# Hardware-Accelerated Machine Learning [Experimental]

This feature allows you to use a GPU to accelerate machine learning tasks, such as Smart Search and Facial Recognition, while reducing CPU load.
As this is a new feature, it is still experimental and may not work on all systems.

:::info
You do not need to redo any machine learning jobs after enabling hardware acceleration. The acceleration device will be used for any jobs that run after enabling it.
:::

## Supported Backends

- ARM NN (Mali)
- CUDA (NVIDIA GPUs with [compute capability](https://developer.nvidia.com/cuda-gpus) 5.2 or higher)
- OpenVINO (Intel discrete GPUs such as Iris Xe and Arc)

## Limitations

- The instructions and configurations here are specific to Docker Compose. Other container engines may require different configuration.
- Only Linux and Windows (through WSL2) servers are supported.
- ARM NN is only supported on devices with Mali GPUs. Other Arm devices are not supported.
- Some models may not be compatible with certain backends. CUDA is the most reliable.

## Prerequisites

#### ARM NN

- Make sure you have the appropriate linux kernel driver installed
  - This is usually pre-installed on the device vendor's Linux images
- `/dev/mali0` must be available in the host server
  - You may confirm this by running `ls /dev` to check that it exists
- You must have the closed-source `libmali.so` firmware (possibly with an additional firmware file)
  - Where and how you can get this file depends on device and vendor, but typically, the device vendor also supplies these
  - The `hwaccel.ml.yml` file assumes the path to it is `/usr/lib/libmali.so`, so update accordingly if it is elsewhere
  - The `hwaccel.ml.yml` file assumes an additional file `/lib/firmware/mali_csffw.bin`, so update accordingly if your device's driver does not require this file
- Optional: Configure your `.env` file, see [environment variables](/docs/install/environment-variables) for ARM NN specific settings

#### CUDA

- The GPU must have compute capability 5.2 or greater.
- The server must have the official NVIDIA driver installed.
- The installed driver must be >= 545 (it must support CUDA 12.3.2).
- On Linux (except for WSL2), you also need to have [NVIDIA Container Toolkit][nvct] installed.

#### OpenVINO

- The server must have a discrete GPU, i.e. Iris Xe or Arc. Expect issues when attempting to use integrated graphics.
- Ensure the server's kernel version is new enough to use the device for hardware accceleration.

## Setup

1. If you do not already have it, download the latest [`hwaccel.ml.yml`][hw-file] file and ensure it's in the same folder as the `docker-compose.yml`.
2. In the `docker-compose.yml` under `immich-machine-learning`, uncomment the `extends` section and change `cpu` to the appropriate backend.
3. Still in `immich-machine-learning`, add one of -[armnn, cuda, openvino] to the `image` section's tag at the end of the line.
4. Redeploy the `immich-machine-learning` container with these updated settings.

#### Single Compose File

Some platforms, including Unraid and Portainer, do not support multiple Compose files as of writing. As an alternative, you can "inline" the relevant contents of the [`hwaccel.ml.yml`][hw-file] file into the `immich-machine-learning` service directly.

For example, the `cuda` section in this file is:

```yaml
deploy:
  resources:
    reservations:
      devices:
        - driver: nvidia
          count: 1
          capabilities:
            - gpu
```

You can add this to the `immich-machine-learning` service instead of extending from `hwaccel.ml.yml`:

```yaml
immich-machine-learning:
  container_name: immich_machine_learning
  # Note the `-cuda` at the end
  image: ghcr.io/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}-cuda
  # Note the lack of an `extends` section
  deploy:
    resources:
      reservations:
        devices:
          - driver: nvidia
            count: 1
            capabilities:
              - gpu
  volumes:
    - model-cache:/cache
  env_file:
    - .env
  restart: always
```

Once this is done, you can redeploy the `immich-machine-learning` container.

:::info
You can confirm the device is being recognized and used by checking its utilization (via `nvtop` for CUDA, `intel_gpu_top` for OpenVINO, etc.). You can also enable debug logging by setting `IMMICH_LOG_LEVEL=debug` in the `.env` file and restarting the `immich-machine-learning` container. When a Smart Search or Face Detection job begins, you should see a log for `Available ORT providers` containing the relevant provider. In the case of ARM NN, the absence of a `Could not load ANN shared libraries` log entry means it loaded successfully.
:::

[hw-file]: https://github.com/immich-app/immich/releases/latest/download/hwaccel.ml.yml
[nvct]: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html

## Tips

- If you encounter an error when a model is running, try a different model to see if the issue is model-specific.
- You may want to increase concurrency past the default for higher utilization. However, keep in mind that this will also increase VRAM consumption.
- Larger models benefit more from hardware acceleration, if you have the VRAM for them.