# Use NPU 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 NPU device(s) access to the container user.

### Provide NPU access to the container
This can be done by making the following changes to the docker compose file.

```yaml
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
NPU 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.

## Tutorial on how to use NPU specific pipelines

> **Note:** This sample application already provides a default `compose-without-scenescape.yml`
> file that includes the necessary NPU access to the containers.

The pipeline `yolov11s_npu` in DLStreamer Pipeline Server's `config.json` 
contains NPU specific elements and uses NPU backend for inferencing. We can start the pipeline
as follows:

```sh
./sample_start.sh npu
```

Go to Grafana as explained in [Get Started](../get-started.md) to view the dashboard.