mirror of
https://github.com/blakeblackshear/frigate.git
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Merge 243172fbf4 into 9b02c7318d
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xt-jorge
@@ -20,7 +20,7 @@ Object detection and enrichments (like Semantic Search, Face Recognition, and Li
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- **Nvidia**
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- Nvidia GPUs will automatically be detected and used for enrichments in the `-tensorrt` Frigate image.
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- Jetson devices will automatically be detected and used for enrichments in the `-tensorrt-jp6` Frigate image.
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- Jetson devices will automatically be detected and used for enrichments in the `-tensorrt-jp6` Frigate image on JetPack 6 / L4T R36. JetPack 7 enrichment acceleration is documented after separate hardware validation.
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- **RockChip**
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- RockChip NPU will automatically be detected and used for semantic search v1 and face recognition in the `-rk` Frigate image.
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@@ -359,7 +359,7 @@ Or map in all the `/dev/video*` devices.
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## NVIDIA Jetson
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A separate set of docker images is available for Jetson devices. They come with an `ffmpeg` build with codecs that use the Jetson's dedicated media engine. If your Jetson host is running Jetpack 6.0+ use the `stable-tensorrt-jp6` tagged 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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A separate set of docker images is available for Jetson devices. They come with an `ffmpeg` build with codecs that use the Jetson's dedicated media engine. If your Jetson host is running JetPack 6 / L4T R36, use the `stable-tensorrt-jp6` tagged image. If your Jetson host is running JetPack 7.2 / L4T R39.2, use the `stable-tensorrt-jp7` tagged image. Note that the Orin Nano has no video encoder, so Frigate will use software encoding on this platform, but the Jetson TensorRT images will still allow hardware decoding and ONNX detector GPU acceleration.
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You will need to use the image with the nvidia container runtime:
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@@ -369,7 +369,7 @@ You will need to use the image with the nvidia container runtime:
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docker run -d \
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...
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--runtime nvidia
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ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6
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ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp7
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```
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### Docker Compose - Jetson
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@@ -378,10 +378,12 @@ docker run -d \
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services:
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frigate:
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...
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image: ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6
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image: ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp7
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runtime: nvidia # Add this
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```
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Use `stable-tensorrt-jp6` instead when the Jetson host is running JetPack 6 / L4T R36. JetPack 7.0/7.1 on L4T R38.x are not covered by the initial `stable-tensorrt-jp7` guidance unless separately validated.
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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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@@ -46,8 +46,8 @@ Frigate supports multiple different detectors that work on different types of ha
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**Nvidia Jetson** <CommunityBadge />
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- [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Jetson devices, using one of many default models.
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- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt-jp6` Frigate image when a supported ONNX model is configured.
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- [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on JetPack 6 / L4T R36 Jetson devices, using one of many default models.
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- [ONNX](#onnx): Jetson GPU acceleration will automatically be detected and used by the ONNX detector in the `-tensorrt-jp6` image on JetPack 6 / L4T R36 or the `-tensorrt-jp7` image on JetPack 7.2 / L4T R39.2 when a supported ONNX model is configured.
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**Rockchip** <CommunityBadge />
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@@ -437,7 +437,7 @@ If the correct build is used for your GPU then the GPU will be detected and used
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- **Nvidia**
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- Nvidia GPUs will automatically be detected and used with the ONNX detector in the `-tensorrt` Frigate image.
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- Jetson devices will automatically be detected and used with the ONNX detector in the `-tensorrt-jp6` Frigate image.
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- Jetson devices will automatically be detected and used with the ONNX detector in the `-tensorrt-jp6` Frigate image on JetPack 6 / L4T R36 or the `-tensorrt-jp7` Frigate image on JetPack 7.2 / L4T R39.2.
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:::
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@@ -567,7 +567,9 @@ For detailed instructions on compiling models, refer to the [MemryX Compiler](ht
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## NVidia TensorRT Detector
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Nvidia Jetson devices may be used for object detection using the TensorRT libraries. Due to the size of the additional libraries, this detector is only provided in images with the `-tensorrt-jp6` tag suffix, e.g. `ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6`. This detector is designed to work with Yolo models for object detection.
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Nvidia Jetson devices may be used for object detection using the TensorRT libraries on JetPack 6 / L4T R36. Due to the size of the additional libraries, this detector is only provided in images with the `-tensorrt-jp6` tag suffix, e.g. `ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6`. This detector is designed to work with Yolo models for object detection.
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On JetPack 7.2 / L4T R39.2, the `-tensorrt-jp7` image initially supports ONNX detector GPU acceleration. Native `type: tensorrt` engine generation on JP7 is pending hardware validation.
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### Generate Models
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@@ -90,7 +90,7 @@ Frigate supports multiple different detectors that work on different types of ha
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- [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx)
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- Runs well with any size models including large
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- <CommunityBadge /> [Jetson](#nvidia-jetson): Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6.
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- <CommunityBadge /> [Jetson](#nvidia-jetson): Jetson devices are supported via the TensorRT or ONNX detectors when running JetPack 6 / L4T R36. On JetPack 7.2 / L4T R39.2, the initial supported path is ONNX detector GPU acceleration.
