mirror of
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Blake Blackshear
GitHub
parent
390403d957
commit
43c623be25
2
.github/workflows/ci.yml
vendored
2
.github/workflows/ci.yml
vendored
@ -212,7 +212,7 @@ jobs:
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Create short sha
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run: echo "SHORT_SHA=${GITHUB_SHA::7}" >> $GITHUB_ENV
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- uses: int128/docker-manifest-create-action@v1
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- uses: int128/docker-manifest-create-action@v2
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with:
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tags: ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}:${{ github.ref_name }}-${{ env.SHORT_SHA }}
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suffixes: |
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@ -123,9 +123,9 @@ or when using docker compose:
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```yaml
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services:
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frigate:
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...
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environment:
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DOWNLOAD_YOLOV8: "1"
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---
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environment:
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DOWNLOAD_YOLOV8: "1"
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```
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When this variable is set then frigate will at startup fetch [yolov8.small.models.tar.gz](https://github.com/harakas/models/releases/download/yolov8.1-1.1/yolov8.small.models.tar.gz) and extract it into the `/config/model_cache/yolov8/` directory.
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@ -313,7 +313,7 @@ frigate:
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### Configuration Parameters
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The TensorRT detector can be selected by specifying `tensorrt` as the model type. The GPU will need to be passed through to the docker container using the same methods described in the [Hardware Acceleration](hardware_acceleration.md#nvidia-gpu) section. If you pass through multiple GPUs, you can select which GPU is used for a detector with the `device` configuration parameter. The `device` parameter is an integer value of the GPU index, as shown by `nvidia-smi` within the container.
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The TensorRT detector can be selected by specifying `tensorrt` as the model type. The GPU will need to be passed through to the docker container using the same methods described in the [Hardware Acceleration](hardware_acceleration.md#nvidia-gpus) section. If you pass through multiple GPUs, you can select which GPU is used for a detector with the `device` configuration parameter. The `device` parameter is an integer value of the GPU index, as shown by `nvidia-smi` within the container.
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The TensorRT detector uses `.trt` model files that are located in `/config/model_cache/tensorrt` by default. These model path and dimensions used will depend on which model you have generated.
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@ -484,11 +484,10 @@ When using docker compose:
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```yaml
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services:
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frigate:
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...
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devices:
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- /dev/dri
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- /dev/kfd
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...
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---
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devices:
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- /dev/dri
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- /dev/kfd
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```
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For reference on recommended settings see [running ROCm/pytorch in Docker](https://rocm.docs.amd.com/projects/install-on-linux/en/develop/how-to/3rd-party/pytorch-install.html#using-docker-with-pytorch-pre-installed).
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@ -503,9 +502,9 @@ For chipset specific frigate rocm builds this variable is already set automatica
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For the general rocm frigate build there is some automatic detection:
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- gfx90c -> 9.0.0
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- gfx1031 -> 10.3.0
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- gfx1103 -> 11.0.0
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- gfx90c -> 9.0.0
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- gfx1031 -> 10.3.0
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- gfx1103 -> 11.0.0
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If you have something else you might need to override the `HSA_OVERRIDE_GFX_VERSION` at Docker launch. Suppose the version you want is `9.0.0`, then you should configure it from command line as:
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@ -519,18 +518,18 @@ When using docker compose:
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```yaml
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services:
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frigate:
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...
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environment:
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HSA_OVERRIDE_GFX_VERSION: "9.0.0"
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---
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environment:
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HSA_OVERRIDE_GFX_VERSION: "9.0.0"
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```
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Figuring out what version you need can be complicated as you can't tell the chipset name and driver from the AMD brand name.
