Remove jp4 build and add notes for jp6 (#16670)

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Nicolas Mowen 2025-02-18 11:20:35 -07:00 committed by GitHub
parent c819961cc8
commit ab797c95af
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5 changed files with 5 additions and 49 deletions

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@ -76,36 +76,6 @@ jobs:
rpi.tags=${{ steps.setup.outputs.image-name }}-rpi
*.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64
*.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64,mode=max
jetson_jp4_build:
if: false
runs-on: ubuntu-22.04
name: Jetson Jetpack 4
steps:
- name: Check out code
uses: actions/checkout@v4
with:
persist-credentials: false
- name: Set up QEMU and Buildx
id: setup
uses: ./.github/actions/setup
with:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- name: Build and push TensorRT (Jetson, Jetpack 4)
env:
ARCH: arm64
BASE_IMAGE: timongentzsch/l4t-ubuntu20-opencv:latest
SLIM_BASE: timongentzsch/l4t-ubuntu20-opencv:latest
TRT_BASE: timongentzsch/l4t-ubuntu20-opencv:latest
uses: docker/bake-action@v6
with:
source: .
push: true
targets: tensorrt
files: docker/tensorrt/trt.hcl
set: |
tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt-jp4
*.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp4
*.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp4,mode=max
jetson_jp5_build:
if: false
runs-on: ubuntu-22.04

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@ -39,14 +39,14 @@ jobs:
STABLE_TAG=${BASE}:stable
PULL_TAG=${BASE}:${BUILD_TAG}
docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG} docker://${VERSION_TAG}
for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk h8l rocm; do
for variant in standard-arm64 tensorrt tensorrt-jp5 tensorrt-jp6 rk h8l rocm; do
docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG}-${variant} docker://${VERSION_TAG}-${variant}
done
# stable tag
if [[ "${BUILD_TYPE}" == "stable" ]]; then
docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG} docker://${STABLE_TAG}
for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk h8l rocm; do
for variant in standard-arm64 tensorrt tensorrt-jp5 tensorrt-jp6 rk h8l rocm; do
docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG}-${variant} docker://${STABLE_TAG}-${variant}
done
fi

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@ -1,10 +1,8 @@
BOARDS += trt
JETPACK4_BASE ?= timongentzsch/l4t-ubuntu20-opencv:latest # L4T 32.7.1 JetPack 4.6.1
JETPACK5_BASE ?= nvcr.io/nvidia/l4t-tensorrt:r8.5.2-runtime # L4T 35.3.1 JetPack 5.1.1
JETPACK6_BASE ?= nvcr.io/nvidia/tensorrt:23.12-py3-igpu
X86_DGPU_ARGS := ARCH=amd64 COMPUTE_LEVEL="50 60 70 80 90"
JETPACK4_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK4_BASE) SLIM_BASE=$(JETPACK4_BASE) TRT_BASE=$(JETPACK4_BASE)
JETPACK5_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK5_BASE) SLIM_BASE=$(JETPACK5_BASE) TRT_BASE=$(JETPACK5_BASE)
JETPACK6_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK6_BASE) SLIM_BASE=$(JETPACK6_BASE) TRT_BASE=$(JETPACK6_BASE)
@ -13,11 +11,6 @@ local-trt: version
--set tensorrt.tags=frigate:latest-tensorrt \
--load
local-trt-jp4: version
$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=frigate:latest-tensorrt-jp4 \
--load
local-trt-jp5: version
$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=frigate:latest-tensorrt-jp5 \
@ -31,8 +24,6 @@ local-trt-jp6: version
build-trt:
$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt
$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4
$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5
$(JETPACK6_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
@ -42,9 +33,6 @@ push-trt: build-trt
$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt \
--push
$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4 \
--push
$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5 \
--push

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@ -295,10 +295,8 @@ These instructions were originally based on the [Jellyfin documentation](https:/
## NVIDIA Jetson (Orin AGX, Orin NX, Orin Nano\*, Xavier AGX, Xavier NX, TX2, TX1, Nano)
A separate set of docker images is available that is based on Jetpack/L4T. They come with an `ffmpeg` build
with codecs that use the Jetson's dedicated media engine. If your Jetson host is running Jetpack 4.6, use the
`stable-tensorrt-jp4` tagged image, or if your Jetson host is running Jetpack 5.0+, use the `stable-tensorrt-jp5`
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.
with codecs that use the Jetson's dedicated media engine. If your Jetson host is running Jetpack 5.0+ use the `stable-tensorrt-jp5`
tagged image, or 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.
You will need to use the image with the nvidia container runtime:

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@ -250,7 +250,7 @@ The official docker image tags for the current stable version are:
The community supported docker image tags for the current stable version are:
- `stable-tensorrt-jp5` - Frigate build optimized for nvidia Jetson devices running Jetpack 5
- `stable-tensorrt-jp4` - Frigate build optimized for nvidia Jetson devices running Jetpack 4.6
- `stable-tensorrt-jp6` - Frigate build optimized for nvidia Jetson devices running Jetpack 6
- `stable-rk` - Frigate build for SBCs with Rockchip SoC
- `stable-rocm` - Frigate build for [AMD GPUs](../configuration/object_detectors.md#amdrocm-gpu-detector)
- `stable-h8l` - Frigate build for the Hailo-8L M.2 PICe Raspberry Pi 5 hat