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Docker Hardware Acceleration - NVIDIA NVENC/NVDEC

Overview​

Unmanic itself does not require hardware acceleration, but some plugins can take advantage of NVIDIA NVDEC/NVENC for faster video decoding and encoding.

For example, the Transcode Videos plugin can use NVENC/NVDEC when an NVIDIA GPU is present and correctly configured.

note

Install the NVIDIA driver first by following the Linux hardware acceleration guide.

1) Install the NVIDIA Container Toolkit​

If you intend to use Unmanic inside a Docker container, you will need to pass through the required NVIDIA devices to the container.

note

These next instructions assume you first have Docker installed. If you don't, do that first and come back.

Install the NVIDIA Container Toolkit and configure Docker to use it (sudo nvidia-ctk runtime configure --runtime=docker).

Once you have followed these steps, you can test that the Unmanic Docker container can use the NVENC hardware by running:

docker run --rm --gpus all --entrypoint="" josh5/unmanic nvidia-smi

You should see the following output:

Sun Apr 17 05:31:44 2022       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.54       Driver Version: 510.54       CUDA Version: 11.6     |
|-------------------------------+----------------------+----------------------|
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:01:00.0  On |                  N/A |
|  0%   34C    P8    N/A / 120W |    185MiB /  4096MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

2) Create the Docker container​

We can now pass the --runtime=nvidia (or runtime: nvidia in compose) as a param when creating a Docker container to have that container granted access to the NVIDIA GPU. We can also set the NVIDIA_VISIBLE_DEVICES to either the ID of a specific GPU or simply to 'all' for the container to have access to all NVIDIA GPUs on the host.

  PUID=$(id -u)
  PGID=$(id -g)

  # CONFIG_DIR - Where you settings are saved
  CONFIG_DIR=/config

  # LIBRARY_DIR - The location/locations of your library
  LIBRARY_DIR=/library

  # CACHE_DIR - A tmpfs or and folder for temporary conversion files
  CACHE_DIR=/tmp/unmanic
  
  # NVIDIA_VISIBLE_DEVICES - The GPUs that will be accessible to the container
  NVIDIA_VISIBLE_DEVICES=all

  docker run -ti --rm \
      --runtime=nvidia \
      -e NVIDIA_VISIBLE_DEVICES=${NVIDIA_VISIBLE_DEVICES} \
      -e PUID=${PUID} \
      -e PGID=${PGID} \
      -p 8888:8888 \
      -v ${CONFIG_DIR}:/config \
      -v ${LIBRARY_DIR}:/library \
      -v ${CACHE_DIR}:/tmp/unmanic \
      josh5/unmanic:latest