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- first make sure that rocm environment is running properly by running `/opt/rocm/bin/rocminfo` in the frigate container -- it should list both the CPU and the GPU with their properties
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- find the chipset version you have (gfxNNN) from the output of the `rocminfo` (see below)
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- use a search engine to query what `HSA_OVERRIDE_GFX_VERSION` you need for the given gfx name ("gfxNNN ROCm HSA_OVERRIDE_GFX_VERSION")
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- override the `HSA_OVERRIDE_GFX_VERSION` with relevant value
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- if things are not working check the frigate docker logs
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- first make sure that rocm environment is running properly by running `/opt/rocm/bin/rocminfo` in the frigate container -- it should list both the CPU and the GPU with their properties
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- find the chipset version you have (gfxNNN) from the output of the `rocminfo` (see below)
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- use a search engine to query what `HSA_OVERRIDE_GFX_VERSION` you need for the given gfx name ("gfxNNN ROCm HSA_OVERRIDE_GFX_VERSION")
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- override the `HSA_OVERRIDE_GFX_VERSION` with relevant value
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- if things are not working check the frigate docker logs
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#### Figuring out if AMD/ROCm is working and found your GPU
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@ -566,9 +565,9 @@ or when using docker compose:
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```yaml
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services:
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frigate:
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...
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environment:
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DOWNLOAD_YOLOV8: "1"
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---
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environment:
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DOWNLOAD_YOLOV8: "1"
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```
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Download can be triggered also in regular frigate builds using that environment variable. The following files will be available under `/config/model_cache/yolov8/`:
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@ -33,7 +33,6 @@ Fork [blakeblackshear/frigate-hass-integration](https://github.com/blakeblackshe
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### Prerequisites
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- [Frigate source code](#frigate-core-web-and-docs)
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- GNU make
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- Docker
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- An extra detector (Coral, OpenVINO, etc.) is optional but recommended to simulate real world performance.
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@ -129,7 +128,6 @@ ffmpeg -c:v h264_qsv -re -stream_loop -1 -i https://streams.videolan.org/ffmpeg/
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### Prerequisites
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- [Frigate source code](#frigate-core-web-and-docs)
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- All [core](#core) prerequisites _or_ another running Frigate instance locally available
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- Node.js 20
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@ -188,7 +186,6 @@ npm run test
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### Prerequisites
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- [Frigate source code](#frigate-core-web-and-docs)
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- Node.js 20
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### Making changes
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@ -27,7 +27,7 @@ Motion masks prevent detection of [motion](#motion) in masked areas from trigger
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### Object Mask
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Object filter masks drop any bounding boxes where the bottom center (overlap doesn't matter) is in the masked area. It forces them to be considered a [false positive](#false_positive) so that they are ignored.
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Object filter masks drop any bounding boxes where the bottom center (overlap doesn't matter) is in the masked area. It forces them to be considered a [false positive](#false-positive) so that they are ignored.
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## Min Score
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@ -64,9 +64,9 @@ When configuring the integration, you will be asked for the `URL` of your Frigat
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Home Assistant > Configuration > Integrations > Frigate > Options
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```
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| Option | Description |
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| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| RTSP URL Template | A [jinja2](https://jinja.palletsprojects.com/) template that is used to override the standard RTSP stream URL (e.g. for use with reverse proxies). This option is only shown to users who have [advanced mode](https://www.home-assistant.io/blog/2019/07/17/release-96/#advanced-mode) enabled. See [RTSP streams](#streams) below. |
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| Option | Description |
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| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| RTSP URL Template | A [jinja2](https://jinja.palletsprojects.com/) template that is used to override the standard RTSP stream URL (e.g. for use with reverse proxies). This option is only shown to users who have [advanced mode](https://www.home-assistant.io/blog/2019/07/17/release-96/#advanced-mode) enabled. See [RTSP streams](#rtsp-stream) below. |
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## Entities Provided
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@ -178,7 +178,7 @@ for how to set these.
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#### API URLs
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When multiple Frigate instances are configured, [API](#api) URLs should include an
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When multiple Frigate instances are configured, [API](#notification-api) URLs should include an
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identifier to tell Home Assistant which Frigate instance to refer to. The
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identifier used is the MQTT `client_id` parameter included in the configuration,
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and is used like so:
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@ -13,7 +13,8 @@ const variants = {
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},
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overlay: {
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active: "font-bold text-white bg-selected rounded-full",
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inactive: "text-primary-white rounded-full bg-gradient-to-br from-gray-400 to-gray-500 bg-gray-500",
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inactive:
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"text-primary-white rounded-full bg-gradient-to-br from-gray-400 to-gray-500 bg-gray-500",
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},
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};
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