How to use GPU for inference

Pre-requisites

In order to benefit from hardware acceleration, pipelines can be constructed in a manner that different stages such as decoding, inference etc., can make use of these devices. For containerized applications built using the DL Streamer Pipeline Server, first we need to provide GPU device(s) access to the container user.

Provide GPU access to the container

This can be done by making the following changes to the docker compose file.

services:
  dlstreamer-pipeline-server:
    group_add:
      # render group ID for ubuntu 22.04 host OS
      - "110"
      # render group ID for ubuntu 24.04 host OS
      - "992"
    devices:
      # you can add specific devices in case you don't want to provide access to all like below.
      - "/dev:/dev"

The changes above adds the container user to the render group and provides access to the GPU devices.

Hardware specific encoder/decoders

Unlike the changes done for the container above, the following requires a modification to the media pipeline itself.

Gstreamer has a variety of hardware specific encoders and decoders elements such as Intel specific VA-API elements that you can benefit from by adding them into your media pipeline. Examples of such elements are vah264dec, vah264enc, vajpegdec, vajpegdec, etc.

Additionally, one can also enforce zero-copy of buffers using GStreamer caps (capabilities) to the pipeline by adding video/x-raw(memory: VAMemory) for Intel GPUs (integrated and discrete).

Read DL Streamer docs for more details.

GPU specific element properties

DL Streamer inference elements also provides property such as device=GPU and pre-process-backend=va-surface-sharing to infer and pre-process on GPU. Read DL Streamer docs for more.

Tutorial on how to use GPU specific pipelines

Note - This sample application already provides a default docker-compose.yml file that includes the necessary GPU access to the containers.

The pipeline worker_safety_gear_detection_gpu in pipeline-server-config contains GPU specific elements and uses GPU backend for inferencing. We will now start the pipeline with a curl request.

curl http://<HOST_IP>:8080/pipelines/user_defined_pipelines/worker_safety_gear_detection_gpu -X POST -H 'Content-Type: application/json' -d '{
    "source": {
        "uri": "file:///home/pipeline-server/resources/videos/Safety_Full_Hat_and_Vest.avi",
        "type": "uri"
    },
    "destination": {
        "metadata": {
            "type": "file",
            "path": "/tmp/results.jsonl",
            "format": "json-lines"
        }
    },
    "parameters": {
        "detection-properties": {
            "model": "/home/pipeline-server/resources/models/worker-safety-gear-detection/deployment/Detection/model/model.xml"
        }
    }
}'

We should see the metadata results in /tmp/results.jsonl file.