Container Compute Service:inference-nv-pytorch 25.05

Main features and bug fixes

Content

Assets

Driver requirements

For CUDA 12.8 images: NVIDIA driver release >= 570

For CUDA 12.4 images: NVIDIA driver release >= 550

Quick Start

The following example uses only Docker to pull the inference-nv-pytorch image and uses the Qwen2.5-7B-Instruct model to test inference services.

1. Pull the inference container image.

   docker pull egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:[tag]

2. Download an open source model in the modelscope format.

   pip install modelscope
   cd /mnt
   modelscope download --model Qwen/Qwen2.5-7B-Instruct --local_dir ./Qwen2.5-7B-Instruct

3. Run the following command to log on to the container.

   docker run -d -t --network=host --privileged --init --ipc=host \
   --ulimit memlock=-1 --ulimit stack=67108864  \
   -v /mnt/:/mnt/ \
   egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:[tag]

4. Run an inference test to test the inference conversation feature of vLLM.

   1. Start the Server service.

      python3 -m vllm.entrypoints.openai.api_server \
      --model /mnt/Qwen2.5-7B-Instruct \
      --trust-remote-code --disable-custom-all-reduce \
      --tensor-parallel-size 1

   2. Test on the client.

      curl http://localhost:8000/v1/chat/completions \
          -H "Content-Type: application/json" \
          -d '{
          "model": "/mnt/Qwen2.5-7B-Instruct",  
          "messages": [
          {"role": "system", "content": "You are a friendly AI assistant."},
          {"role": "user", "content": "Please introduce deep learning."}
          ]}'

      For more information about how to work with vLLM, see vLLM.

Known issues

None