Chapter 5. 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

   Copy to Clipboard Toggle word wrap

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

   $ podman pull registry.redhat.io/rhaiis/vllm-rocm-rhel9:3.3.0

   Copy to Clipboard Toggle word wrap

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

   $ sudo setsebool -P container_use_devices 1

   Copy to Clipboard Toggle word wrap

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

   Copy to Clipboard Toggle word wrap

   $ chmod g+rwX rhaiis-cache

   Copy to Clipboard Toggle word wrap

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

   Copy to Clipboard Toggle word wrap

   $ source private.env

   Copy to Clipboard Toggle word wrap

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 \
         --entrypoint="" \
         registry.redhat.io/rhaiis/vllm-rocm-rhel9:3.3.0 \
         amd-smi static -a

         Copy to Clipboard Toggle word wrap

         Where:

         --group-add keep-groups

         Preserves the supplementary groups from the host user. On AMD systems, you must belong to both the video and render groups to access GPUs.

      2. Start the container:

         podman run --rm -it \
         --device /dev/kfd --device /dev/dri \
         --security-opt=label=disable \
         --group-add keep-groups \
         --shm-size=4GB -p 8000:8000 \
         --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-rhel9:3.3.0 \
         --model RedHatAI/Llama-3.2-1B-Instruct-FP8 \
         --tensor-parallel-size 2

         Copy to Clipboard Toggle word wrap

         Where:

         --security-opt=label=disable

         Disables SELinux label relabeling for volume mounts. Without this option, the container might fail to start.

         --shm-size=4GB -p 8000:8000

         Specifies the shared memory size and port mapping. Increase --shm-size to 8GB if you experience shared memory issues.

         --tensor-parallel-size 2

         Specifies the number of GPUs to use for tensor parallelism. Set this value to match the number of available GPUs.

Verification

1. 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

   Copy to Clipboard Toggle word wrap

   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
       }
   }

   Copy to Clipboard Toggle word wrap