mirror of
https://github.com/blakeblackshear/frigate.git
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Multi arch image with nvidia decode support
* build working * update makefile * use jellyfin-ffmpeg for all arch * just build web once for all arch * update actions build * update docs
This commit is contained in:
Blake Blackshear
GitHub
parent
f2030d301f
commit
f536494a38
22
.github/workflows/pull_request.yml
vendored
22
.github/workflows/pull_request.yml
vendored
@ -20,20 +20,6 @@ jobs:
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run: npm run lint
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working-directory: ./web
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web_build:
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name: Web - Build
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@master
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- uses: actions/setup-node@master
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with:
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node-version: 16.x
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- run: npm install
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working-directory: ./web
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- name: Build
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run: npm run build
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working-directory: ./web
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web_test:
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name: Web - Test
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runs-on: ubuntu-latest
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@ -71,6 +57,14 @@ jobs:
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steps:
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- name: Check out code
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uses: actions/checkout@v2
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- uses: actions/setup-node@master
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with:
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node-version: 16.x
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- run: npm install
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working-directory: ./web
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- name: Build web
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run: npm run build
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working-directory: ./web
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- name: Set up QEMU
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uses: docker/setup-qemu-action@v1
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- name: Set up Docker Buildx
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28
Makefile
28
Makefile
@ -1,18 +1,36 @@
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default_target: frigate
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default_target: local
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COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
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VERSION = 0.11.0
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CURRENT_UID := $(shell id -u)
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CURRENT_GID := $(shell id -g)
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version:
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echo "VERSION='0.11.0-$(COMMIT_HASH)'" > frigate/version.py
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echo "VERSION=\"$(VERSION)-$(COMMIT_HASH)\"" > frigate/version.py
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build_web:
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docker run --volume ${PWD}/web:/web -w /web --volume /etc/passwd:/etc/passwd:ro --volume /etc/group:/etc/group:ro -u $(CURRENT_UID):$(CURRENT_GID) node:16 /bin/bash -c "npm install && npm run build"
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nginx_frigate:
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docker buildx build --push --platform linux/arm/v7,linux/arm64/v8,linux/amd64 --tag blakeblackshear/frigate-nginx:1.0.2 --file docker/Dockerfile.nginx .
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frigate: version
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local:
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DOCKER_BUILDKIT=1 docker build -t frigate -f docker/Dockerfile .
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frigate_push: version
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docker buildx build --push --platform linux/arm64/v8,linux/amd64 --tag blakeblackshear/frigate:0.11.0-$(COMMIT_HASH) --file docker/Dockerfile .
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amd64:
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docker buildx build --platform linux/amd64 --tag blakeblackshear/frigate:$(VERSION)-$(COMMIT_HASH) --file docker/Dockerfile .
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arm64:
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docker buildx build --platform linux/arm64 --tag blakeblackshear/frigate:$(VERSION)-$(COMMIT_HASH) --file docker/Dockerfile .
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armv7:
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docker buildx build --platform linux/arm/v7 --tag blakeblackshear/frigate:$(VERSION)-$(COMMIT_HASH) --file docker/Dockerfile .
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build: version amd64 arm64 armv7
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docker buildx build --platform linux/arm/v7,linux/arm64/v8,linux/amd64 --tag blakeblackshear/frigate:$(VERSION)-$(COMMIT_HASH) --file docker/Dockerfile .
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push: build
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docker buildx build --push --platform linux/arm/v7,linux/arm64/v8,linux/amd64 --tag blakeblackshear/frigate:$(VERSION)-$(COMMIT_HASH) --file docker/Dockerfile .
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run_tests: frigate
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docker run --rm --entrypoint=python3 frigate:latest -u -m unittest
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@ -1,13 +1,5 @@
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FROM blakeblackshear/frigate-nginx:1.0.2 as nginx
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FROM node:16 as web
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WORKDIR /opt/frigate
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COPY web/ .
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RUN npm install && npm run build
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FROM debian:11 as wheels
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ARG TARGETARCH
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@ -54,7 +46,13 @@ RUN pip3 wheel --wheel-dir=/wheels -r requirements-wheels.txt
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FROM debian:11-slim
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ARG TARGETARCH
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ENV DEBIAN_FRONTEND=noninteractive
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# https://askubuntu.com/questions/972516/debian-frontend-environment-variable
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ARG DEBIAN_FRONTEND="noninteractive"
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# http://stackoverflow.com/questions/48162574/ddg#49462622
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ARG APT_KEY_DONT_WARN_ON_DANGEROUS_USAGE=DontWarn
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# https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(Native-GPU-Support)
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ENV NVIDIA_DRIVER_CAPABILITIES="compute,video,utility"
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ENV FLASK_ENV=development
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COPY --from=wheels /wheels /wheels
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@ -71,30 +69,29 @@ RUN apt-get -qq update \
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&& wget -O - http://archive.raspberrypi.org/debian/raspberrypi.gpg.key | apt-key add - \
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&& echo "deb http://archive.raspberrypi.org/debian/ bullseye main" | tee /etc/apt/sources.list.d/raspi.list \
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# add coral repo
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&& APT_KEY_DONT_WARN_ON_DANGEROUS_USAGE=DontWarn apt-key adv --fetch-keys https://packages.cloud.google.com/apt/doc/apt-key.gpg \
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&& apt-key adv --fetch-keys https://packages.cloud.google.com/apt/doc/apt-key.gpg \
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&& echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" > /etc/apt/sources.list.d/coral-edgetpu.list \
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&& echo "libedgetpu1-max libedgetpu/accepted-eula select true" | debconf-set-selections \
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# jellyfin-ffmpeg
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&& wget -O - https://repo.jellyfin.org/jellyfin_team.gpg.key | apt-key add - \
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&& echo "deb [arch=$( dpkg --print-architecture )] https://repo.jellyfin.org/$( awk -F'=' '/^ID=/{ print $NF }' /etc/os-release ) $( awk -F'=' '/^VERSION_CODENAME=/{ print $NF }' /etc/os-release ) main" | tee /etc/apt/sources.list.d/jellyfin.list \
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&& apt-get -qq update \
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&& apt-get -qq install --no-install-recommends -y \
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ffmpeg \
