Merge 243172fbf4 into c007661a71

.github/workflows/ci.yml

@@ -106,6 +106,41 @@ jobs:
             tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt-jp6
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp6
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp6,mode=max
+  jetson_jp7_build:
+    runs-on: ubuntu-22.04-arm
+    name: Jetson Jetpack 7
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v6
+        with:
+          persist-credentials: false
+      - name: Set up QEMU and Buildx
+        id: setup
+        uses: ./.github/actions/setup
+        with:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Build and push TensorRT (Jetson, Jetpack 7)
+        env:
+          ARCH: arm64
+          BASE_IMAGE: nvcr.io/nvidia/tensorrt:26.02-py3-igpu
+          SLIM_BASE: nvcr.io/nvidia/tensorrt:26.02-py3-igpu
+          TRT_BASE: nvcr.io/nvidia/tensorrt:26.02-py3-igpu
+          BUILD_ONNXRUNTIME_FROM_SOURCE: "1"
+          ONNXRUNTIME_VERSION: "1.25.1"
+          ONNXRUNTIME_BRANCH: rel-1.25.1
+          TENSORRT_PYTHON_BRANCH: auto
+          L4T_APT_RELEASE: r39.2
+          JETSON_SOC_REPO: som
+        uses: docker/bake-action@v7
+        with:
+          source: .
+          push: true
+          targets: tensorrt
+          files: docker/tensorrt/trt.hcl
+          set: |
+            tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt-jp7
+            *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp7
+            *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp7,mode=max
   amd64_extra_builds:
     runs-on: ubuntu-22.04
     name: AMD64 Extra Build

.github/workflows/release.yml

@@ -39,14 +39,16 @@ 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://${VERSION_TAG}
-          for variant in standard-arm64 tensorrt tensorrt-jp6 rk rocm synaptics; do
+          for variant in standard-arm64 tensorrt tensorrt-jp6 tensorrt-jp7 rk rocm synaptics; do
+            docker manifest inspect "${PULL_TAG}-${variant}" >/dev/null
             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://${STABLE_TAG}
-            for variant in standard-arm64 tensorrt tensorrt-jp6 rk rocm synaptics; do
+            for variant in standard-arm64 tensorrt tensorrt-jp6 tensorrt-jp7 rk rocm synaptics; do
+              docker manifest inspect "${PULL_TAG}-${variant}" >/dev/null
               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

docker/main/Dockerfile

@@ -87,7 +87,9 @@ RUN apt-get -qq update \
     && apt-get -qq install -y wget python3 python3-dev python3-distutils gcc pkg-config libhdf5-dev \
     && wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
     && sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
-    && python3 get-pip.py "pip" \
+    # --ignore-installed: the Ubuntu 24.04 (noble) JP7 base ships a distro pip with no RECORD
+    # file, so get-pip.py's default reinstall dies with "uninstall-no-record-file". Harmless on JP6.
+    && python3 get-pip.py --ignore-installed "pip" \
     && pip3 install -r /requirements-ov.txt
 
 # Get OpenVino Model
@@ -181,9 +183,11 @@ RUN apt-get -qq update \
 
 RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
 
+# --ignore-installed: the Ubuntu 24.04 (noble) JP7 base ships a distro pip with no RECORD file,
+# so get-pip.py's default reinstall dies with "uninstall-no-record-file". Harmless on JP6.
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
     && sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
-    && python3 get-pip.py "pip"
+    && python3 get-pip.py --ignore-installed "pip"
 
 COPY docker/main/requirements.txt /requirements.txt
 COPY docker/main/requirements-dev.txt /requirements-dev.txt
@@ -268,12 +272,18 @@ RUN --mount=type=bind,source=docker/main/install_deps.sh,target=/deps/install_de
 ENV DEFAULT_FFMPEG_VERSION="8.0"
 ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:7.0:5.0"
 
+# --ignore-installed: the Ubuntu 24.04 (noble) JP7 base ships a distro pip with no RECORD file,
+# so get-pip.py's default reinstall dies with "uninstall-no-record-file". Harmless on JP6.
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
     && sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
-    && python3 get-pip.py "pip"
+    && python3 get-pip.py --ignore-installed "pip"
 
