to unpack .../14-libapache-pom-java_18-1_all.deb ... #11 31.10 Unpacking libapache-pom-java (18-1) ... #11 31.16 Selecting previously unselected package libatinject-jsr330-api-java. #11 31.16 Preparing to unpack .../15-libatinject-jsr330-api-java_1.0+ds1-5_all.deb ... #11 31.17 Unpacking libatinject-jsr330-api-java (1.0+ds1-5) ... #11 31.23 Selecting previously unselected package libgeronimo-interceptor-3.0-spec-java. #11 31.24 Preparing to unpack .../16-libgeronimo-interceptor-3.0-spec-java_1.0.1-4fakesync_all.deb ... #11 31.25 Unpacking libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 31.31 Selecting previously unselected package libcdi-api-java. #11 31.31 Preparing to unpack .../17-libcdi-api-java_1.2-2_all.deb ... #11 31.32 Unpacking libcdi-api-java (1.2-2) ... #11 31.39 Selecting previously unselected package libcommons-cli-java. #11 31.39 Preparing to unpack .../18-libcommons-cli-java_1.4-1_all.deb ... #11 31.40 Unpacking libcommons-cli-java (1.4-1) ... #11 31.47 Selecting previously unselected package libcommons-parent-java. #11 31.48 Preparing to unpack .../19-libcommons-parent-java_43-1_all.deb ... #11 31.49 Unpacking libcommons-parent-java (43-1) ... #11 31.55 Selecting previously unselected package libcommons-io-java. #11 31.56 Preparing to unpack .../20-libcommons-io-java_2.6-2ubuntu0.20.04.1_all.deb ... #11 31.56 Unpacking libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 31.66 Selecting previously unselected package libcommons-lang3-java. #11 31.66 Preparing to unpack .../21-libcommons-lang3-java_3.8-2_all.deb ... #11 31.67 Unpacking libcommons-lang3-java (3.8-2) ... #11 31.78 Selecting previously unselected package libgeronimo-annotation-1.3-spec-java. #11 31.78 Preparing to unpack .../22-libgeronimo-annotation-1.3-spec-java_1.0-1_all.deb ... #11 31.79 Unpacking libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 31.86 Selecting previously unselected package libgif7:amd64. #11 31.87 Preparing to unpack .../23-libgif7_5.1.9-1_amd64.deb ... #11 31.88 Unpacking libgif7:amd64 (5.1.9-1) ... #11 31.95 Selecting previously unselected package libjsr305-java. #11 31.95 Preparing to unpack .../24-libjsr305-java_0.1~+svn49-11_all.deb ... #11 31.96 Unpacking libjsr305-java (0.1~+svn49-11) ... #11 32.03 Selecting previously unselected package libguava-java. #11 32.03 Preparing to unpack .../25-libguava-java_19.0-1_all.deb ... #11 32.04 Unpacking libguava-java (19.0-1) ... #11 32.29 Selecting previously unselected package libaopalliance-java. #11 32.29 Preparing to unpack .../26-libaopalliance-java_20070526-6_all.deb ... #11 32.30 Unpacking libaopalliance-java (20070526-6) ... #11 32.36 Selecting previously unselected package libguice-java. #11 32.37 Preparing to unpack .../27-libguice-java_4.2.1-1ubuntu0.2_all.deb ... #11 32.37 Unpacking libguice-java (4.2.1-1ubuntu0.2) ... #11 32.59 Selecting previously unselected package libhawtjni-runtime-java. #11 32.60 Preparing to unpack .../28-libhawtjni-runtime-java_1.17-1_all.deb ... #11 32.60 Unpacking libhawtjni-runtime-java (1.17-1) ... #11 32.67 Selecting previously unselected package libjansi-native-java. #11 32.68 Preparing to unpack .../29-libjansi-native-java_1.8-1_all.deb ... #11 32.68 Unpacking libjansi-native-java (1.8-1) ... #11 32.75 Selecting previously unselected package libjansi-java. #11 32.75 Preparing to unpack .../30-libjansi-java_1.18-1_all.deb ... #11 32.76 Unpacking libjansi-java (1.18-1) ... #11 32.83 Selecting previously unselected package libmaven-parent-java. #11 32.84 Preparing to unpack .../31-libmaven-parent-java_31-2_all.deb ... #11 32.85 Unpacking libmaven-parent-java (31-2) ... #11 32.91 Selecting previously unselected package libplexus-utils2-java. #11 32.91 Preparing to unpack .../32-libplexus-utils2-java_3.3.0-1_all.deb ... #11 32.92 Unpacking libplexus-utils2-java (3.3.0-1) ... #11 33.02 Selecting previously unselected package libwagon-provider-api-java. #11 33.02 Preparing to unpack .../33-libwagon-provider-api-java_3.3.4-1_all.deb ... #11 33.03 Unpacking libwagon-provider-api-java (3.3.4-1) ... #11 33.10 Selecting previously unselected package libmaven-resolver-java. #11 33.11 Preparing to unpack .../34-libmaven-resolver-java_1.4.1-1_all.deb ... #11 33.12 Unpacking libmaven-resolver-java (1.4.1-1) ... #11 33.26 Selecting previously unselected package libmaven-shared-utils-java. #11 33.27 Preparing to unpack .../35-libmaven-shared-utils-java_3.3.0-1ubuntu0.20.04.1_all.deb ... #11 33.27 Unpacking libmaven-shared-utils-java (3.3.0-1ubuntu0.20.04.1) ... #11 33.36 Selecting previously unselected package libplexus-cipher-java. #11 33.37 Preparing to unpack .../36-libplexus-cipher-java_1.7-3_all.deb ... #11 33.37 Unpacking libplexus-cipher-java (1.7-3) ... #11 33.44 Selecting previously unselected package libplexus-classworlds-java. #11 33.45 Preparing to unpack .../37-libplexus-classworlds-java_2.6.0-1_all.deb ... #11 33.45 Unpacking libplexus-classworlds-java (2.6.0-1) ... #11 33.53 Selecting previously unselected package libplexus-component-annotations-java. #11 33.53 Preparing to unpack .../38-libplexus-component-annotations-java_2.1.0-1_all.deb ... #11 33.54 Unpacking libplexus-component-annotations-java (2.1.0-1) ... #11 33.60 Selecting previously unselected package libplexus-interpolation-java. #11 33.60 Preparing to unpack .../39-libplexus-interpolation-java_1.26-1_all.deb ... #11 33.61 Unpacking libplexus-interpolation-java (1.26-1) ... #11 33.69 Selecting previously unselected package libplexus-sec-dispatcher-java. #11 33.70 Preparing to unpack .../40-libplexus-sec-dispatcher-java_1.4-4_all.deb ... #11 33.70 Unpacking libplexus-sec-dispatcher-java (1.4-4) ... #11 33.77 Selecting previously unselected package libslf4j-java. #11 33.77 Preparing to unpack .../41-libslf4j-java_1.7.25-3_all.deb ... #11 33.78 Unpacking libslf4j-java (1.7.25-3) ... #11 33.89 Selecting previously unselected package libsisu-inject-java. #11 33.89 Preparing to unpack .../42-libsisu-inject-java_0.3.3-1_all.deb ... #11 33.90 Unpacking libsisu-inject-java (0.3.3-1) ... #11 34.01 Selecting previously unselected package libsisu-plexus-java. #11 34.02 Preparing to unpack .../43-libsisu-plexus-java_0.3.3-3_all.deb ... #11 34.02 Unpacking libsisu-plexus-java (0.3.3-3) ... #11 34.12 Selecting previously unselected package 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(11.0.22+7-0ubuntu2~20.04.1) ... #11 34.88 Selecting previously unselected package openjdk-11-jdk-headless:amd64. #11 34.89 Preparing to unpack .../49-openjdk-11-jdk-headless_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 34.89 Unpacking openjdk-11-jdk-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 39.97 Selecting previously unselected package openjdk-11-jdk:amd64. #11 39.97 Preparing to unpack .../50-openjdk-11-jdk_11.0.22+7-0ubuntu2~20.04.1_amd64.deb ... #11 39.98 Unpacking openjdk-11-jdk:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 40.14 Setting up libgraphite2-3:amd64 (1.3.13-11build1) ... #11 40.16 Setting up liblcms2-2:amd64 (2.9-4) ... #11 40.18 Setting up libslf4j-java (1.7.25-3) ... #11 40.20 Setting up libplexus-utils2-java (3.3.0-1) ... #11 40.22 Setting up libplexus-classworlds-java (2.6.0-1) ... #11 40.24 Setting up libjsr305-java (0.1~+svn49-11) ... #11 40.26 Setting up java-common (0.72) ... #11 40.30 Setting up libaopalliance-java (20070526-6) ... #11 40.32 Setting up libcommons-cli-java (1.4-1) ... #11 40.34 Setting up libplexus-component-annotations-java (2.1.0-1) ... #11 40.38 Setting up libplexus-cipher-java (1.7-3) ... #11 40.40 Setting up libgeronimo-annotation-1.3-spec-java (1.0-1) ... #11 40.42 Setting up libgeronimo-interceptor-3.0-spec-java (1.0.1-4fakesync) ... #11 40.44 Setting up libasound2-data (1.2.2-2.1ubuntu2.5) ... #11 40.46 Setting up libapache-pom-java (18-1) ... #11 40.48 Setting up libavahi-common-data:amd64 (0.7-4ubuntu7.3) ... #11 40.50 Setting up libatinject-jsr330-api-java (1.0+ds1-5) ... #11 40.53 Setting up libdbus-1-3:amd64 (1.12.16-2ubuntu2.3) ... #11 40.55 Setting up libplexus-interpolation-java (1.26-1) ... #11 40.57 Setting up libpcsclite1:amd64 (1.8.26-3) ... #11 40.59 Setting up libgif7:amd64 (5.1.9-1) ... #11 40.61 Setting up libasound2:amd64 (1.2.2-2.1ubuntu2.5) ... #11 40.63 Setting up libplexus-sec-dispatcher-java (1.4-4) ... #11 40.65 Setting up libharfbuzz0b:amd64 (2.6.4-1ubuntu4.2) ... #11 40.67 Setting up libwagon-http-shaded-java (3.3.4-1) ... #11 40.69 Setting up libcdi-api-java (1.2-2) ... #11 40.71 Setting up libhawtjni-runtime-java (1.17-1) ... #11 40.73 Setting up libwagon-provider-api-java (3.3.4-1) ... #11 40.75 Setting up libmaven-parent-java (31-2) ... #11 40.77 Setting up libcommons-parent-java (43-1) ... #11 40.79 Setting up libavahi-common3:amd64 (0.7-4ubuntu7.3) ... #11 40.81 Setting up libmaven-resolver-java (1.4.1-1) ... #11 40.84 Setting up libguava-java (19.0-1) ... #11 40.86 Setting up libcommons-lang3-java (3.8-2) ... #11 40.88 Setting up libjansi-native-java (1.8-1) ... #11 40.90 Setting up libwagon-file-java (3.3.4-1) ... #11 40.92 Setting up libavahi-client3:amd64 (0.7-4ubuntu7.3) ... #11 40.94 Setting up libcommons-io-java (2.6-2ubuntu0.20.04.1) ... #11 40.96 Setting up libguice-java (4.2.1-1ubuntu0.2) ... #11 40.98 Setting up libjansi-java (1.18-1) ... #11 41.00 Setting up libcups2:amd64 (2.3.1-9ubuntu1.6) ... #11 41.02 Setting up libmaven-shared-utils-java (3.3.0-1ubuntu0.20.04.1) ... #11 41.04 Setting up libsisu-inject-java (0.3.3-1) ... #11 41.06 Setting up libsisu-plexus-java (0.3.3-3) ... #11 41.08 Setting up libmaven3-core-java (3.6.3-1ubuntu0.1) ... #11 41.11 Setting up openjdk-11-jre-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.31 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/java to provide /usr/bin/java (java) in auto mode #11 41.32 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jjs to provide /usr/bin/jjs (jjs) in auto mode #11 41.33 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/keytool to provide /usr/bin/keytool (keytool) in auto mode #11 41.34 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmid to provide /usr/bin/rmid (rmid) in auto mode #11 41.34 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmiregistry to provide /usr/bin/rmiregistry (rmiregistry) in auto mode #11 41.35 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/pack200 to provide /usr/bin/pack200 (pack200) in auto mode #11 41.36 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/unpack200 to provide /usr/bin/unpack200 (unpack200) in auto mode #11 41.37 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/lib/jexec to provide /usr/bin/jexec (jexec) in auto mode #11 41.92 Setting up openjdk-11-jre:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.95 Setting up openjdk-11-jdk-headless:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 41.98 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jar to provide /usr/bin/jar (jar) in auto mode #11 41.99 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jarsigner to provide /usr/bin/jarsigner (jarsigner) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javac to provide /usr/bin/javac (javac) in auto mode #11 42.00 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javadoc to provide /usr/bin/javadoc (javadoc) in auto mode #11 42.01 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/javap to provide /usr/bin/javap (javap) in auto mode #11 42.02 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jcmd to provide /usr/bin/jcmd (jcmd) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdb to provide /usr/bin/jdb (jdb) in auto mode #11 42.03 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeprscan to provide /usr/bin/jdeprscan (jdeprscan) in auto mode #11 42.04 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jdeps to provide /usr/bin/jdeps (jdeps) in auto mode #11 42.05 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jfr to provide /usr/bin/jfr (jfr) in auto mode #11 42.06 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jimage to provide /usr/bin/jimage (jimage) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jinfo to provide /usr/bin/jinfo (jinfo) in auto mode #11 42.07 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jlink to provide /usr/bin/jlink (jlink) in auto mode #11 42.08 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmap to provide /usr/bin/jmap (jmap) in auto mode #11 42.09 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jmod to provide /usr/bin/jmod (jmod) in auto mode #11 42.10 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jps to provide /usr/bin/jps (jps) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jrunscript to provide /usr/bin/jrunscript (jrunscript) in auto mode #11 42.11 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jshell to provide /usr/bin/jshell (jshell) in auto mode #11 42.12 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstack to provide /usr/bin/jstack (jstack) in auto mode #11 42.13 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstat to provide /usr/bin/jstat (jstat) in auto mode #11 42.14 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jstatd to provide /usr/bin/jstatd (jstatd) in auto mode #11 42.15 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmic to provide /usr/bin/rmic (rmic) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/serialver to provide /usr/bin/serialver (serialver) in auto mode #11 42.16 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jaotc to provide /usr/bin/jaotc (jaotc) in auto mode #11 42.17 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jhsdb to provide /usr/bin/jhsdb (jhsdb) in auto mode #11 42.19 Setting up maven (3.6.3-1ubuntu0.1) ... #11 42.23 update-alternatives: using /usr/share/maven/bin/mvn to provide /usr/bin/mvn (mvn) in auto mode #11 42.25 Setting up openjdk-11-jdk:amd64 (11.0.22+7-0ubuntu2~20.04.1) ... #11 42.28 update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/jconsole to provide /usr/bin/jconsole (jconsole) in auto mode #11 42.29 Setting up ca-certificates-java (20190405ubuntu1.1) ... #11 42.35 head: cannot open '/etc/ssl/certs/java/cacerts' for reading: No such file or directory #11 42.65 Adding debian:COMODO_RSA_Certification_Authority.pem #11 42.66 Adding debian:Hongkong_Post_Root_CA_1.pem #11 42.67 Adding debian:Entrust_Root_Certification_Authority_-_EC1.pem #11 42.67 Adding debian:Certainly_Root_R1.pem #11 42.68 Adding debian:Amazon_Root_CA_2.pem #11 42.68 Adding debian:Certainly_Root_E1.pem #11 42.68 Adding debian:AffirmTrust_Premium_ECC.pem #11 42.68 Adding debian:ANF_Secure_Server_Root_CA.pem #11 42.68 Adding debian:GlobalSign_Root_R46.pem #11 42.69 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068.pem #11 42.69 Adding debian:Certum_Trusted_Root_CA.pem #11 42.69 Adding debian:Go_Daddy_Root_Certificate_Authority_-_G2.pem #11 42.69 Adding debian:SecureSign_RootCA11.pem #11 42.69 Adding debian:Amazon_Root_CA_4.pem #11 42.70 Adding debian:GDCA_TrustAUTH_R5_ROOT.pem #11 42.70 Adding debian:T-TeleSec_GlobalRoot_Class_2.pem #11 42.70 Adding debian:Trustwave_Global_ECC_P256_Certification_Authority.pem #11 42.70 Adding debian:GTS_Root_R1.pem #11 42.71 Adding debian:Izenpe.com.pem #11 42.71 Adding debian:CFCA_EV_ROOT.pem #11 42.71 Adding debian:DigiCert_Global_Root_G2.pem #11 42.71 Adding debian:XRamp_Global_CA_Root.pem #11 42.72 Adding debian:SSL.com_EV_Root_Certification_Authority_RSA_R2.pem #11 42.72 Adding debian:Security_Communication_RootCA3.pem #11 42.72 Adding debian:Starfield_Services_Root_Certificate_Authority_-_G2.pem #11 42.72 Adding debian:Amazon_Root_CA_1.pem #11 42.72 Adding debian:DigiCert_Assured_ID_Root_CA.pem #11 42.72 Adding debian:SSL.com_Root_Certification_Authority_RSA.pem #11 42.73 Adding debian:Telia_Root_CA_v2.pem #11 42.73 Adding debian:SwissSign_Gold_CA_-_G2.pem #11 42.73 Adding debian:GLOBALTRUST_2020.pem #11 42.73 Adding debian:emSign_ECC_Root_CA_-_G3.pem #11 42.73 Adding debian:Microsoft_RSA_Root_Certificate_Authority_2017.pem #11 42.74 Adding debian:TunTrust_Root_CA.pem #11 42.74 Adding debian:Security_Communication_ECC_RootCA1.pem #11 42.74 Adding debian:DigiCert_TLS_ECC_P384_Root_G5.pem #11 42.74 Adding debian:vTrus_Root_CA.pem #11 42.75 Adding debian:Certum_EC-384_CA.pem #11 42.76 Adding debian:CA_Disig_Root_R2.pem #11 42.76 Adding debian:emSign_Root_CA_-_C1.pem #11 42.77 Adding debian:AffirmTrust_Commercial.pem #11 42.77 Adding debian:Entrust_Root_Certification_Authority_-_G4.pem #11 42.78 Adding debian:Starfield_Class_2_CA.pem #11 42.78 Adding debian:Baltimore_CyberTrust_Root.pem #11 42.79 Adding debian:GTS_Root_R4.pem #11 42.79 Adding debian:Starfield_Root_Certificate_Authority_-_G2.pem #11 42.80 Adding debian:QuoVadis_Root_CA_1_G3.pem #11 42.81 Adding debian:DigiCert_Trusted_Root_G4.pem #11 42.81 Adding debian:DigiCert_High_Assurance_EV_Root_CA.pem #11 42.82 Adding debian:AC_RAIZ_FNMT-RCM.pem #11 42.82 Adding debian:Comodo_AAA_Services_root.pem #11 42.82 Adding debian:emSign_ECC_Root_CA_-_C3.pem #11 42.83 Adding debian:Entrust_Root_Certification_Authority.pem #11 42.84 Adding debian:D-TRUST_EV_Root_CA_1_2020.pem #11 42.85 Adding debian:Certigna_Root_CA.pem #11 42.85 Adding debian:TeliaSonera_Root_CA_v1.pem #11 42.86 Adding debian:Certum_Trusted_Network_CA.pem #11 42.86 Adding debian:HiPKI_Root_CA_-_G1.pem #11 42.87 Adding debian:SZAFIR_ROOT_CA2.pem #11 42.87 Adding debian:USERTrust_RSA_Certification_Authority.pem #11 42.88 Adding debian:OISTE_WISeKey_Global_Root_GC_CA.pem #11 42.88 Adding debian:Buypass_Class_2_Root_CA.pem #11 42.89 Adding debian:AC_RAIZ_FNMT-RCM_SERVIDORES_SEGUROS.pem #11 42.89 Adding debian:e-Szigno_Root_CA_2017.pem #11 42.90 Adding debian:Microsoft_ECC_Root_Certificate_Authority_2017.pem #11 42.91 Adding debian:Security_Communication_Root_CA.pem #11 42.91 Adding debian:GlobalSign_Root_CA.pem #11 42.92 Adding debian:E-Tugra_Certification_Authority.pem #11 42.93 Adding debian:Trustwave_Global_Certification_Authority.pem #11 42.93 Adding debian:QuoVadis_Root_CA_2_G3.pem #11 42.93 Adding debian:SSL.com_EV_Root_Certification_Authority_ECC.pem #11 42.94 Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068_2.pem #11 42.95 Adding debian:E-Tugra_Global_Root_CA_ECC_v3.pem #11 42.95 Adding debian:Entrust.net_Premium_2048_Secure_Server_CA.pem #11 42.96 Adding debian:AffirmTrust_Premium.pem #11 42.96 Adding debian:Amazon_Root_CA_3.pem #11 42.97 Adding debian:AffirmTrust_Networking.pem #11 42.98 Adding debian:IdenTrust_Public_Sector_Root_CA_1.pem #11 42.98 Adding debian:GlobalSign_ECC_Root_CA_-_R4.pem #11 42.99 Adding debian:QuoVadis_Root_CA_3_G3.pem #11 42.99 Adding debian:certSIGN_ROOT_CA.pem #11 43.00 Adding debian:IdenTrust_Commercial_Root_CA_1.pem #11 43.00 Adding debian:SecureTrust_CA.pem #11 43.01 Adding debian:DigiCert_Global_Root_G3.pem #11 43.01 Adding debian:GlobalSign_Root_E46.pem #11 43.02 Adding debian:SwissSign_Silver_CA_-_G2.pem #11 43.02 Adding debian:vTrus_ECC_Root_CA.pem #11 43.03 Adding debian:GlobalSign_ECC_Root_CA_-_R5.pem #11 43.04 Adding debian:OISTE_WISeKey_Global_Root_GB_CA.pem #11 43.04 Adding debian:DigiCert_Assured_ID_Root_G3.pem #11 43.05 Adding debian:emSign_Root_CA_-_G1.pem #11 43.05 Adding debian:DigiCert_TLS_RSA4096_Root_G5.pem #11 43.06 Adding debian:COMODO_Certification_Authority.pem #11 43.06 Adding debian:DigiCert_Global_Root_CA.pem #11 43.07 Adding debian:QuoVadis_Root_CA_2.pem #11 43.08 Adding debian:TWCA_Global_Root_CA.pem #11 43.09 Adding debian:ACCVRAIZ1.pem #11 43.09 Adding debian:GTS_Root_R2.pem #11 43.10 Adding debian:HARICA_TLS_RSA_Root_CA_2021.pem #11 43.10 Adding debian:Actalis_Authentication_Root_CA.pem #11 43.11 Adding debian:Certum_Trusted_Network_CA_2.pem #11 43.11 Adding debian:Atos_TrustedRoot_2011.pem #11 43.11 Adding debian:T-TeleSec_GlobalRoot_Class_3.pem #11 43.13 Adding debian:NetLock_Arany_=Class_Gold=_F\u0151tan\u00fas\u00edtv\u00e1ny.pem #11 43.13 Adding debian:Certigna.pem #11 43.14 Adding debian:Microsec_e-Szigno_Root_CA_2009.pem #11 43.14 Adding debian:UCA_Extended_Validation_Root.pem #11 43.14 Adding debian:DigiCert_Assured_ID_Root_G2.pem #11 43.15 Adding debian:QuoVadis_Root_CA_3.pem #11 43.15 Adding debian:NAVER_Global_Root_Certification_Authority.pem #11 43.16 Adding debian:GlobalSign_Root_CA_-_R6.pem #11 43.17 Adding debian:TUBITAK_Kamu_SM_SSL_Kok_Sertifikasi_-_Surum_1.pem #11 43.17 Adding debian:Trustwave_Global_ECC_P384_Certification_Authority.pem #11 43.18 Adding debian:Go_Daddy_Class_2_CA.pem #11 43.18 Adding debian:TWCA_Root_Certification_Authority.pem #11 43.19 Adding debian:certSIGN_Root_CA_G2.pem #11 43.19 Adding debian:COMODO_ECC_Certification_Authority.pem #11 43.20 Adding debian:ISRG_Root_X2.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_EV_2009.pem #11 43.21 Adding debian:D-TRUST_Root_Class_3_CA_2_2009.pem #11 43.22 Adding debian:UCA_Global_G2_Root.pem #11 43.22 Adding debian:D-TRUST_BR_Root_CA_1_2020.pem #11 43.23 Adding debian:Secure_Global_CA.pem #11 43.24 Adding debian:Hongkong_Post_Root_CA_3.pem #11 43.24 Adding debian:Buypass_Class_3_Root_CA.pem #11 43.25 Adding debian:Entrust_Root_Certification_Authority_-_G2.pem #11 43.25 Adding debian:SSL.com_Root_Certification_Authority_ECC.pem #11 43.26 Adding debian:Hellenic_Academic_and_Research_Institutions_RootCA_2015.pem #11 43.27 Adding debian:E-Tugra_Global_Root_CA_RSA_v3.pem #11 43.27 Adding debian:USERTrust_ECC_Certification_Authority.pem #11 43.28 Adding debian:ePKI_Root_Certification_Authority.pem #11 43.28 Adding debian:ISRG_Root_X1.pem #11 43.29 Adding debian:HARICA_TLS_ECC_Root_CA_2021.pem #11 43.29 Adding debian:GTS_Root_R3.pem #11 43.30 Adding debian:Hellenic_Academic_and_Research_Institutions_ECC_RootCA_2015.pem #11 43.30 Adding debian:Security_Communication_RootCA2.pem #11 43.31 Adding debian:GlobalSign_Root_CA_-_R3.pem #11 43.35 done. #11 43.36 Processing triggers for mime-support (3.64ubuntu1) ... #11 43.40 Processing triggers for libc-bin (2.31-0ubuntu9.15) ... #11 43.45 Processing triggers for ca-certificates (20230311ubuntu0.20.04.1) ... #11 43.47 Updating certificates in /etc/ssl/certs... #11 44.82 0 added, 0 removed; done. #11 44.82 Running hooks in /etc/ca-certificates/update.d... #11 44.83 #11 45.16 done. #11 45.16 done. #11 DONE 45.9s #12 [ 8/17] WORKDIR /opt/mlflow #12 DONE 0.1s #13 [ 9/17] RUN pip install mlflow==2.12.2 #13 1.009 Collecting mlflow==2.12.2 #13 1.075 Downloading mlflow-2.12.2-py3-none-any.whl.metadata (29 kB) #13 1.207 Collecting Flask < 4 (from mlflow==2.12.2) #13 1.222 Downloading flask-3.0.3-py3-none-any.whl.metadata (3.2 kB) #13 1.309 Collecting alembic!=1.10.0, < 2 (from mlflow==2.12.2) #13 1.324 Downloading alembic-1.13.1-py3-none-any.whl.metadata (7.4 kB) #13 1.395 Collecting click < 9,>=7.0 (from mlflow==2.12.2) #13 1.409 Downloading click-8.1.7-py3-none-any.whl.metadata (3.0 kB) #13 1.467 Collecting cloudpickle < 4 (from mlflow==2.12.2) #13 1.483 Downloading cloudpickle-3.0.0-py3-none-any.whl.metadata (7.0 kB) #13 1.564 Collecting docker < 8,>=4.0.0 (from mlflow==2.12.2) #13 1.579 Downloading docker-7.0.0-py3-none-any.whl.metadata (3.5 kB) #13 1.635 Collecting entrypoints < 1 (from mlflow==2.12.2) #13 1.651 Downloading entrypoints-0.4-py3-none-any.whl.metadata (2.6 kB) #13 1.742 Collecting gitpython < 4,>=3.1.9 (from mlflow==2.12.2) #13 1.757 Downloading GitPython-3.1.43-py3-none-any.whl.metadata (13 kB) #13 1.822 Collecting graphene < 4 (from mlflow==2.12.2) #13 1.837 Downloading graphene-3.3-py2.py3-none-any.whl.metadata (7.7 kB) #13 1.946 Collecting importlib-metadata!