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**Rockchip** <CommunityBadge />
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@@ -258,9 +258,9 @@ Inference speeds may vary depending on the host platform. The above data was mea
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### Nvidia Jetson
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Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson) 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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Jetson devices running JetPack 6 / L4T R36 are supported via the TensorRT or ONNX detectors. Jetson devices running JetPack 7.2 / L4T R39.2 use the `stable-tensorrt-jp7` image and initially support ONNX detector GPU acceleration; native `type: tensorrt` engine generation on JP7 is pending hardware validation. The Jetson images will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets). The JetPack 6 image will also 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 20-40 ms for most models. 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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For JetPack 6 TensorRT detector usage, inference speed will vary depending on the YOLO model, jetson platform and jetson nvpmodel (GPU/DLA/EMC clock speed). It is typically 20-40 ms for most models. 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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### Rockchip platform
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@@ -556,7 +556,8 @@ The official docker image tags for the current stable version are:
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The community supported docker image tags for the current stable version are:
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- `stable-tensorrt-jp6` - Frigate build optimized for Nvidia Jetson devices running Jetpack 6
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- `stable-tensorrt-jp6` - Frigate build optimized for Nvidia Jetson devices running JetPack 6 / L4T R36
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- `stable-tensorrt-jp7` - Frigate build optimized for Nvidia Jetson devices running JetPack 7.2 / L4T R39.2
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- `stable-rk` - Frigate build for SBCs with Rockchip SoC
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## Home Assistant App
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@@ -35,9 +35,10 @@ devices:
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helpType: "warning"
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needsNvidiaConfig: true
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- id: "stable-tensorrt-jp6"
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name: "NVIDIA Jetson"
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description: "Jetson development board"
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- &jetsonDevice
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id: "stable-tensorrt-jp6"
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name: "NVIDIA Jetson JP6"
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description: "JetPack 6 / L4T R36"
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icon: '<svg viewBox="0 0 32 33" xmlns="http://www.w3.org/2000/svg"><path d="M11.6925 17.7697V15.8762C11.8738 15.8633 12.0563 15.8528 12.2422 15.847C17.3372 15.6837 20.68 20.2978 20.68 20.2978C20.68 20.2978 17.0699 25.3962 13.1992 25.3962C12.6415 25.3962 12.1423 25.3052 11.6925 25.1511V19.4077C13.6766 19.6515 14.0748 20.5417 15.2671 22.5623L17.919 20.2885C17.919 20.2885 15.9831 17.7067 12.7195 17.7067C12.3649 17.7067 12.0253 17.7323 11.6913 17.7685L11.6925 17.7697ZM11.6913 11.5128V14.342C11.8738 14.3268 12.0574 14.3152 12.241 14.3082C19.3259 14.0655 23.9425 20.2162 23.9425 20.2162C23.9425 20.2162 18.6408 26.7705 13.1166 26.7705C12.6105 26.7705 12.1366 26.7227 11.6913 26.6433V28.3922C12.0723 28.4412 12.4671 28.4703 12.8779 28.4703C18.0177 28.4703 21.7358 25.8021 25.3356 22.6428C25.9323 23.1281 28.3754 24.31 28.8781 24.828C25.4549 27.7412 17.4795 30.0885 12.9571 30.0885C12.521 30.0885 12.1022 30.0617 11.6913 30.022V32.479H31.2282V11.514H11.6925L11.6913 11.5128ZM11.6913 25.15V26.6433C6.93708 25.7812 5.6174 20.7575 5.6174 20.7575C5.6174 20.7575 7.89986 18.1862 11.6913 17.7697V19.4077C11.6913 19.4077 11.6867 19.4077 11.6845 19.4077C9.69462 19.165 8.14085 21.055 8.14085 21.055C8.14085 21.055 9.01183 24.2365 11.6925 25.1523L11.6913 25.15ZM3.24888 20.5417C3.24888 20.5417 6.06609 16.3148 11.6925 15.8773V14.3443C5.46134 14.853 0.0644531 20.2185 0.0644531 20.2185C0.0644531 20.2185 3.12036 29.2018 11.6925 30.0243V28.3945C5.40167 27.5895 3.24888 20.5417 3.24888 20.5417Z" fill="#76B900"></path></svg>'
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svgStyle:
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width: 50px
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@@ -46,10 +47,17 @@ devices:
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padding-bottom: 15px
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imageTag: "stable-tensorrt-jp6"
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autoHardware: []
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helpText: "NVIDIA Jetson devices automatically configure runtime: nvidia."
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helpText: "JetPack 6 / L4T R36 Jetson devices automatically configure runtime: nvidia."
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helpType: "info"
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runtime: "nvidia"
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- <<: *jetsonDevice
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id: "stable-tensorrt-jp7"
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name: "NVIDIA Jetson JP7"
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description: "JetPack 7.2 / L4T R39.2"
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imageTag: "stable-tensorrt-jp7"
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helpText: "JetPack 7.2 / L4T R39.2 Jetson devices automatically configure runtime: nvidia."
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- id: "stable-rocm"
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name: "AMD GPU"
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description: "ROCm acceleration"
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@@ -176,6 +184,7 @@ hardware:
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description: "Pass through /dev/dri for GPU hardware acceleration (Intel/AMD)."
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disabledWhen:
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- "stable-tensorrt-jp6"
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- "stable-tensorrt-jp7"
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- "apple-silicon"
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devices:
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- host: "/dev/dri"
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@@ -187,6 +196,7 @@ hardware:
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description: "Pass through /dev/accel for Intel NPU acceleration."
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disabledWhen:
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- "stable-tensorrt-jp6"
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- "stable-tensorrt-jp7"
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- "apple-silicon"
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- "stable-rocm"
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- "stable-rk"
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@@ -247,6 +257,7 @@ hardware:
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disabledWhen:
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- "stable-tensorrt"
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- "stable-tensorrt-jp6"
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- "stable-tensorrt-jp7"
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- "stable-rocm"
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- "stable-rk"
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- "stable-synaptics"
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