第5章 Serving and inferencing with Podman using AMD ROCm AI accelerators

Procedure

1. Open a terminal on your server host, and log in to registry.redhat.io:

   $ podman login registry.redhat.io

2. Pull the AMD ROCm image by running the following command:

   $ podman pull registry.redhat.io/rhaiis/vllm-rocm-rhel9:3.2.5

3. If your system has SELinux enabled, configure SELinux to allow device access:

   $ sudo setsebool -P container_use_devices 1

4. Create a volume and mount it into the container. Adjust the container permissions so that the container can use it.

   $ mkdir -p rhaiis-cache

   $ chmod g+rwX rhaiis-cache

5. Create or append your HF_TOKEN Hugging Face token to the private.env file. Source the private.env file.

   $ echo "export HF_TOKEN=<your_HF_token>" > private.env

   $ source private.env

6. Start the AI Inference Server container image.

   1. For AMD ROCm accelerators:

      1. Use amd-smi static -a to verify that the container can access the host system GPUs:

         $ podman run -ti --rm --pull=newer \
         --security-opt=label=disable \
         --device=/dev/kfd --device=/dev/dri \
         --group-add keep-groups \ 

         1

         
         --entrypoint="" \
         registry.redhat.io/rhaiis/vllm-rocm-rhel9:3.2.5 \
         amd-smi static -a

         1

         You must belong to both the video and render groups on AMD systems to use the GPUs. To access GPUs, you must pass the --group-add=keep-groups supplementary groups option into the container.

      2. Start the container:

         podman run --rm -it \
         --device /dev/kfd --device /dev/dri \
         --security-opt=label=disable \ 

         1

         
         --group-add keep-groups \
         --shm-size=4GB -p 8000:8000 \ 

         2

         
         --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
         --env "HF_HUB_OFFLINE=0" \
         -v ./rhaiis-cache:/opt/app-root/src/.cache \
         registry.redhat.io/rhaiis/vllm-rocm-rhel9:3.2.5 \
         --model RedHatAI/Llama-3.2-1B-Instruct-FP8 \
         --tensor-parallel-size 2 

         3

         1

         --security-opt=label=disable prevents SELinux from relabeling files in the volume mount. If you choose not to use this argument, your container might not successfully run.

         2

         If you experience an issue with shared memory, increase --shm-size to 8GB.

         3

         Set --tensor-parallel-size to match the number of GPUs when running the AI Inference Server container on multiple GPUs.

7. In a separate tab in your terminal, make a request to the model with the API.

   curl -X POST -H "Content-Type: application/json" -d '{
       "prompt": "What is the capital of France?",
       "max_tokens": 50
   }' http://<your_server_ip>:8000/v1/completions | jq

   Example output

   {
       "id": "cmpl-b84aeda1d5a4485c9cb9ed4a13072fca",
       "object": "text_completion",
       "created": 1746555421,
       "model": "RedHatAI/Llama-3.2-1B-Instruct-FP8",
       "choices": [
           {
               "index": 0,
               "text": " Paris.\nThe capital of France is Paris.",
               "logprobs": null,
               "finish_reason": "stop",
               "stop_reason": null,
               "prompt_logprobs": null
           }
       ],
       "usage": {
           "prompt_tokens": 8,
           "total_tokens": 18,
           "completion_tokens": 10,
           "prompt_tokens_details": null
       }
   }