=4.7.0, < 8,>=3.7.0 (from mlflow==2.12.2) #13 1.960 Downloading importlib_metadata-7.1.0-py3-none-any.whl.metadata (4.7 kB) #13 2.027 Collecting markdown < 4,>=3.3 (from mlflow==2.12.2) #13 2.079 Downloading Markdown-3.6-py3-none-any.whl.metadata (7.0 kB) #13 2.299 Collecting matplotlib < 4 (from mlflow==2.12.2) #13 2.314 Downloading matplotlib-3.7.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (5.7 kB) #13 2.593 Collecting numpy < 2 (from mlflow==2.12.2) #13 2.612 Downloading numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB) #13 2.685 Collecting packaging < 25 (from mlflow==2.12.2) #13 2.698 Downloading packaging-24.0-py3-none-any.whl.metadata (3.2 kB) #13 2.902 Collecting pandas < 3 (from mlflow==2.12.2) #13 2.916 Downloading pandas-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB) #13 3.172 Collecting protobuf < 5,>=3.12.0 (from mlflow==2.12.2) #13 3.187 Downloading protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes) #13 3.323 Collecting pyarrow < 16,>=4.0.0 (from mlflow==2.12.2) #13 3.338 Downloading pyarrow-15.0.2-cp38-cp38-manylinux_2_28_x86_64.whl.metadata (3.0 kB) #13 3.456 Collecting pytz < 2025 (from mlflow==2.12.2) #13 3.471 Downloading pytz-2024.1-py2.py3-none-any.whl.metadata (22 kB) #13 3.482 Requirement already satisfied: pyyaml < 7,>=5.1 in /usr/lib/python3/dist-packages (from mlflow==2.12.2) (5.3.1) #13 3.672 Collecting querystring-parser < 2 (from mlflow==2.12.2) #13 3.689 Downloading querystring_parser-1.2.4-py2.py3-none-any.whl.metadata (559 bytes) #13 3.780 Collecting requests < 3,>=2.17.3 (from mlflow==2.12.2) #13 3.794 Downloading requests-2.31.0-py3-none-any.whl.metadata (4.6 kB) #13 3.969 Collecting scikit-learn < 2 (from mlflow==2.12.2) #13 3.985 Downloading scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB) #13 4.159 Collecting scipy < 2 (from mlflow==2.12.2) #13 4.175 Downloading scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (58 kB) #13 4.199 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fonttools, entrypoints, cycler, cloudpickle, click, charset-normalizer, certifi, blinker, Werkzeug, sqlalchemy, scipy, requests, querystring-parser, python-dateutil, pyarrow, Mako, Jinja2, importlib-resources, importlib-metadata, gunicorn, graphql-relay, gitdb, contourpy, scikit-learn, pandas, matplotlib, markdown, graphene, gitpython, Flask, docker, alembic, mlflow #13 34.72 Successfully installed Flask-3.0.3 Jinja2-3.1.4 Mako-1.3.5 MarkupSafe-2.1.5 Werkzeug-3.0.3 alembic-1.13.1 aniso8601-9.0.1 blinker-1.8.2 certifi-2024.2.2 charset-normalizer-3.3.2 click-8.1.7 cloudpickle-3.0.0 contourpy-1.1.1 cycler-0.12.1 docker-7.0.0 entrypoints-0.4 fonttools-4.51.0 gitdb-4.0.11 gitpython-3.1.43 graphene-3.3 graphql-core-3.2.3 graphql-relay-3.2.0 greenlet-3.0.3 gunicorn-22.0.0 idna-3.7 importlib-metadata-7.1.0 importlib-resources-6.4.0 itsdangerous-2.2.0 joblib-1.4.2 kiwisolver-1.4.5 markdown-3.6 matplotlib-3.7.5 mlflow-2.12.2 numpy-1.24.4 packaging-24.0 pandas-2.0.3 pillow-10.3.0 protobuf-4.25.3 pyarrow-15.0.2 pyparsing-3.1.2 python-dateutil-2.9.0.post0 pytz-2024.1 querystring-parser-1.2.4 requests-2.31.0 scikit-learn-1.3.2 scipy-1.10.1 six-1.16.0 smmap-5.0.1 sqlalchemy-2.0.30 sqlparse-0.5.0 threadpoolctl-3.5.0 typing-extensions-4.11.0 tzdata-2024.1 urllib3-2.2.1 zipp-3.18.2 #13 34.72 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv #13 DONE 36.4s #14 [10/17] RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.12.2:pom -DoutputDirectory=/opt/java #14 1.558 [INFO] Scanning for projects... #14 1.845 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-clean-plugin/2.5/maven-clean-plugin-2.5.pom #14 2.423 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-clean-plugin/2.5/maven-clean-plugin-2.5.pom (3.9 kB at 6.7 kB/s) #14 2.448 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-plugins/22/maven-plugins-22.pom #14 2.499 [INFO] Downloaded from central: https://repo.maven.apache.org/maven2/org/apache/maven/plugins/maven-plugins/22/maven-plugins-22.pom (13 kB at 256 kB/s) #14 2.511 [INFO] Downloading from central: 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12.86 [INFO] Finished at: 2024-05-16T20:25:32Z #14 12.86 [INFO] ------------------------------------------------------------------------ #14 DONE 13.0s #15 [11/17] RUN mvn --batch-mode dependency:copy -Dartifact=org.mlflow:mlflow-scoring:2.12.2:jar -DoutputDirectory=/opt/java/jars #15 1.696 [INFO] Scanning for projects... #15 1.897 [INFO] #15 1.897 [INFO] ------------------ < org.apache.maven:standalone-pom >------------------- #15 1.898 [INFO] Building Maven Stub Project (No POM) 1 #15 1.898 [INFO] --------------------------------[ pom ]--------------------------------- #15 1.905 [INFO] #15 1.905 [INFO] --- maven-dependency-plugin:2.8:copy (default-cli) @ standalone-pom --- #15 2.594 [INFO] Configured Artifact: org.mlflow:mlflow-scoring:2.12.2:jar #15 2.804 [INFO] Downloading from central: https://repo.maven.apache.org/maven2/org/mlflow/mlflow-scoring/2.12.2/mlflow-scoring-2.12.2.jar #15 3.207 [INFO] Downloaded from central: 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/opt/java/jars/maven-plugin-parameter-documenter-2.2.1.jar #17 134.5 [INFO] Copying commons-math3-3.4.1.jar to /opt/java/jars/commons-math3-3.4.1.jar #17 134.5 [INFO] Copying junit-4.12.jar to /opt/java/jars/junit-4.12.jar #17 134.5 [INFO] Copying slf4j-jdk14-1.7.25.jar to /opt/java/jars/slf4j-jdk14-1.7.25.jar #17 134.5 [INFO] Copying slf4j-api-1.7.25.jar to /opt/java/jars/slf4j-api-1.7.25.jar #17 134.5 [INFO] Copying jetty-io-9.4.11.v20180605.jar to /opt/java/jars/jetty-io-9.4.11.v20180605.jar #17 134.5 [INFO] Copying httpclient-4.5.6.jar to /opt/java/jars/httpclient-4.5.6.jar #17 134.5 [INFO] Copying maven-model-2.2.1.jar to /opt/java/jars/maven-model-2.2.1.jar #17 134.5 [INFO] Copying jackson-dataformat-yaml-2.10.0.jar to /opt/java/jars/jackson-dataformat-yaml-2.10.0.jar #17 134.5 [INFO] Copying doxia-logging-api-1.1.jar to /opt/java/jars/doxia-logging-api-1.1.jar #17 134.5 [INFO] Copying surefire-api-2.22.0.jar to /opt/java/jars/surefire-api-2.22.0.jar #17 134.5 [INFO] Copying maven-plugin-registry-2.2.1.jar to /opt/java/jars/maven-plugin-registry-2.2.1.jar #17 134.5 [INFO] Copying backport-util-concurrent-3.1.jar to /opt/java/jars/backport-util-concurrent-3.1.jar #17 134.5 [INFO] Copying wagon-http-shared-1.0-beta-6.jar to /opt/java/jars/wagon-http-shared-1.0-beta-6.jar #17 134.5 [INFO] Copying commons-httpclient-3.1.jar to /opt/java/jars/commons-httpclient-3.1.jar #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] BUILD SUCCESS #17 134.5 [INFO] ------------------------------------------------------------------------ #17 134.5 [INFO] Total time: 02:12 min #17 134.5 [INFO] Finished at: 2024-05-16T20:27:50Z #17 134.5 [INFO] ------------------------------------------------------------------------ #17 DONE 134.8s #18 [14/17] COPY model_dir/success6g_model /opt/ml/model #18 DONE 0.1s #19 [15/17] RUN python -c \"from mlflow.models import container as C; C._install_pyfunc_deps('/opt/ml/model', install_mlflow=False, enable_mlserver=True, env_manager='virtualenv');\" #19 1.285 2024/05/16 20:27:52 INFO mlflow.models.container: creating and activating custom environment #19 1.289 2024/05/16 20:27:52 INFO mlflow.utils.virtualenv: Installing python 3.10.11 if it does not exist #19 1.673 Downloading Python-3.10.11.tar.xz... #19 1.673 -> https://www.python.org/ftp/python/3.10.11/Python-3.10.11.tar.xz #19 4.792 Installing Python-3.10.11... #19 108.0 Installed Python-3.10.11 to /root/.pyenv/versions/3.10.11 #19 108.4 2024/05/16 20:29:39 INFO mlflow.utils.virtualenv: Creating a new environment in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b with /root/.pyenv/versions/3.10.11/bin/python #19 109.3 created virtual environment CPython3.10.11.final.0-64 in 719ms #19 109.3 creator CPython3Posix(dest=/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b, clear=False, no_vcs_ignore=False, global=False) #19 109.3 seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/.local/share/virtualenv) #19 109.3 added seed packages: pip==24.0, setuptools==69.5.1, wheel==0.43.0 #19 109.3 activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator #19 109.4 2024/05/16 20:29:40 INFO mlflow.utils.virtualenv: Installing dependencies #19 168.4 WARNING: There was an error checking the latest version of pip. #19 169.3 Collecting mlserver!