Hardware Acceleration

It is recommended to update your configuration to enable hardware accelerated decoding in ffmpeg. Depending on your system, these parameters may not be compatible. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro

Raspberry Pi 3/4#

Ensure you increase the allocated RAM for your GPU to at least 128 (raspi-config > Performance Options > GPU Memory). NOTICE: If you are using the addon, you may need to turn off Protection mode for hardware acceleration.

ffmpeg:
  hwaccel_args: -c:v h264_v4l2m2m

Intel-based CPUs (<10th Generation) via Quicksync#

ffmpeg:
  hwaccel_args: -hwaccel vaapi -hwaccel_device /dev/dri/renderD128 -hwaccel_output_format yuv420p

NOTICE: With some of the processors, like the J4125, the default driver iHD doesn't seem to work correctly for hardware acceleration. You may need to change the driver to i965 by adding the following environment variable LIBVA_DRIVER_NAME=i965 to your docker-compose file.

Intel-based CPUs (>=10th Generation) via Quicksync#

ffmpeg:
  hwaccel_args: -c:v h264_qsv

AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver#

Note: You also need to set LIBVA_DRIVER_NAME=radeonsi as an environment variable on the container.

ffmpeg:
  hwaccel_args: -hwaccel vaapi -hwaccel_device /dev/dri/renderD128 -hwaccel_output_format yuv420p

NVIDIA GPU#

Supported Nvidia GPUs for Decoding

These instructions are based on the jellyfin documentation

Add --gpus all to your docker run command or update your compose file. If you have multiple Nvidia graphic card, you can add them with their ids obtained via nvidia-smi command

services:
  frigate:
    ...
    image: blakeblackshear/frigate:stable
    deploy:    # <------------- Add this section
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids: ['0'] # this is only needed when using multiple GPUs
              capabilities: [gpu]

The decoder you need to pass in the hwaccel_args will depend on the input video.

A list of supported codecs (you can use ffmpeg -decoders | grep cuvid in the container to get a list)

 V..... h263_cuvid           Nvidia CUVID H263 decoder (codec h263)
 V..... h264_cuvid           Nvidia CUVID H264 decoder (codec h264)
 V..... hevc_cuvid           Nvidia CUVID HEVC decoder (codec hevc)
 V..... mjpeg_cuvid          Nvidia CUVID MJPEG decoder (codec mjpeg)
 V..... mpeg1_cuvid          Nvidia CUVID MPEG1VIDEO decoder (codec mpeg1video)
 V..... mpeg2_cuvid          Nvidia CUVID MPEG2VIDEO decoder (codec mpeg2video)
 V..... mpeg4_cuvid          Nvidia CUVID MPEG4 decoder (codec mpeg4)
 V..... vc1_cuvid            Nvidia CUVID VC1 decoder (codec vc1)
 V..... vp8_cuvid            Nvidia CUVID VP8 decoder (codec vp8)
 V..... vp9_cuvid            Nvidia CUVID VP9 decoder (codec vp9)

For example, for H264 video, you'll select h264_cuvid.

ffmpeg:
  hwaccel_args: -c:v h264_cuvid

If everything is working correctly, you should see a significant improvement in performance. Verify that hardware decoding is working by running docker exec -it frigate nvidia-smi, which should show the ffmpeg processes:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 455.38       Driver Version: 455.38       CUDA Version: 11.1     |
|-------------------------------+----------------------+----------------------+
| 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  GeForce GTX 166...  Off  | 00000000:03:00.0 Off |                  N/A |
| 38%   41C    P2    36W / 125W |   2082MiB /  5942MiB |      5%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A     12737      C   ffmpeg                            249MiB |
|    0   N/A  N/A     12751      C   ffmpeg                            249MiB |
|    0   N/A  N/A     12772      C   ffmpeg                            249MiB |
|    0   N/A  N/A     12775      C   ffmpeg                            249MiB |
|    0   N/A  N/A     12800      C   ffmpeg                            249MiB |
|    0   N/A  N/A     12811      C   ffmpeg                            417MiB |
|    0   N/A  N/A     12827      C   ffmpeg                            417MiB |
+-----------------------------------------------------------------------------+