+# --ignore-installed: on the Ubuntu 24.04 (noble) JP7 base, `pip3 install -U` tries to upgrade
+# distro-managed packages (e.g. wheel 0.42.0) that ship without a RECORD file, which fails with
+# "uninstall-no-record-file". Installing over them into /usr/local without uninstalling avoids
+# that; harmless on JP6 where these come from pip and have RECORD files.
 RUN --mount=type=bind,from=wheels,source=/wheels,target=/deps/wheels \
-    pip3 install -U /deps/wheels/*.whl
+    pip3 install -U --ignore-installed /deps/wheels/*.whl
 
 # Install Axera Engine
 RUN pip3 install https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3-frigate/axengine-0.1.3-py3-none-any.whl

docker/main/build_nginx.sh

@@ -10,9 +10,15 @@ NGX_DEVEL_KIT_VERSION="v0.3.3"
 
 source /etc/os-release
 
-if [[ "$VERSION_ID" == "12" ]]; then
+# Enable deb-src so `apt-get build-dep nginx` can find the source package. Detect the apt
+# source FORMAT rather than guessing by distro: Debian 12 + Ubuntu 24.04 (noble — the JP7
+# TensorRT igpu base) use the deb822 *.sources format with a `Types:` line; older Ubuntu
+# (e.g. the JP6 22.04 jammy base) uses the legacy one-line /etc/apt/sources.list.
+if [[ -f /etc/apt/sources.list.d/debian.sources ]]; then
     sed -i '/^Types:/s/deb/& deb-src/' /etc/apt/sources.list.d/debian.sources
-else
+elif [[ -f /etc/apt/sources.list.d/ubuntu.sources ]]; then
+    sed -i '/^Types:/s/deb/& deb-src/' /etc/apt/sources.list.d/ubuntu.sources
+elif [[ -f /etc/apt/sources.list ]]; then
     cp /etc/apt/sources.list /etc/apt/sources.list.d/sources-src.list
     sed -i 's|deb http|deb-src http|g' /etc/apt/sources.list.d/sources-src.list
 fi

docker/main/build_sqlite_vec.sh

@@ -6,9 +6,16 @@ SQLITE_VEC_VERSION="0.1.3"
 
 source /etc/os-release
 
-if [[ "$VERSION_ID" == "12" ]]; then
+# Enable deb-src so `apt-get build-dep sqlite3` can find the source package. Detect the apt
+# source FORMAT rather than guessing by distro: Debian 12 + Ubuntu 24.04 (noble — the JP7
+# TensorRT igpu base) use the deb822 *.sources format with a `Types:` line; older Ubuntu
+# (e.g. the JP6 22.04 jammy base) uses the legacy one-line /etc/apt/sources.list. On noble the
+# legacy /etc/apt/sources.list is effectively empty, so the old copy+sed path enabled nothing.
+if [[ -f /etc/apt/sources.list.d/debian.sources ]]; then
     sed -i '/^Types:/s/deb/& deb-src/' /etc/apt/sources.list.d/debian.sources
-else
+elif [[ -f /etc/apt/sources.list.d/ubuntu.sources ]]; then
+    sed -i '/^Types:/s/deb/& deb-src/' /etc/apt/sources.list.d/ubuntu.sources
+elif [[ -f /etc/apt/sources.list ]]; then
     cp /etc/apt/sources.list /etc/apt/sources.list.d/sources-src.list
     sed -i 's|deb http|deb-src http|g' /etc/apt/sources.list.d/sources-src.list
 fi

docker/tensorrt/Dockerfile.arm64

@@ -4,6 +4,12 @@
 ARG DEBIAN_FRONTEND=noninteractive
 ARG BASE_IMAGE
 ARG TRT_BASE=nvcr.io/nvidia/tensorrt:23.12-py3
+ARG BUILD_ONNXRUNTIME_FROM_SOURCE=0
+ARG ONNXRUNTIME_VERSION=1.25.1
+ARG ONNXRUNTIME_BRANCH=rel-1.25.1
+ARG TENSORRT_PYTHON_BRANCH=release/8.6
+ARG L4T_APT_RELEASE=
+ARG JETSON_SOC_REPO=
 