=1.3.1, < 1.4.0,>=1.2.0 #19 169.4 Downloading mlserver-1.3.5-py3-none-any.whl (113 kB) #19 169.4 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 113.2/113.2 kB 3.4 MB/s eta 0:00:00 #19 169.6 Collecting mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0 #19 169.6 Downloading mlserver_mlflow-1.3.5-py3-none-any.whl (10 kB) #19 169.7 Requirement already satisfied: click in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) (8.1.3) #19 169.8 Collecting fastapi!=0.89.0, < =0.89.1,>=0.88.0 (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.8 Downloading fastapi-0.89.1-py3-none-any.whl (55 kB) #19 169.8 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 55.8/55.8 kB 7.4 MB/s eta 0:00:00 #19 169.9 Collecting python-dotenv (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 169.9 Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB) #19 171.0 Collecting grpcio (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.0 Downloading grpcio-1.63.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB) #19 171.3 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1.4.0,>=1.2.0) (4.25.3) #19 171.5 Collecting uvicorn (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.5 Downloading uvicorn-0.29.0-py3-none-any.whl (60 kB) #19 171.6 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 60.8/60.8 kB 8.7 MB/s eta 0:00:00 #19 171.7 Collecting starlette-exporter (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.7 Downloading starlette_exporter-0.21.0-py3-none-any.whl (14 kB) #19 171.9 Collecting py-grpc-prometheus (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 171.9 Downloading py_grpc_prometheus-0.8.0-py3-none-any.whl (12 kB) #19 172.1 Collecting aiokafka (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 172.1 Downloading aiokafka-0.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB) #19 172.2 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orjson-3.10.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (142 kB) #19 173.6 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 142.5/142.5 kB 19.4 MB/s eta 0:00:00 #19 173.7 Collecting uvloop (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 173.7 Downloading uvloop-0.19.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.4 MB) #19 173.8 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.4/3.4 MB 19.8 MB/s eta 0:00:00 #19 173.9 Requirement already satisfied: mlflow in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (2.12.2) #19 174.2 Collecting pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1, < 2.0.0,>=1.6.2 (from fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 174.2 Downloading pydantic-1.10.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB) #19 174.4 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 3.1/3.1 MB 19.8 MB/s eta 0:00:00 #19 174.5 Collecting starlette==0.22.0 (from fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 174.5 Downloading starlette-0.22.0-py3-none-any.whl (64 kB) #19 174.5 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0:00:00 #19 176.1 Collecting geventhttpclient < =2.0.2,>=1.4.4 (from tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.2 Downloading geventhttpclient-2.0.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (100 kB) #19 176.2 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.3/100.3 kB 10.5 MB/s eta 0:00:00 #19 176.3 Requirement already satisfied: async-timeout in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from aiokafka->mlserver!=1.3.1, < 1.4.0,>=1.2.0) (4.0.3) #19 176.3 Requirement already satisfied: packaging in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from aiokafka->mlserver!=1.3.1, < 1.4.0,>=1.2.0) (23.1) #19 176.4 Requirement already satisfied: Flask < 4 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.0.3) #19 176.4 Requirement already satisfied: alembic!=1.10.0, < 2 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (1.13.1) #19 176.4 Requirement already satisfied: cloudpickle < 4 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (2.2.1) #19 176.4 Requirement already satisfied: docker < 8,>=4.0.0 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (7.0.0) #19 176.4 Requirement already satisfied: entrypoints < 1 in 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requirements. This could take a while. #19 176.6 Collecting starlette-exporter (from mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.7 Downloading starlette_exporter-0.20.0-py3-none-any.whl (14 kB) #19 176.7 Downloading starlette_exporter-0.19.0-py3-none-any.whl (14 kB) #19 176.7 Downloading starlette_exporter-0.18.0-py3-none-any.whl (14 kB) #19 176.8 Downloading starlette_exporter-0.17.1-py3-none-any.whl (14 kB) #19 176.8 Collecting h11>=0.8 (from uvicorn->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 176.8 Downloading h11-0.14.0-py3-none-any.whl (58 kB) #19 176.9 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 58.3/58.3 kB 8.0 MB/s eta 0:00:00 #19 176.9 Requirement already satisfied: typing-extensions>=4.0 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from 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/root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from sqlalchemy < 3,>=1.4.0->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (3.0.3) #19 179.6 Collecting sniffio>=1.1 (from anyio < 5,>=3.4.0->starlette==0.22.0->fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 179.6 Downloading sniffio-1.3.1-py3-none-any.whl (10 kB) #19 179.7 Collecting exceptiongroup>=1.0.2 (from anyio < 5,>=3.4.0->starlette==0.22.0->fastapi!=0.89.0, < =0.89.1,>=0.88.0->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 179.7 Downloading exceptiongroup-1.2.1-py3-none-any.whl (16 kB) #19 179.9 Collecting zope.event (from gevent>=0.13->geventhttpclient < =2.0.2,>=1.4.4->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 180.0 Downloading zope.event-5.0-py3-none-any.whl (6.8 kB) #19 180.2 Collecting zope.interface (from gevent>=0.13->geventhttpclient < =2.0.2,>=1.4.4->tritonclient[http]>=2.24->mlserver!=1.3.1, < 1.4.0,>=1.2.0) #19 180.2 Downloading zope.interface-6.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (247 kB) #19 180.2 \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 247.5/247.5 kB 31.3 MB/s eta 0:00:00 #19 180.2 Requirement already satisfied: smmap < 6,>=3.0.1 in /root/.mlflow/envs/mlflow-bb8a0827694c8de04b7469b4f93834b709416c0b/lib/python3.10/site-packages (from gitdb < 5,>=4.0.1->gitpython < 4,>=3.1.9->mlflow->mlserver-mlflow!