Container Compute Service:inference-nv-pytorch 25.02

Public image

egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:25.02-vllm0.7.2-sglang0.4.3.post2-pytorch2.5-cuda12.4-20250305-serverless

VPC image

acs-registry-vpc.{region-id}.cr.aliyuncs.com/egslingjun/{image:tag}

Replace {region-id} with the region where your ACS is activated (for example, cn-beijing), and {image:tag} with the image name and tag.

Image compatibility

Two image variants are available. Choose based on your deployment target:

Prerequisites

• Docker installed and running

• NVIDIA Driver release >= 550

Run an inference test

1. Pull the container image.

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

   Replace [tag] with the image tag for your target deployment (see Image compatibility).

2. Download the Qwen2.5-7B-Instruct model from ModelScope.

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

3. Start 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. Start the vLLM server inside the container.

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

5. Send a test request to the server.

   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 vLLM, see the vLLM documentation.