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Update docs
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Andrew Reiter
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@ -21,8 +21,8 @@ See [the hwaccel docs](/configuration/hardware_acceleration.md) for more info on
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| preset-nvidia-h264 | Nvidia GPU with h264 stream | |
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| preset-nvidia-h265 | Nvidia GPU with h265 stream | |
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| preset-nvidia-mjpeg | Nvidia GPU with mjpeg stream | Recommend restreaming mjpeg and using nvidia-h264 |
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| preset-jetson-h264 | Nvidia Jetson with h264 stream | |
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| preset-jetson-h265 | Nvidia Jetson with h265 stream | |
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| preset-jetson-h264 | Nvidia Jetson with h264 stream | |
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| preset-jetson-h265 | Nvidia Jetson with h265 stream | |
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### Input Args Presets
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@ -250,10 +250,14 @@ These instructions were originally based on the [Jellyfin documentation](https:/
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## NVIDIA Jetson (Orin AGX, Orin NX, Orin Nano*, Xavier AGX, Xavier NX, TX2, TX1, Nano)
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A separate set of docker images is available that is based on Jetpack/L4T. They comes with an `ffmpeg` build
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with codecs that use the Jetson's dedicated media engine. Use the `frigate-tensorrt-jp*` image with the nvidia container
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runtime:
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with codecs that use the Jetson's dedicated media engine. If your Jetson host is running Jetpack 4.6, use the
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`frigate-tensorrt-jp4` image, or if your Jetson host is running Jetpack 5.0+, use the `frigate-tensorrt-jp5`
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image. Note that the Orin Nano has no video encoder, so frigate will use software encoding on this platform,
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but the image will still allow hardware decoding and tensorrt object detection.
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#### Docker Run CLI
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You will need to use the image with the nvidia container runtime:
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### Docker Run CLI - Jetson
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```bash
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docker run -d \
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@ -262,7 +266,34 @@ docker run -d \
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ghcr.io/blakeblackshear/frigate-tensorrt-jp5
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```
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If your Jetson host is running Jetpack 4.6, use the `frigate-tensorrt-jp4` image, or if your Jetson host is running Jetpack 5.0+, use the `frigate-tensorrt-jp5` image. Note that the Orin Nano has no video encoder, so frigate will use software encoding on this platform, but the image will still allow hardware decoding and tensorrt object detection.
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### Docker Compose - Jetson
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```yaml
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version: '2.4'
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services:
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frigate:
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...
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image: ghcr.io/blakeblackshear/frigate-tensorrt-jp5
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runtime: nvidia # Add this
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```
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:::note
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The `runtime:` tag is not supported on older versions of docker-compose. If you run into this, you can instead use the nvidia runtime system-wide by adding `"default-runtime": "nvidia"` to `/etc/docker/daemon.json`:
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```
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{
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"runtimes": {
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"nvidia": {
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"path": "nvidia-container-runtime",
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"runtimeArgs": []
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}
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},
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"default-runtime": "nvidia"
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}
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```
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:::
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### Setup Decoder
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@ -70,7 +70,7 @@ Inference speeds vary greatly depending on the CPU, GPU, or VPU used, some known
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| Intel i5 1135G7 | 10 - 15 ms | |
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| Intel i5 12600K | ~ 15 ms | Inference speeds on CPU were ~ 35 ms |
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### TensorRT
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### TensorRT - Nvidia GPU
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The TensortRT detector is able to run on x86 hosts that have an Nvidia GPU which supports the 12.x series of CUDA libraries. The minimum driver version on the host system must be `>=525.60.13`. Also the GPU must support a Compute Capability of `5.0` or greater. This generally correlates to a Maxwell-era GPU or newer, check the [TensorRT docs for more info](/configuration/object_detectors#nvidia-tensorrt-detector).
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@ -87,6 +87,14 @@ Inference speeds will vary greatly depending on the GPU and the model used.
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| Quadro P400 2GB | 20 - 25 ms |
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| Quadro P2000 | ~ 12 ms |
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### Community Supported:
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#### Nvidia Jetson
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Frigate supports all Jetson boards, from the inexpensive Jetson Nano to the powerful Jetson Orin AGX. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
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Inference speed will vary depending on the YOLO model, jetson platform and jetson nvpmodel (GPU/DLA/EMC clock speed). It is typically ~20ms for `tiny` models and ~200ms for their large variants. The DLA is more efficient than the GPU, but not faster, so using the DLA will reduce power consumption but will slightly increase inference time.
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## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
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This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.
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@ -93,6 +93,9 @@ The following officially supported builds are available:
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The following community supported builds are available:
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`ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp5` - Frigate build optimized for nvidia Jetson devices running Jetpack 5
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`ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp4` - Frigate build optimized for nvidia Jetson devices running Jetpack 4.6
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:::
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```yaml
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