[Doc] Add install doc

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

Author: wangxiyuan

README.md

@@ -39,7 +39,7 @@ By using vLLM Ascend plugin, popular open-source models, including Transformer-l
   * PyTorch >= 2.4.0, torch-npu >= 2.4.0
   * vLLM (the same version as vllm-ascend)
 
-Find more about how to setup your environment step by step in [here](docs/installation.md).
+Find more about how to setup your environment step by step in [here](docs/source/installation.md).
 
 ## Getting Started
 
@@ -68,7 +68,7 @@ Run the following command to start the vLLM server with the [Qwen/Qwen2.5-0.5B-I
 vllm serve Qwen/Qwen2.5-0.5B-Instruct
 curl http://localhost:8000/v1/models
 ```
-**Please refer to [official docs](./docs/index.md) for more details.**
+**Please refer to [official docs](https://vllm-ascend.readthedocs.io/en/latest/) for more details.**
 
 ## Contributing
 See [CONTRIBUTING](docs/source/developer_guide/contributing.md) for more details, which is a step-by-step guide to help you set up development environment, build and test.

README.zh.md

@@ -39,7 +39,7 @@ vLLM 昇腾插件 (`vllm-ascend`) 是一个让vLLM在Ascend NPU无缝运行的
   * PyTorch >= 2.4.0, torch-npu >= 2.4.0
   * vLLM (与vllm-ascend版本一致)
 
-在[此处](docs/installation.md)，您可以了解如何逐步准备环境。
+在[此处](docs/source/installation.md)，您可以了解如何逐步准备环境。
 
 ## 开始使用
 
@@ -69,7 +69,7 @@ vllm serve Qwen/Qwen2.5-0.5B-Instruct
 curl http://localhost:8000/v1/models
 ```
 
-**请参阅 [官方文档](./docs/index.md)以获取更多详细信息**
+**请参阅 [官方文档](https://vllm-ascend.readthedocs.io/en/latest/)以获取更多详细信息**
 
 ## 贡献
 有关更多详细信息，请参阅 [CONTRIBUTING](docs/source/developer_guide/contributing.zh.md)，可以更详细的帮助您部署开发环境、构建和测试。

docs/source/installation.md

@@ -1,22 +1,49 @@
 # Installation
 
-## Dependencies
-| Requirement  | Supported version | Recommended version | Note |
-| ------------ | ------- | ----------- | ----------- | 
-| 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 required |
+This document describes how to install vllm-ascend manually.
 
-## Prepare Ascend NPU environment
+## Requirements
 
-Below is a quick note to install recommended version software:
+- OS: Linux
+- Python: 3.10 or higher
+- A hardware with Ascend NPU. It's usually the Atlas 800 A2 series.
+- Software:
 
-### Containerized installation
+    | Software     | Supported version | Note |
+    | ------------ | ----------------- | ---- | 
+    | CANN         | >= 8.0.0.beta1    | Required for vllm-ascend and torch-npu |
+    | torch-npu    | >= 2.5.1rc1       | Required for vllm-ascend |
+    | torch        | >= 2.5.1          | Required for torch-npu and vllm |
 
-You can use the [container image](https://hub.docker.com/r/ascendai/cann) directly with one line command:
+## Configure a new environment
+
+Before installing the package, you need to make sure that the firmware and driver for NPU is installed correctly. i.e. `npu-smi` command is available.
+
+> Tips: following the instructions provided in the [Ascend Installation Guide](https://ascend.github.io/docs/sources/ascend/quick_install.html) can help you to set up the environment easily.
+
+Once it's done, you can choice either **Set up using Python** or **Set up using Docker** section to install and use vllm-ascend.
+
+If you want to install vllm-ascend in local bare environment by hand, you need install CANN first, otherwise, you can skip this step.
 
 ```bash
+# Create a virtual environment
+python -m venv vllm-ascend-env
+source vllm-ascend-env/bin/activate
+# Install python packages.
+pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple attrs numpy==1.24.0 decorator sympy cffi pyyaml pathlib2 psutil protobuf scipy requests absl-py wheel typing_extensions
+# Download and install the CANN package from the official website.
+wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.0.0/Ascend-cann-toolkit_8.0.0_linux-aarch64.run
+sh Ascend-cann-toolkit_8.0.0_linux-aarch64.run --full
+wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.0.0/Ascend-cann-kernels-910b_8.0.0_linux-aarch64.run
+sh Ascend-cann-kernels-910b_8.0.0_linux-aarch64.run --full
+```
+
+## Set up using Python
+
+First of all, make sure you have an environment with CANN installed. It can be done by **Configure a new environment** step. Or by using an CANN container directly.
+
+```
+# Setup a CANN container using docker
 docker run \
     --name vllm-ascend-env \
     --device /dev/davinci1 \
@@ -28,28 +55,113 @@ docker run \
     -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 quay.io/ascend/cann:8.0.rc3.beta1-910b-ubuntu22.04-py3.10 bash
+    -it quay.io/ascend/cann:8.0.0.beta1-910b-ubuntu22.04-py3.10 bash
 ```
 
-You do not need to install `torch` and `torch_npu` manually, they will be automatically installed as `vllm-ascend` dependencies.
+Then you need to install vllm from souce code.
 
-### Manual installation
+```bash
+git clone https://github.com/vllm-project/vllm
+cd vllm
+VLLM_TARGET_DEVICE=empty python setup.py install
+```
 
-Or follow the instructions provided in the [Ascend Installation Guide](https://ascend.github.io/docs/sources/ascend/quick_install.html) to set up the environment.
+Then you can install vllm-ascend pre-built wheel or from source code.
 
-## Building
+### Pre-built wheels (Not support yet)
 
-### Build Python package from source
+```bash
+pip install vllm-ascend -f https://download.pytorch.org/whl/torch/
+```
+
+### Build wheel from source
 
 ```bash
 git clone https://github.com/vllm-project/vllm-ascend.git
 cd vllm-ascend
-pip install -e .
+pip install -e . -f https://download.pytorch.org/whl/torch/
+```
+
+## Set up using Docker
+
+> Tips: CANN, torch, torch_npu, vllm and vllm_ascend are pre-installed in the Docker image already.
+
+### Pre-built images (Not support yet)
+
+Just pull the image and run it with bash.
+
+```bash
+docker pull quay.io/ascend/vllm-ascend:latest
+
+docker run \
+    --name vllm-ascend-env \
+    --device /dev/davinci1 \
+    --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 quay.io/ascend/vllm-ascend:0.7.1rc1 bash
 ```
 
-### Build container image from source
+### Build image from source
+
+If you want to build the docker image from main branch, you can do it by following steps:
+
 ```bash
 git clone https://github.com/vllm-project/vllm-ascend.git
 cd vllm-ascend
-docker build -t vllm-ascend-dev-image -f ./Dockerfile .
+
+docker build -t vllm-ascend-dev-image:latest -f ./Dockerfile .
+
+docker run \
+    --name vllm-ascend-env \
+    --device /dev/davinci1 \
+    --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 vllm-ascend-dev-image:latest bash
+```
+
+## Extra information
+
+### Verify installation
+
+Create and Run a simple inference test. Then `example.py` is:
+
+```
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+
+# Create a sampling params object.
+sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
+# Create an LLM.
+llm = LLM(model="facebook/opt-125m")
+
+# Generate texts from the prompts.
+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}")
+```
+
+Then run:
+```bash
+# export VLLM_USE_MODELSCOPE=true to speed up download if huggingface is not reachable.
+python example.py
 ```