 # Build TensorRT-specific library
 FROM ${TRT_BASE} AS trt-deps
@@ -12,7 +18,9 @@ ARG TARGETARCH
 ARG COMPUTE_LEVEL
 
 RUN apt-get update \
-    && apt-get install -y git build-essential cuda-nvcc-* cuda-nvtx-* libnvinfer-dev libnvinfer-plugin-dev libnvparsers-dev libnvonnxparsers-dev \
+    && TRT_DEV_PACKAGES="git build-essential cuda-nvcc-* cuda-nvtx-* libnvinfer-dev libnvinfer-plugin-dev libnvonnxparsers-dev" \
+    && if apt-cache show libnvparsers-dev > /dev/null 2>&1; then TRT_DEV_PACKAGES="${TRT_DEV_PACKAGES} libnvparsers-dev"; fi \
+    && apt-get install -y ${TRT_DEV_PACKAGES} \
     && rm -rf /var/lib/apt/lists/*
 RUN --mount=type=bind,source=docker/tensorrt/detector/tensorrt_libyolo.sh,target=/tensorrt_libyolo.sh \
     /tensorrt_libyolo.sh
@@ -58,6 +66,12 @@ HEALTHCHECK --start-period=600s --start-interval=5s --interval=15s --timeout=5s
 
 FROM ${BASE_IMAGE} AS build-wheels
 ARG DEBIAN_FRONTEND
+ARG BUILD_ONNXRUNTIME_FROM_SOURCE
+ARG ONNXRUNTIME_VERSION
+ARG ONNXRUNTIME_BRANCH
+ARG TENSORRT_PYTHON_BRANCH
+ARG L4T_APT_RELEASE
+ARG JETSON_SOC_REPO
 
 # Add deadsnakes PPA for python3.11
 RUN apt-get -qq update && \
@@ -69,41 +83,66 @@ RUN apt-get -qq update && \
 RUN apt-get -qq update \
     && apt-get -qq install -y --no-install-recommends \
     python3.11 python3.11-dev \
-    wget build-essential cmake git \
+    wget curl build-essential cmake git ninja-build \
     && rm -rf /var/lib/apt/lists/*
 
 # Ensure python3 defaults to python3.11
 RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
 
+# --ignore-installed: on the JP7 Ubuntu 24.04 (noble) base the deadsnakes python3.11 ships a
+# distro-packaged pip 24.0 with no RECORD file, so get-pip.py's default reinstall fails with
+# "uninstall-no-record-file". Installing over it without uninstalling avoids that; harmless on JP6.
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
     && sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
-    && python3 get-pip.py "pip"
+    && python3 get-pip.py --ignore-installed "pip"
 
 FROM build-wheels AS trt-wheels
 ARG DEBIAN_FRONTEND
 ARG TARGETARCH
+ARG BUILD_ONNXRUNTIME_FROM_SOURCE
+ARG ONNXRUNTIME_VERSION
+ARG ONNXRUNTIME_BRANCH
+ARG TENSORRT_PYTHON_BRANCH
 
 # python-tensorrt build deps are 3.4 GB!
 RUN apt-get update \
-    && apt-get install -y ccache cuda-cudart-dev-* cuda-nvcc-* libnvonnxparsers-dev libnvparsers-dev libnvinfer-plugin-dev \
+    && TRT_DEV_PACKAGES="ccache cuda-cudart-dev-* cuda-nvcc-* libnvonnxparsers-dev libnvinfer-plugin-dev" \
+    && if apt-cache show libnvparsers-dev > /dev/null 2>&1; then TRT_DEV_PACKAGES="${TRT_DEV_PACKAGES} libnvparsers-dev"; fi \
+    && apt-get install -y ${TRT_DEV_PACKAGES} \
     && ([ -e /usr/local/cuda ] || ln -s /usr/local/cuda-* /usr/local/cuda) \
     && rm -rf /var/lib/apt/lists/*;
 
