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87144cd572
* Add hwaccel presets * Use hwaccel presets * Add input arg presets * Use input arg presets * Make util to clean up redundant code * Add support for output arg presets * Add tests * Update camera specific to use presets * Update hwaccel to use presets * Format files and fix tests * Rewrite tests to test record correctly * Move presets from string to list to avoid manually separating into a list * Add mjpeg cuvid decoder preset * Fix tests * Fix comment
120 lines
5.4 KiB
Markdown
120 lines
5.4 KiB
Markdown
---
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id: hardware_acceleration
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title: Hardware Acceleration
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---
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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
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### Raspberry Pi 3/4
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Ensure you increase the allocated RAM for your GPU to at least 128 (raspi-config > Performance Options > GPU Memory).
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**NOTICE**: If you are using the addon, you may need to turn off `Protection mode` for hardware acceleration.
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```yaml
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ffmpeg:
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hwaccel_args: preset-rpi-64-h264
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```
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### Intel-based CPUs (<10th Generation) via Quicksync
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```yaml
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ffmpeg:
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hwaccel_args: preset-intel-vaapi
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```
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**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 or [in the frigate.yml for HA OS users](advanced.md#environment_vars).
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### Intel-based CPUs (>=10th Generation) via Quicksync
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```yaml
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ffmpeg:
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hwaccel_args: preset-intel-qsv-h264
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```
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### AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver
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**Note:** You also need to set `LIBVA_DRIVER_NAME=radeonsi` as an environment variable on the container.
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```yaml
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ffmpeg:
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hwaccel_args: preset-amd-vaapi
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```
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### NVIDIA GPU
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[Supported Nvidia GPUs for Decoding](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new)
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These instructions are based on the [jellyfin documentation](https://jellyfin.org/docs/general/administration/hardware-acceleration.html#nvidia-hardware-acceleration-on-docker-linux)
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Add `--gpus all` to your docker run command or update your compose file.
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If you have multiple Nvidia graphic card, you can add them with their ids obtained via `nvidia-smi` command
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```yaml
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services:
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frigate:
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...
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image: blakeblackshear/frigate:stable
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deploy: # <------------- Add this section
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resources:
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reservations:
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devices:
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- driver: nvidia
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device_ids: ['0'] # this is only needed when using multiple GPUs
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count: 1 # number of GPUs
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capabilities: [gpu]
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```
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The decoder you need to pass in the `hwaccel_args` will depend on the input video.
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A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get a list)
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```shell
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V..... h263_cuvid Nvidia CUVID H263 decoder (codec h263)
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V..... h264_cuvid Nvidia CUVID H264 decoder (codec h264)
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V..... hevc_cuvid Nvidia CUVID HEVC decoder (codec hevc)
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V..... mjpeg_cuvid Nvidia CUVID MJPEG decoder (codec mjpeg)
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V..... mpeg1_cuvid Nvidia CUVID MPEG1VIDEO decoder (codec mpeg1video)
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V..... mpeg2_cuvid Nvidia CUVID MPEG2VIDEO decoder (codec mpeg2video)
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V..... mpeg4_cuvid Nvidia CUVID MPEG4 decoder (codec mpeg4)
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V..... vc1_cuvid Nvidia CUVID VC1 decoder (codec vc1)
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V..... vp8_cuvid Nvidia CUVID VP8 decoder (codec vp8)
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V..... vp9_cuvid Nvidia CUVID VP9 decoder (codec vp9)
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```
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For example, for H264 video, you'll select `preset-nvidia-h264`.
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```yaml
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ffmpeg:
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hwaccel_args: preset-nvidia-h264
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```
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If everything is working correctly, you should see a significant improvement in performance.
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Verify that hardware decoding is working by running `docker exec -it frigate nvidia-smi`, which should show the ffmpeg
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processes:
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```
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+-----------------------------------------------------------------------------+
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| NVIDIA-SMI 455.38 Driver Version: 455.38 CUDA Version: 11.1 |
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|-------------------------------+----------------------+----------------------+
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| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
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| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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| | | MIG M. |
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|===============================+======================+======================|
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| 0 GeForce GTX 166... Off | 00000000:03:00.0 Off | N/A |
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| 38% 41C P2 36W / 125W | 2082MiB / 5942MiB | 5% Default |
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| | | N/A |
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+-------------------------------+----------------------+----------------------+
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+-----------------------------------------------------------------------------+
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| Processes: |
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| GPU GI CI PID Type Process name GPU Memory |
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| ID ID Usage |
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|=============================================================================|
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| 0 N/A N/A 12737 C ffmpeg 249MiB |
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| 0 N/A N/A 12751 C ffmpeg 249MiB |
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| 0 N/A N/A 12772 C ffmpeg 249MiB |
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| 0 N/A N/A 12775 C ffmpeg 249MiB |
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| 0 N/A N/A 12800 C ffmpeg 249MiB |
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| 0 N/A N/A 12811 C ffmpeg 417MiB |
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| 0 N/A N/A 12827 C ffmpeg 417MiB |
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+-----------------------------------------------------------------------------+
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```
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