=1.3.1, < 1.4.0,>=1.2.0) (5.0.1) #19 181.2 Installing collected packages: brotli, zope.interface, zope.event, uvloop, sniffio, python-rapidjson, python-dotenv, pydantic, prometheus-client, orjson, multidict, importlib-resources, h11, grpcio, frozenlist, exceptiongroup, attrs, aiokafka, aiofiles, yarl, uvicorn, tritonclient, py-grpc-prometheus, gevent, anyio, aiosignal, starlette, geventhttpclient, aiohttp, starlette-exporter, fastapi, mlserver, mlserver-mlflow #19 184.4 Successfully installed aiofiles-23.2.1 aiohttp-3.9.5 aiokafka-0.10.0 aiosignal-1.3.1 anyio-4.3.0 attrs-23.2.0 brotli-1.1.0 exceptiongroup-1.2.1 fastapi-0.89.1 frozenlist-1.4.1 gevent-24.2.1 geventhttpclient-2.0.2 grpcio-1.63.0 h11-0.14.0 importlib-resources-6.4.0 mlserver-1.3.5 mlserver-mlflow-1.3.5 multidict-6.0.5 orjson-3.10.3 prometheus-client-0.20.0 py-grpc-prometheus-0.8.0 pydantic-1.10.15 python-dotenv-1.0.1 python-rapidjson-1.16 sniffio-1.3.1 starlette-0.22.0 starlette-exporter-0.17.1 tritonclient-2.45.0 uvicorn-0.29.0 uvloop-0.19.0 yarl-1.9.4 zope.event-5.0 zope.interface-6.4 #19 184.4 WARNING: There was an error checking the latest version of pip. #19 DONE 186.7s #20 [16/17] RUN chmod o+rwX /opt/mlflow/ #20 DONE 0.4s #21 [17/17] RUN rm -rf /var/lib/apt/lists/* #21 DONE 0.6s #22 exporting to image #22 exporting layers #22 exporting layers 12.0s done #22 writing image sha256:29875270957bd037ee2b833cf9201a001d353e4fef34b1d8c4923b278ef8ac02 done #22 naming to docker.io/5uperpalo/mlflow-success6g done #22 DONE 12.1s ```","title":"Appendix"},{"location":"notebooks/05_model_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = 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event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { 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.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ 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In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Model analysis \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [3]: Copied! from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast In [4]: Copied! trainer = dill_load ( \"lgbm_trainer.dill\" ) trainer = dill_load(\"lgbm_trainer.dill\") In [5]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ trainer . target_col ], random_state = random_state , ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ trainer . target_col ], random_state = random_state , ) # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[trainer.target_col], random_state=random_state, ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[trainer.target_col], random_state=random_state, ) In [6]: Copied! df_predicted_proba = trainer . predict_proba ( df = df_test . drop ( columns = trainer . target_col ), binary2d = False ) df_predicted_cls = trainer . predict_cls ( df = df_test . drop ( columns = trainer . target_col )) df_predicted_proba = trainer.predict_proba( df=df_test.drop(columns=trainer.target_col), binary2d=False ) df_predicted_cls = trainer.predict_cls(df=df_test.drop(columns=trainer.target_col)) Model feature importance and \"certainty\" of the model in predicting classes - probability density per class \u00b6 In [7]: Copied! lgb . plot_importance ( trainer . model , importance_type = \"split\" ) # lgb.plot_importance(trainer.model, importance_type=\"gain\") lgb.plot_importance(trainer.model, importance_type=\"split\") # lgb.plot_importance(trainer.model, importance_type=\"gain\") Out[7]: <Axes: title={'center': 'Feature importance'}, xlabel='Feature importance', ylabel='Features'> In [8]: Copied! fig = prob_distrib_per_class ( predicted_probs = df_predicted_proba . values . flatten (), actual = df_test [ trainer . target_col ] . values . flatten (), task = \"binary\" , ) fig = prob_distrib_per_class( predicted_probs=df_predicted_proba.values.flatten(), actual=df_test[trainer.target_col].values.flatten(), task=\"binary\", ) SHAP analysis \u00b6 In [9]: Copied! df_test_prep = trainer . preprocessors [ 0 ] . transform ( df_test . drop ( columns = [ trainer . target_col ]) ) df_test_prep [ trainer . target_col ] = df_test [ trainer . target_col ] . astype ( int ) lgb_test , _ = to_lgbdataset ( train = df_test_prep , cat_cols = trainer . cat_cols , target_col = trainer . target_col , id_cols = trainer . id_cols , ) df_test_prep = trainer.preprocessors[0].transform( df_test.drop(columns=[trainer.target_col]) ) df_test_prep[trainer.target_col] = df_test[trainer.target_col].astype(int) lgb_test, _ = to_lgbdataset( train=df_test_prep, cat_cols=trainer.cat_cols, target_col=trainer.target_col, id_cols=trainer.id_cols, ) In [10]: Copied! base_value = trainer . model . predict ( data = lgb_test . data , pred_contrib = True ) shap_values = base_value [:, : - 1 ] base_value = np . mean ( base_value [:, - 1 ]) base_value = trainer.model.predict(data=lgb_test.data, pred_contrib=True) shap_values = base_value[:, :-1] base_value = np.mean(base_value[:, -1]) In [11]: Copied! shap . summary_plot ( shap_values , features = lgb_test . data , plot_type = \"dot\" , sort = False , show = True ) shap.summary_plot( shap_values, features=lgb_test.data, plot_type=\"dot\", sort=False, show=True ) In [19]: Copied! df_predicted_cls [ 10 : 12 ] df_predicted_cls[10:12] Out[19]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 10 0 11 1 In [20]: Copied! df_test . iloc [ 10 : 12 ] df_test.iloc[10:12] Out[20]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 579 38.04 89.0 895.5 0.0 22.0 9.77 83.14 34050.0 101.0 14.76 0 123abc 23 100.00 22.0 926.0 0.0 11.0 12.66 83.53 32160.0 101.0 14.72 1 123abc In [21]: Copied! shap_decision_plot = shap . decision_plot ( base_value = base_value , shap_values = shap_values [ 10 : 12 ], features = trainer . model . feature_name (), feature_names = trainer . model . feature_name (), # legend_labels=legend_labels, link = \"logit\" , # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), ) shap_decision_plot = shap.decision_plot( base_value=base_value, shap_values=shap_values[10:12], features=trainer.model.feature_name(), feature_names=trainer.model.feature_name(), # legend_labels=legend_labels, link=\"logit\", # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), )","title":"05_model_analysis"},{"location":"notebooks/05_model_analysis.html#model-analysis","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [3]: Copied! from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast from inference_model.utils import dill_load from inference_model.training.utils import get_feature_importance from inference_model.eda.target.analysis import correlation from inference_model.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from inference_model.training.utils import to_lgbdataset import shap import warnings import numpy as np import ast In [4]: Copied! trainer = dill_load ( \"lgbm_trainer.dill\" ) trainer = dill_load(\"lgbm_trainer.dill\") In [5]: Copied! # 1. get/create some example data with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df_pd = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df_pd = pd . concat ([ df_pd , temp_df ], axis = 1 ) df_pd . dropna ( inplace = True ) df_pd [ \"class\" ] = 0 df_pd [ \"car_id\" ] = \"123abc\" df_pd . loc [: 100 , [ \"class\" ]] = 1 df_pd . loc [: 100 , [ \"engine_load\" ]] = 100 target_col = \"class\" id_cols = [ \"car_id\" ] cat_cols = [] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ trainer . target_col ], random_state = random_state , ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ trainer . target_col ], random_state = random_state , ) # 1. get/create some example data with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df_pd = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df_pd = pd.concat([df_pd, temp_df], axis=1) df_pd.dropna(inplace=True) df_pd[\"class\"] = 0 