Updating the docs site to use SaladCloud

.github/CONTRIBUTING.md

@@ -26,13 +26,13 @@ In our documentation, we label the "Explanation" section "Products", but otherwi
 
 ## Discussing
 
-The easiest way to contribute to the project is by participating in community discussions on the [Salad Discord
+The easiest way to contribute to the project is by participating in community discussions on the [SaladCloud Discord
 server][discord]. The community often chimes in with helpful advice when you have a question, and you may also find
 yourself providing answers and helping others. Be sure to review the [code of conduct][code-of-conduct-page] before
 participating.
 
 _Please do not use GitHub issues to ask a question._ We will politely close a GitHub issue that asks a question and
-kindly refer you to the Salad Discord server or [official support][support].
+kindly refer you to the SaladCloud Discord server or [official support][support].
 
 _Please do not use public GitHub issues to report a security vulnerability._ Instead, refer to our [security
 policy][security-page] for responsible disclosure options.

.github/SUPPORT.md

@@ -1,10 +1,10 @@
 # Support
 
-Refer to the [official support documentation][support] for details on getting in touch with the Salad team.
+Refer to the [official support documentation][support] for details on getting in touch with the SaladCloud team.
 
-We also host community discussions on the [Salad Discord server][discord]. The community often chimes in with helpful
-advice when you have a question, and you may also find yourself providing answers and helping others. Be sure to review
-the [code of conduct][code-of-conduct-page] before participating.
+We also host community discussions on the [SaladCloud Discord server][discord]. The community often chimes in with
+helpful advice when you have a question, and you may also find yourself providing answers and helping others. Be sure to
+review the [code of conduct][code-of-conduct-page] before participating.
 
 [code-of-conduct-page]: ./CODE_OF_CONDUCT.md
 [discord]: https://discord.gg/ApSm4Kn7Aq

api-specs/s4.yml

@@ -1,6 +1,6 @@
 openapi: 3.0.0
 info:
-  title: Salad Simple Storage Service
+  title: SaladCloud Simple Storage Service
   version: 1.1.0
 components:
   securitySchemes:

cspell.json

@@ -14,7 +14,10 @@
     "german",
     "italian",
     "spanish",
-    "portuguese"
+    "portuguese",
+    "companies",
+    "filetypes",
+    "npm"
   ],
   "dictionaryDefinitions": [
     {
@@ -29,5 +32,6 @@
     "@cspell/dict-es-es/cspell-ext.json",
     "@cspell/dict-pt-br/cspell-ext.json",
     "@cspell/dict-pt-pt/cspell-ext.json"
-  ]
+  ],
+  "allowCompoundWords": true
 }

dictionaries/salad-cloud.txt

@@ -1,93 +1,141 @@
+-Jxpf
 alwayson
-azcopy
+ASGI
+astype
 azcopy
 btop
+buildx
+Civitai
 CivitAI
 ckpt
 clid
-ComfyUI
 comfyui
+ComfyUI
 containerd
 Controlnet
 Controlnets
 cuda
 cudnn
 denoise
 denoising
-distil
+devel
+dewdney
 Diátaxis
+distil
 dreambooth
 dreamshaper
 encodedimage
 esports
 Falco
 fastapi
+ffprobe
 firstphase
+Flickr
+fooocus
 Fooocus
-getenv
+genpkey
 getenv
 getsize
-gsutil
+gevent
 gsutil
 Healthcheck
+healthz
+heygordian
+httpie
+imds
 IMDS
 imdssdk
+imgsz
+imshow
 infotexts
 inpainting
 INSTALLDIR
 interruptible
 italian
+iterrows
 konieshadow
 LAVIS
+libgl1-mesa-dev
+libsm6
+libxext6
+libxrender1
 listdir
 LoRA
 LoRAs
 Lykon
 makedirs
+metavoiceio
 mintlify
 neofetch
 nomos8khatLOtf_v20.safetensors
 noproxy
+notna
 numpy
+nvcr
+Ollama
+ONNX
 OpenAPI
 Pabbly
 Pabbly’s
+pafy
+Pafy
+pbtxt
 popitem
 pretrained
 println
+proxied
+pt
 pydantic
+pytorch
 Pytube
 Quickstart
+Roboflow
 routable
+RTMP
+rtsp
+RTSP
 Rundiffusion
+s6-overlay-noarch
 safetensors
 SaladCloud
 saladcloudimdssdk
 saladcloudimdssdkconfig
 saladtechnologies
+salesforce
 schnell
 SD.Next
 sdapi
 sdnext
-SDXL
 sdxl
+SDXL
+sklearn
 spanish
 subseed
 subseeds
+threadpoolctl
 timeframe
-timeframe
+tlsv
 tmax
 tmin
+tojson
+TPOT
+TTFT
+ultralytics
+Ultralytics
 uncond
 underperform
 unet
 upscaler
 upscalers
+uvicorn
 Uvicorn
 vaeapp
 vCPU
+vram
 webui
 WORKDIR
 xformers
 xlvaeapp
+yolob8m-custom
+yolov8
 zapier

