# Quickstart

## 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)

## Setup environment using container

```{code-block} bash
   :substitutions:

# You can change version a suitable one base on your requirement, e.g. main
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_newest_release_version|

docker run \
--name vllm-ascend \
--device /dev/davinci0 \
--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 \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```

## Usage

There are 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
export VLLM_USE_MODELSCOPE=true
```

Try to run below Python script directly or use `python3` shell 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 3-5 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
export VLLM_USE_MODELSCOPE=true
# Deploy vLLM server (The first run will take about 3-5 mins (10 MB/s) to download models)
vllm serve Qwen/Qwen2.5-0.5B-Instruct &
```

If you see log as below:

```
INFO:     Started server process [3594]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
```
Congratulations, you have successfully started the vLLM server!

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:

```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
```

vLLM is serving as background process, you can use `kill -2 $VLLM_PID` to stop the background process gracefully,
it's equal to `Ctrl-C` to stop foreground vLLM process:

```bash
ps -ef | grep "/.venv/bin/vllm serve" | grep -v grep
VLLM_PID=`ps -ef | grep "/.venv/bin/vllm serve" | grep -v grep | awk '{print $2}'`
kill -2 $VLLM_PID
```

You will see output as below:
```
INFO:     Shutting down FastAPI HTTP server.
INFO:     Shutting down
INFO:     Waiting for application shutdown.
INFO:     Application shutdown complete.
```

Finally, you can exit container by using `ctrl-D`.