df_pd[\"car_id\"] = \"123abc\" df_pd.loc[:100, [\"class\"]] = 1 df_pd.loc[:100, [\"engine_load\"]] = 100 target_col = \"class\" id_cols = [\"car_id\"] cat_cols = [] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() df_pd[cat_cols] = df_pd[cat_cols].astype(str) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[trainer.target_col], random_state=random_state, ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[trainer.target_col], random_state=random_state, ) In [6]: Copied! df_predicted_proba = trainer . predict_proba ( df = df_test . drop ( columns = trainer . target_col ), binary2d = False ) df_predicted_cls = trainer . predict_cls ( df = df_test . drop ( columns = trainer . target_col )) df_predicted_proba = trainer.predict_proba( df=df_test.drop(columns=trainer.target_col), binary2d=False ) df_predicted_cls = trainer.predict_cls(df=df_test.drop(columns=trainer.target_col))","title":"Model analysis"},{"location":"notebooks/05_model_analysis.html#model-feature-importance-and-certainty-of-the-model-in-predicting-classes-probability-density-per-class","text":"In [7]: Copied! lgb . plot_importance ( trainer . model , importance_type = \"split\" ) # lgb.plot_importance(trainer.model, importance_type=\"gain\") lgb.plot_importance(trainer.model, importance_type=\"split\") # lgb.plot_importance(trainer.model, importance_type=\"gain\") Out[7]: <Axes: title={'center': 'Feature importance'}, xlabel='Feature importance', ylabel='Features'> In [8]: Copied! fig = prob_distrib_per_class ( predicted_probs = df_predicted_proba . values . flatten (), actual = df_test [ trainer . target_col ] . values . flatten (), task = \"binary\" , ) fig = prob_distrib_per_class( predicted_probs=df_predicted_proba.values.flatten(), actual=df_test[trainer.target_col].values.flatten(), task=\"binary\", )","title":"Model feature importance and \"certainty\" of the model in predicting classes - probability density per class"},{"location":"notebooks/05_model_analysis.html#shap-analysis","text":"In [9]: Copied! df_test_prep = trainer . preprocessors [ 0 ] . transform ( df_test . drop ( columns = [ trainer . target_col ]) ) df_test_prep [ trainer . target_col ] = df_test [ trainer . target_col ] . astype ( int ) lgb_test , _ = to_lgbdataset ( train = df_test_prep , cat_cols = trainer . cat_cols , target_col = trainer . target_col , id_cols = trainer . id_cols , ) df_test_prep = trainer.preprocessors[0].transform( df_test.drop(columns=[trainer.target_col]) ) df_test_prep[trainer.target_col] = df_test[trainer.target_col].astype(int) lgb_test, _ = to_lgbdataset( train=df_test_prep, cat_cols=trainer.cat_cols, target_col=trainer.target_col, id_cols=trainer.id_cols, ) In [10]: Copied! base_value = trainer . model . predict ( data = lgb_test . data , pred_contrib = True ) shap_values = base_value [:, : - 1 ] base_value = np . mean ( base_value [:, - 1 ]) base_value = trainer.model.predict(data=lgb_test.data, pred_contrib=True) shap_values = base_value[:, :-1] base_value = np.mean(base_value[:, -1]) In [11]: Copied! shap . summary_plot ( shap_values , features = lgb_test . data , plot_type = \"dot\" , sort = False , show = True ) shap.summary_plot( shap_values, features=lgb_test.data, plot_type=\"dot\", sort=False, show=True ) In [19]: Copied! df_predicted_cls [ 10 : 12 ] df_predicted_cls[10:12] Out[19]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } class 10 0 11 1 In [20]: Copied! df_test . iloc [ 10 : 12 ] df_test.iloc[10:12] Out[20]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage class car_id 579 38.04 89.0 895.5 0.0 22.0 9.77 83.14 34050.0 101.0 14.76 0 123abc 23 100.00 22.0 926.0 0.0 11.0 12.66 83.53 32160.0 101.0 14.72 1 123abc In [21]: Copied! shap_decision_plot = shap . decision_plot ( base_value = base_value , shap_values = shap_values [ 10 : 12 ], features = trainer . model . feature_name (), feature_names = trainer . model . feature_name (), # legend_labels=legend_labels, link = \"logit\" , # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), ) shap_decision_plot = shap.decision_plot( base_value=base_value, shap_values=shap_values[10:12], features=trainer.model.feature_name(), feature_names=trainer.model.feature_name(), # legend_labels=legend_labels, link=\"logit\", # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), )","title":"SHAP analysis"},{"location":"notebooks/success6g-edge.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); In [1]: Copied! import pandas as pd import ast import os import redis os . chdir ( \"..\" ) import pandas as pd import ast import os import redis os.chdir(\"..\") Get the data into pandas \u00b6 In [2]: Copied! with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df = pd . concat ([ df , temp_df ], axis = 1 ) with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df = pd.concat([df, temp_df], axis=1) In [3]: Copied! df df Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage 0 37.25 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 37.25 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 2 36.86 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 3 36.86 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 4 36.86 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 ... ... ... ... ... ... ... ... ... ... ... 32740 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32741 NaN NaN NaN NaN NaN 9.44 NaN NaN NaN NaN 32742 NaN NaN NaN NaN NaN 9.49 NaN NaN NaN NaN 32743 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32744 NaN NaN NaN NaN NaN 9.52 NaN NaN NaN NaN 32745 rows \u00d7 10 columns In [4]: Copied! df . dropna ( inplace = True ) df.dropna(inplace=True) In [5]: Copied! df [ \"class\" ] = 0 df . loc [: 100 , [ \"class\" ]] = 1 df . loc [: 100 , [ \"engine_load\" ]] = 100 df[\"class\"] = 0 df.loc[:100, [\"class\"]] = 1 df.loc[:100, [\"engine_load\"]] = 100 In [6]: Copied! from lightgbm import LGBMClassifier model = LGBMClassifier () model . fit ( df . drop ( columns = [ \"class\" ]), df [ \"class\" ]) from lightgbm import LGBMClassifier model = LGBMClassifier() model.fit(df.drop(columns=[\"class\"]), df[\"class\"]) [LightGBM] [Info] Number of positive: 101, number of negative: 934 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 508 [LightGBM] [Info] Number of data points in the train set: 1035, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097585 -> initscore=-2.224356 [LightGBM] [Info] Start training from score -2.224356 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf Out[6]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} LGBMClassifier() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. LGBMClassifier LGBMClassifier() Redis \u00b6 Install and start the redis database \u00b6 pip install prometheus-client sudo apt install lsb-release curl gpg curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list sudo apt-get update sudo apt-get install redis sudo service redis-server start # sudo service redis-server stop sudo service redis-server status redis-cli ping Fill the Redis database \u00b6 send data to redis redis-cli -h localhost -p 6379 redis-cli -h 10.152.183.169 -a redis -p 6379 XADD v2x * car_id 123 engine_load 37.25 engine_coolant_temp 17.00 engine_speed 904.50 vehicle_speed 0.00 intake_air_temp 10.00 maf 12.55 throttle_position 83.14 fuel_rg_pressure 37270.00 barometric_pressure 101.00 control_voltage 0.0666 In [7]: Copied! # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis . Redis ( host = redis_host , port = 6379 , password = \"redis\" ) # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis.Redis(host=redis_host, port=6379, password=\"redis\") # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) In [8]: Copied! entry_data = { \"field1\" : \"value1\" , \"field2\" : \"value2\" } for index , row in df . iterrows (): redis_conn . xadd ( \"v2x\" , dict ( row )) entry_data = {\"field1\": \"value1\", \"field2\": \"value2\"} for index, row in df.iterrows(): redis_conn.xadd(\"v2x\", dict(row)) Retrieve data from redis \u00b6 In [13]: Copied! while True : # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[ stream , [[ number , parts ]]]] = redis_conn . xread ( streams = { \"v2x\" : \"$\" }, block = 0 ) keys = [ p . decode ( \"utf-8\" ) for p in parts . keys ()] vals = [ float ( p . decode ( \"utf-8\" )) for p in parts . values ()] d = pd . DataFrame ( dict ( zip ( keys , vals )), index = [ 0 ]) d [ \"predicted_class\" ] = model . predict ( d . drop ( columns = [ \"car_id\" ]))[ 0 ] print ( d ) while True: # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[stream, [[number, parts]]]] = redis_conn.xread(streams={\"v2x\": \"$\"}, block=0) keys = [p.decode(\"utf-8\") for p in parts.keys()] vals = [float(p.decode(\"utf-8\")) for p in parts.values()] d = pd.DataFrame(dict(zip(keys, vals)), index=[0]) d[\"predicted_class\"] = model.predict(d.drop(columns=[\"car_id\"]))[0] print(d)","title":"success6g-edge"},{"location":"notebooks/success6g-edge.html#get-the-data-into-pandas","text":"In [2]: Copied! with open ( \"data/log_tiguan_27_mar_dac.txt\" ) as f : data = ast . literal_eval ( f . read ()) df = pd . DataFrame () for data_value in data : temp_df = pd . DataFrame ( data_value [ list ( data_value )[ 0 ]]) . sort_values ( by = \"ts_millis:\" , ascending = True )[ \"value\" ] temp_df . rename ( list ( data_value )[ 0 ], inplace = True ) df = pd . concat ([ df , temp_df ], axis = 1 ) with open(\"data/log_tiguan_27_mar_dac.txt\") as f: data = ast.literal_eval(f.read()) df = pd.DataFrame() for data_value in data: temp_df = pd.DataFrame(data_value[list(data_value)[0]]).sort_values( by=\"ts_millis:\", ascending=True )[\"value\"] temp_df.rename(list(data_value)[0], inplace=True) df = pd.concat([df, temp_df], axis=1) In [3]: Copied! df df Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } engine_load engine_coolant_temp engine_speed vehicle_speed intake_air_temp maf throttle_position fuel_rg_pressure barometric_pressure control_voltage 0 37.25 17.0 904.5 0.0 10.0 12.55 83.14 37270.0 101.0 0.06 1 37.25 17.0 906.0 0.0 11.0 12.36 83.14 37800.0 101.0 14.56 2 36.86 17.0 905.0 0.0 10.0 12.36 83.53 37800.0 101.0 14.68 3 36.86 18.0 905.5 0.0 11.0 12.30 83.53 37800.0 101.0 14.72 4 36.86 18.0 907.0 0.0 11.0 12.47 83.14 36740.0 101.0 14.72 ... ... ... ... ... ... ... ... ... ... ... 32740 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32741 NaN NaN NaN NaN NaN 9.44 NaN NaN NaN NaN 32742 NaN NaN NaN NaN NaN 9.49 NaN NaN NaN NaN 32743 NaN NaN NaN NaN NaN 9.41 NaN NaN NaN NaN 32744 NaN NaN NaN NaN NaN 9.52 NaN NaN NaN NaN 32745 rows \u00d7 10 columns In [4]: Copied! df . dropna ( inplace = True ) df.dropna(inplace=True) In [5]: Copied! df [ \"class\" ] = 0 df . loc [: 100 , [ \"class\" ]] = 1 df . loc [: 100 , [ \"engine_load\" ]] = 100 df[\"class\"] = 0 df.loc[:100, [\"class\"]] = 1 df.loc[:100, [\"engine_load\"]] = 100 In [6]: Copied! from lightgbm import LGBMClassifier model = LGBMClassifier () model . fit ( df . drop ( columns = [ \"class\" ]), df [ \"class\" ]) from lightgbm import LGBMClassifier model = LGBMClassifier() model.fit(df.drop(columns=[\"class\"]), df[\"class\"]) [LightGBM] [Info] Number of positive: 101, number of negative: 934 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 508 [LightGBM] [Info] Number of data points in the train set: 1035, number of used features: 9 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097585 -> initscore=-2.224356 [LightGBM] [Info] Start training from score -2.224356 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf Out[6]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} LGBMClassifier() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. LGBMClassifier LGBMClassifier()","title":"Get the data into pandas"},{"location":"notebooks/success6g-edge.html#redis","text":"","title":"Redis"},{"location":"notebooks/success6g-edge.html#install-and-start-the-redis-database","text":"pip install prometheus-client sudo apt install lsb-release curl gpg curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list sudo apt-get update sudo apt-get install redis sudo service redis-server start # sudo service redis-server stop sudo service redis-server status redis-cli ping","title":"Install and start the redis database"},{"location":"notebooks/success6g-edge.html#fill-the-redis-database","text":"send data to redis redis-cli -h localhost -p 6379 redis-cli -h 10.152.183.169 -a redis -p 6379 XADD v2x * car_id 123 engine_load 37.25 engine_coolant_temp 17.00 engine_speed 904.50 vehicle_speed 0.00 intake_air_temp 10.00 maf 12.55 throttle_position 83.14 fuel_rg_pressure 37270.00 barometric_pressure 101.00 control_voltage 0.0666 In [7]: Copied! # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis . Redis ( host = redis_host , port = 6379 , password = \"redis\" ) # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) # Create connection object # pmulinka@saiacheron:~$ kubectl get svc -n redis # pmulinka@saiacheron:~$ kubectl exec -it redis-master-0 bash -n redis # I have no name!@redis-master-0:/$ dnsdomainname # redis-headless.redis.svc.cluster.local redis_host = \"redis-headless.redis.svc.cluster.local\" redis_host = \"10.152.183.169\" # https://redis.io/docs/latest/develop/connect/clients/python/ redis_conn = redis.Redis(host=redis_host, port=6379, password=\"redis\") # Add some data into 'my_stream' as key-value pairs # entry_data = {'field1': 'value1', 'field2': 'value2'} # for index, row in df.iterrows(): # redis_conn.xadd(\"v2x\", dict(row)) In [8]: Copied! entry_data = { \"field1\" : \"value1\" , \"field2\" : \"value2\" } for index , row in df . iterrows (): redis_conn . xadd ( \"v2x\" , dict ( row )) entry_data = {\"field1\": \"value1\", \"field2\": \"value2\"} for index, row in df.iterrows(): redis_conn.xadd(\"v2x\", dict(row))","title":"Fill the Redis database"},{"location":"notebooks/success6g-edge.html#retrieve-data-from-redis","text":"In [13]: Copied! while True : # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[ stream , [[ number , parts ]]]] = redis_conn . xread ( streams = { \"v2x\" : \"$\" }, block = 0 ) keys = [ p . decode ( \"utf-8\" ) for p in parts . keys ()] vals = [ float ( p . decode ( \"utf-8\" )) for p in parts . values ()] d = pd . DataFrame ( dict ( zip ( keys , vals )), index = [ 0 ]) d [ \"predicted_class\" ] = model . predict ( d . drop ( columns = [ \"car_id\" ]))[ 0 ] print ( d ) while True: # Listening for new items with XREAD # https://redis.io/docs/data-types/streams/ [[stream, [[number, parts]]]] = redis_conn.xread(streams={\"v2x\": \"$\"}, block=0) keys = [p.decode(\"utf-8\") for p in parts.keys()] vals = [float(p.decode(\"utf-8\")) for p in parts.values()] d = pd.DataFrame(dict(zip(keys, vals)), index=[0]) d[\"predicted_class\"] = model.predict(d.drop(columns=[\"car_id\"]))[0] print(d)","title":"Retrieve data from redis"}]}
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index 8284784..b499b96 100755
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -2,112 +2,112 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>None</loc>
-         <lastmod>2024-07-31</lastmod>
+         <lastmod>2024-08-01</lastmod>
          <changefreq>daily</changefreq>
     </url>
 </urlset>
\ No newline at end of file
diff --git a/sitemap.xml.gz b/sitemap.xml.gz
index 48fb76e48be8061d10f2a06430651769e431a6bd..c2375e5b5fd5c753b03c95f8a671729626005e59 100755
GIT binary patch
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