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Update Nvidia Hwaccel Docs (#5172)
* Update hardware_acceleration.md * Update hardware_acceleration.md * Update hardware_acceleration.md * Update hardware_acceleration.md * Update docs/docs/configuration/hardware_acceleration.md Co-authored-by: Felipe Santos <felipecassiors@gmail.com> Co-authored-by: Felipe Santos <felipecassiors@gmail.com>
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@ -45,8 +45,10 @@ ffmpeg:
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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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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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Additional configuration is needed for the docker container to be able to access the Nvidia GPU and this depends on how docker is being run:
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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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#### Docker Compose
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```yaml
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```yaml
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services:
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services:
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frigate:
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frigate:
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@ -62,6 +64,18 @@ services:
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capabilities: [gpu]
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capabilities: [gpu]
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```
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```
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#### Docker Run CLI
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```bash
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docker run -d \
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--name frigate \
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...
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--gpus=all \
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ghcr.io/blakeblackshear/frigate:stable
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```
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#### Setup Decoder
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The decoder you need to pass in the `hwaccel_args` will depend on the input video.
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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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A list of supported codecs (you can use `ffmpeg -decoders | grep cuvid` in the container to get a list)
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