guides/computer-vision/deploy-blip-with-cog.mdx

@@ -13,16 +13,16 @@ synchronous prediction, streaming output and asynchronous prediction with a webh
 impressive, providing different running statuses (STARTING, READY, BUSY and FAILED) within the server.
 
 The Cog prediction server can be further customized to meet specific needs. For instance, its path operation functions
-for the health checks and predictions are, by default, declared with ‘async def’, running in the same main thread. By
-declaring the predictions function with ‘def’, a new thread will be spawned to run the prediction when a new request
+for the health checks and predictions are, by default, declared with `async def`, running in the same main thread. By
+declaring the predictions function with `def`, a new thread will be spawned to run the prediction when a new request
 arrives.This setup can prevent long-running synchronous predictions from blocking timely responses to health queries.
 Another example is IPv6 support: the server is hardcoded to listen on an IPv4 port, we can modify the code to use IPv6
-by replacing ‘0.0.0.0’ with ‘::’ when launching its underlying Uvicorn server.
+by replacing `0.0.0.0` with `::` when launching its underlying Uvicorn server.
 
 The [BLIP](https://arxiv.org/pdf/2201.12086) (Bootstrapping Language-Image Pre-training) supports multiple image-to-text
 tasks, such as Image Captioning, Visual Question Answering and Image Text Matching. Each task requires a dedicated and
 fine-tuned BLIP model that is 1~2 GB in size. We can run inference for the three models of these three tasks
-simultaneously on a Salad node that has a GPU with 8GB VRAM.
+simultaneously on a SaladCloud node that has a GPU with 8GB VRAM.
 
 [LAVIS](https://github.com/salesforce/LAVIS) (A Library for Language-Vision Intelligence) is the Python deep learning
 library, and provides the unified access to the pretrained models, datasets and tasks for multimodal applications,
@@ -36,29 +36,29 @@ HTTP prediction server on SaladCloud, capable of handling various image-to-text
 The following 4 files are necessary for building the image, and we also provide some test code in
 [the Github Repo](https://github.com/SaladTechnologies/cog-deploy).
 
-```Text cog.yaml
+```yml
 build:
   gpu: true
 
-predict: "predict.py:Predictor"
+predict: 'predict.py:Predictor'
 ```
 
 The yaml file defines how to build a Docker image and how to run predictions. In this example, We only use the Cog HTTP
 prediction server (not its CLI tools), the file is quite simple. When the prediction server is launched, it will read
 the file, and then set the number of Uvicorn worker processes to 1 (when the GPU is enabled) and run the provided code -
 predict.py for inference.
 
-```Text requirements.txt
+```
 salesforce-lavis==1.0.2
 cog==0.9.8
 ```
 
-Based on the pytorch base image, we only need to install two Python packages and their dependencies for this
+Based on the PyTorch base image, we only need to install two Python packages and their dependencies for this
 application.
 
-```Text Dockerfile
+```Dockerfile
 # Base Image
-from docker.io/pytorch/pytorch:2.3.0-cuda12.1-cudnn8-runtime
+FROM docker.io/pytorch/pytorch:2.3.0-cuda12.1-cudnn8-runtime
 
 RUN apt-get update && apt-get install -y curl
 RUN pip install --upgrade pip
@@ -90,9 +90,9 @@ SaladCloud, it can start the inference immediately. Alternatively, the models ca
 the container is running. This approach has the advantage of smaller image sizes, allowing for faster builds and pushes.
 
