inference-nv-pytorch 26.03

Main features and bug fixes

Contents

Quick start

The following example shows how to pull the inference-nv-pytorch image using Docker and test the inference service with the Qwen2.5-7B-Instruct model.

1. Pull the inference container image.

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

2. Download the open-source model from ModelScope.

   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 enter the container.

   docker run -it --rm --gpus all --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 verify the vLLM chat completion feature.

   1. Start the server-side 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. Run a test on the client side.

      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": "Introduce deep learning."}
          ]}'

      Output:

      {"id":"chat-d3c28759793d4376a65bfc4e40b59a71","object":"chat.completion","created":1735278194,"model":"/mnt/deep_learning_test/testsuite/dataset/llms_inference_qwen7b-v2.5_accelerate/checkpoint/7B-V2.5/","choices":[{"index":0,"message":{"role":"assistant","content":"Deep learning is a subset of machine learning inspired by the biological nervous system, particularly the interaction between neurons in the brain. Deep learning utilizes deep neural networks to process and analyze large amounts of data to identify effective predictive models. This technology has achieved significant success in various tasks, including image recognition, speech recognition, and natural language processing.\n\nIn deep learning, a neural network consists of multiple layers, including an input layer, several hidden layers, and an output layer. Each layer contains multiple nodes (or neurons) connected to nodes in other layers through weighted connections. During training, the neural network adjusts the weights of these connections based on the input data to minimize the error between the predicted output and the actual output. This process is typically achieved using optimization algorithms such as gradient descent.\n\nThe training of deep learning models requires substantial computational resources and data. In recent years, with advances in computing hardware (such as GPUs and TPUs) and the rapid growth of datasets, deep learning technology has been widely applied and developed. In addition to the applications mentioned above, deep learning is also extensively used in medical diagnosis, autonomous driving, gaming, and finance.","tool_calls":[]},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":237,"completion_tokens":213}}

      For more information, see the vLLM documentation.

Known issues

• This image does not support the deepgpu-comfyui plugin.