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&& apt-get -qq install --no-install-recommends --no-install-suggests -y \
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# coral drivers
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libedgetpu1-max python3-tflite-runtime python3-pycoral \
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jellyfin-ffmpeg \
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&& pip3 install -U /wheels/*.whl \
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&& rm -rf /var/lib/apt/lists/* /wheels \
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&& (apt-get autoremove -y; apt-get autoclean -y)
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# AMD64 specific packages
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RUN if [ "${TARGETARCH}" = "amd64" ]; \
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then \
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wget -qO - https://repositories.intel.com/graphics/intel-graphics.key | apt-key add - \
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&& echo 'deb [arch=amd64] https://repositories.intel.com/graphics/ubuntu focal main' > /etc/apt/sources.list.d/intel-graphics.list \
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&& apt-get -qq update \
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&& apt-get -qq install --no-install-recommends -y \
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# VAAPI drivers for Intel hardware accel
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libva-drm2 libva2 libmfx1 i965-va-driver vainfo intel-media-va-driver-non-free mesa-vdpau-drivers mesa-va-drivers mesa-vdpau-drivers libdrm-radeon1 \
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&& rm -rf /var/lib/apt/lists/* \
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&& (apt-get autoremove -y; apt-get autoclean -y) \
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fi
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&& ln -s /usr/lib/jellyfin-ffmpeg/ffmpeg /usr/bin/ffmpeg \
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# amd64 specific packages
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&& if [ "${TARGETARCH}" = "amd64" ]; then \
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apt-get -qq install --no-install-recommends -y \
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mesa-va-drivers; \
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fi \
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&& rm -rf /wheels \
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&& apt-get remove gnupg apt-transport-https -y \
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&& apt-get clean autoclean -y \
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&& apt-get autoremove -y \
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&& rm -rf /var/lib/apt/lists/*
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COPY --from=nginx /usr/local/nginx/ /usr/local/nginx/
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@ -107,7 +104,7 @@ WORKDIR /opt/frigate/
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ADD frigate frigate/
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ADD migrations migrations/
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COPY --from=web /opt/frigate/dist web/
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COPY web/dist web/
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COPY docker/rootfs/ /
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@ -67,4 +67,76 @@ ffmpeg:
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### NVIDIA GPU
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NVIDIA GPU based decoding via NVDEC is supported, but requires special configuration. See the [NVIDIA NVDEC documentation](/configuration/nvdec) for more details.
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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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```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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- 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 H265 video (hevc), you'll select `hevc_cuvid`.
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```yaml
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ffmpeg:
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hwaccel_args:
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- -c:v
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- hevc_cuvid
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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 `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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@ -1,99 +0,0 @@
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---
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id: nvdec
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title: NVIDIA hardware decoder
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---
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Certain nvidia cards include a hardware decoder, which can greatly improve the
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performance of video decoding. In order to use NVDEC, a special build of
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ffmpeg with NVDEC support is required. The special docker architecture 'amd64nvidia'
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includes this support for amd64 platforms. An aarch64 for the Jetson, which
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also includes NVDEC may be added in the future.
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Some more detailed setup instructions are also available in [this issue](https://github.com/blakeblackshear/frigate/issues/1847#issuecomment-932076731).
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## Docker setup
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### Requirements
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[nVidia closed source driver](https://www.nvidia.com/en-us/drivers/unix/) required to access NVDEC.
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[nvidia-docker](https://github.com/NVIDIA/nvidia-docker) required to pass NVDEC to docker.
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### Setting up docker-compose
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In order to pass NVDEC, the docker engine must be set to `nvidia` and the environment variables
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`NVIDIA_VISIBLE_DEVICES=all` and `NVIDIA_DRIVER_CAPABILITIES=compute,utility,video` must be set.
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In a docker compose file, these lines need to be set:
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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-amd64nvidia
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runtime: nvidia
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environment:
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- NVIDIA_VISIBLE_DEVICES=all
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- NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
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```
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### Setting up the configuration file
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In your frigate config.yml, you'll need to set ffmpeg to use the hardware decoder.
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The decoder you choose 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 H265 video (hevc), you'll select `hevc_cuvid`. Add
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`-c:v hevc_cuvid` to your ffmpeg input arguments:
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```yaml
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ffmpeg:
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input_args: ...
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- -c:v
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- hevc_cuvid
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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 `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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@ -102,6 +102,12 @@ This should show <50% CPU in top, and ~80% CPU without `-c:v h264_v4l2m2m`.
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ffmpeg -c:v h264_v4l2m2m -re -stream_loop -1 -i https://streams.videolan.org/ffmpeg/incoming/720p60.mp4 -f rawvideo -pix_fmt yuv420p pipe: > /dev/null
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```
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**NVIDIA**
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```shell
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ffmpeg -c:v h264_cuvid -re -stream_loop -1 -i https://streams.videolan.org/ffmpeg/incoming/720p60.mp4 -f rawvideo -pix_fmt yuv420p pipe: > /dev/null
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```
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## Web Interface
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### Prerequisites
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@ -22,7 +22,6 @@ module.exports = {
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"configuration/stationary_objects",
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"configuration/advanced",
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"configuration/hardware_acceleration",
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"configuration/nvdec",
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"configuration/camera_specific",
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],
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Integrations: [
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