 For the inbound connection, the containers running on SaladCloud need to listen on an IPv6 port. The Cog HTTP prediction
-server is currently hardcoded to use an IPv4 port, but this can be easily modified by a ‘sed’ command in the Dockerfile.
+server is currently hardcoded to use an IPv4 port, but this can be easily modified by a `sed` command in the Dockerfile.
 
-```Text predict.py
+```python
 from cog import BasePredictor, Input, Path
 import time
 import torch
@@ -124,7 +124,7 @@ predict(), run the inference based on inputs and return the results.
 
 # Test the image
 
-```Text
+```
 # Build
 docker image build -t docker.io/saladtechnologies/sip:0.0.3-blip -f Dockerfile .
 
@@ -140,18 +140,18 @@ docker push docker.io/saladtechnologies/sip:0.0.3-blip
 
 After the container is running, you can log into it and do some tests for health checks and predictions.
 
-<img src="/products/sce/images/bligcog/blipcog1.png" />
-<img src="/products/sce/images/bligcog/blipcog2.png" />
+<img src="/guides/computer-vision/images/blip-cog1.png" />
+<img src="/guides/computer-vision/images/blip-cog2.png" />
 
 The Cog HTTP prediction server is now using IPv6. The port number is configurable via the environment variable - ‘PORT’.
 
-<img src="/products/sce/images/bligcog/blipcog3.png" />
+<img src="/guides/computer-vision/images/blip-cog3.png" />
 
 # Deploy the image on SaladCloud
 
 Create a container group with the following parameters:
 
-```Text
+```md
 # Image Source
 
 saladtechnologies/sip:0.0.3-blip
@@ -162,33 +162,24 @@ saladtechnologies/sip:0.0.3-blip
 
 # Resource
 
-2 vCPUs, 8GB Memory
-Any GPU types with 8 GB or more VRAM
+2 vCPUs, 8GB Memory Any GPU types with 8 GB or more VRAM
 
 # Container Gateway
 
-Enbaled, Port 5000
+Enabled, Port 5000
 
 # Readiness Probe (Protocol: exec)
 
-Enabled
-Protocol: exec
+Enabled Protocol: exec
 
-Command: python
-Argument1: -c
-Argument2: import requests,sys;sys.exit(0 if 'READY' in requests.get('http://[::1]:5000/health-check').text else -1)
+Command: python Argument1: -c Argument2: import requests,sys;sys.exit(0 if 'READY' in
+requests.get('http://[::1]:5000/health-check').text else -1)
 
-Initial Delay Seconds: 60
-Period Seconds: 10
-Timeout Seconds: 5
-Success Threshold: 1
-Failure Threshold: 3
+Initial Delay Seconds: 60 Period Seconds: 10 Timeout Seconds: 5 Success Threshold: 1 Failure Threshold: 3
 
 # Environment Variables (Optional)
 
-COG_LOG_LEVEL, INFO (Default) / DEBUG / WARNING
-PORT, 5000 (Default) or others
-
+COG_LOG_LEVEL, INFO (Default) / DEBUG / WARNING PORT, 5000 (Default) or others
 ```
 
 The Readiness Probe is used to evaluate whether a container is ready to accept the traffic from the load balancer. The
@@ -197,15 +188,15 @@ probe with the protocol - exec, will run the given command inside the container,
 Python script is provided here and run regularly to check whether the models have been loaded successfully and the Cog
 HTTP prediction server is ready.
 
-<img src="/products/sce/images/bligcog/blipcog6.png" />
+<img src="/guides/computer-vision/images/blip-cog6.png" />
 
 # Test the inference endpoint
 
 After the container group is deployed, an access domain name will be created and can be used to access the application.
 
-<img src="/products/sce/images/bligcog/blipcog5.png" />
+<img src="/guides/computer-vision/images/blip-cog5.png" />
 
-```Text
+```shell
 # image_captioning
 
 curl -s -X POST \