Add QuickStart doc

Signed-off-by: Yikun Jiang <yikunkero@gmail.com>

docs/quick_start.md

@@ -1,17 +1,157 @@
 # Quick Start
 
-## Prerequisites
-### Support Devices
+## 1. Prerequisites
+
+### Supported Devices
 - Atlas A2 Training series (Atlas 800T A2, Atlas 900 A2 PoD, Atlas 200T A2 Box16, Atlas 300T A2)
 - Atlas 800I A2 Inference series (Atlas 800I A2)
 
-### Dependencies
-| Requirement | Supported version | Recommended version | Note                                     |
-|-------------|-------------------| ----------- |------------------------------------------|
-| vLLM        | main              | main | Required for vllm-ascend                 |
-| Python      | >= 3.9            | [3.10](https://www.python.org/downloads/) | Required for vllm                        |
-| CANN        | >= 8.0.RC2        | [8.0.RC3](https://www.hiascend.com/developer/download/community/result?module=cann&cann=8.0.0.beta1) | Required for vllm-ascend and torch-npu   |
-| torch-npu   | >= 2.4.0          | [2.5.1rc1](https://gitee.com/ascend/pytorch/releases/tag/v6.0.0.alpha001-pytorch2.5.1)    | Required for vllm-ascend                 |
-| torch       | >= 2.4.0          | [2.5.1](https://github.com/pytorch/pytorch/releases/tag/v2.5.1)      | Required for torch-npu and vllm |
+### Prepare Environment
+
+You can use the container image directly with one line command:
+
+```bash
+DEVICE=/dev/davinci7
+IMAGE=quay.io/ascend/cann:8.0.rc3.beta1-910b-ubuntu22.04-py3.10
+docker run \
+    --name $NAME --device vllm-ascend-env \
+    --device /dev/davinci_manager --device /dev/devmm_svm --device /dev/hisi_hdc \
+    -v /usr/local/dcmi:/usr/local/dcmi -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
+    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
+    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
+    -v /etc/ascend_install.info:/etc/ascend_install.info \
+    -it --rm $IMAGE bash
+```
+
+You can verify by running below commands in above container shell:
+
+```bash
+npu-smi info
+```
+
+You will see following message:
+
+```
++-------------------------------------------------------------------------------------------+
+| npu-smi 23.0.2              Version: 23.0.2                                               |
++----------------------+---------------+----------------------------------------------------+
+| NPU   Name           | Health        | Power(W)    Temp(C)           Hugepages-Usage(page)|
+| Chip                 | Bus-Id        | AICore(%)   Memory-Usage(MB)  HBM-Usage(MB)        |
++======================+===============+====================================================+
+| 0     xxx            | OK            | 0.0         40                0    / 0             |
+| 0                    | 0000:C1:00.0  | 0           882  / 15169      0    / 32768         |
++======================+===============+====================================================+
+```
+
+
+## 2. Installation
+
+Prepare:
+
+```bash
+apt update
+apt install git curl vim -y
+# Config pypi mirror to speedup
+pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
+```
+
+Create your venv
+
+```bash
+python3 -m venv .venv
+source .venv/bin/activate
+```
+
+You can install vLLM and vllm-ascend plugin by using:
+
+```bash
+# Install vLLM main branch (About 5 mins)
+git clone --depth 1 https://github.com/vllm-project/vllm.git
+cd vllm
+VLLM_TARGET_DEVICE=empty pip install .
+cd ..
+
+# Install vLLM Ascend Plugin:
+git clone --depth 1 https://github.com/vllm-project/vllm-ascend.git
+cd vllm-ascend
+pip install -e .
+cd ..
+```
+
+
+## 3. Usage
+
+After vLLM and vLLM Ascend plugin installation, you can start to
+try [vLLM QuickStart](https://docs.vllm.ai/en/latest/getting_started/quickstart.html).
+
+You have two ways to start vLLM on Ascend NPU:
+
+### Offline Batched Inference with vLLM
+
+With vLLM installed, you can start generating texts for list of input prompts (i.e. offline batch inferencing).
+
+```bash
+# Use Modelscope mirror to speed up download
+pip install modelscope
+export VLLM_USE_MODELSCOPE=true
+$ python3
+>>>
+```
+
+Try to execute below script to generate texts:
+
+```python
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "Hello, my name is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+# The first run will take about 1.5 mins (10 MB/s) to download models
+llm = LLM(model="Qwen/Qwen2.5-0.5B-Instruct")
+
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+
+### OpenAI Completions API with vLLM
+
+vLLM can also be deployed as a server that implements the OpenAI API protocol. Run
+the following command to start the vLLM server with the
+[Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) model:
+
+```bash
+# Use Modelscope mirror to speed up download
+pip install modelscope
+export VLLM_USE_MODELSCOPE=true
+# Deploy vLLM server (The first run will take about 1.5 mins (10 MB/s) to download models)
+vllm serve Qwen/Qwen2.5-0.5B-Instruct
+```
+
+Once your server is started, you can query in a new terminal:
+```bash
+docker exec -it vllm-ascend-env bash
+```
+
+You can query the list the models:
+```bash
+curl http://localhost:8000/v1/models | python3 -m json.tool
+```
+
+You can also query the model with input prompts:
 
-Find more about how to setup your environment in [here](docs/environment.md).
+```bash
+curl http://localhost:8000/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "Qwen/Qwen2.5-0.5B-Instruct",
+        "prompt": "Beijing is a",
+        "max_tokens": 5,
+        "temperature": 0
+    }' | python3 -m json.tool
+```