+# The JP7 base image ships a stale cmake 3.24 at /usr/local/bin/cmake that shadows apt's 3.28.
+# TensorRT 10.11's python CMakeLists (and the ONNX Runtime source build) require cmake >= 3.27,
+# so install a current cmake from pip into /usr/local/bin ahead of the stale one. No-op risk on
+# JP6 where the bundled cmake already satisfies the minimum.
+RUN pip3 install --no-cache-dir --upgrade "cmake>=3.27"
+
 # Determine version of tensorrt already installed in base image, e.g. "Version: 8.4.1-1+cuda11.4"
-RUN NVINFER_VER=$(dpkg -s libnvinfer8 | grep -Po "Version: \K.*") \
+RUN NVINFER_VER="$(dpkg-query -W -f='${Version}\n' 'libnvinfer*' 2>/dev/null | grep -E '^[0-9]+[.][0-9]+[.][0-9]+' | sort -V | tail -n1)" \
-    && echo $NVINFER_VER | grep -Po "^\d+\.\d+\.\d+" > /etc/TENSORRT_VER
+    && test -n "$NVINFER_VER" \
+    && TENSORRT_VER="$(echo "$NVINFER_VER" | grep -Eo '^[0-9]+[.][0-9]+[.][0-9]+')" \
+    && TENSORRT_MAJOR="$(echo "$TENSORRT_VER" | cut -d. -f1)" \
+    && echo "$TENSORRT_VER" > /etc/TENSORRT_VER \
+    && echo "$TENSORRT_MAJOR" > /etc/TENSORRT_MAJOR
 
 RUN --mount=type=bind,source=docker/tensorrt/detector/build_python_tensorrt.sh,target=/deps/build_python_tensorrt.sh \
     --mount=type=cache,target=/root/.ccache \
     export PATH="/usr/lib/ccache:$PATH" CCACHE_DIR=/root/.ccache CCACHE_MAXSIZE=2G \
-    && TENSORRT_VER=$(cat /etc/TENSORRT_VER) /deps/build_python_tensorrt.sh
+    && TENSORRT_VER=$(cat /etc/TENSORRT_VER) TENSORRT_PYTHON_BRANCH="${TENSORRT_PYTHON_BRANCH}" /deps/build_python_tensorrt.sh
 
 COPY docker/tensorrt/requirements-arm64.txt /requirements-tensorrt.txt
 
 RUN pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt
 
 # See https://elinux.org/Jetson_Zoo#ONNX_Runtime
-ADD https://nvidia.box.com/shared/static/9yvw05k6u343qfnkhdv2x6xhygze0aq1.whl /trt-wheels/onnxruntime_gpu-1.19.0-cp311-cp311-linux_aarch64.whl
+RUN --mount=type=bind,source=docker/tensorrt/detector/build_onnxruntime_arm64.sh,target=/deps/build_onnxruntime_arm64.sh \
+    mkdir -p /trt-wheels \
+    && if [ "${BUILD_ONNXRUNTIME_FROM_SOURCE}" = "1" ]; then \
+        ONNXRUNTIME_VERSION="${ONNXRUNTIME_VERSION}" ONNXRUNTIME_BRANCH="${ONNXRUNTIME_BRANCH}" /deps/build_onnxruntime_arm64.sh; \
+    else \
+        wget -q https://nvidia.box.com/shared/static/9yvw05k6u343qfnkhdv2x6xhygze0aq1.whl -O /trt-wheels/onnxruntime_gpu-1.19.0-cp311-cp311-linux_aarch64.whl; \
+    fi
 
 FROM build-wheels AS trt-model-wheels
 ARG DEBIAN_FRONTEND
@@ -116,16 +155,20 @@ RUN --mount=type=bind,source=docker/tensorrt/requirements-models-arm64.txt,targe
 
 FROM wget AS jetson-ffmpeg
 ARG DEBIAN_FRONTEND
+ARG L4T_APT_RELEASE
+ARG JETSON_SOC_REPO
 ENV CCACHE_DIR /root/.ccache
 ENV CCACHE_MAXSIZE 2G
 RUN --mount=type=bind,source=docker/tensorrt/build_jetson_ffmpeg.sh,target=/deps/build_jetson_ffmpeg.sh \
     --mount=type=cache,target=/root/.ccache \
-    /deps/build_jetson_ffmpeg.sh
+    L4T_APT_RELEASE="${L4T_APT_RELEASE}" JETSON_SOC_REPO="${JETSON_SOC_REPO}" /deps/build_jetson_ffmpeg.sh
 
 # Frigate w/ TensorRT for NVIDIA Jetson platforms
 FROM tensorrt-base AS frigate-tensorrt
 RUN apt-get update \
-    && apt-get install -y python-is-python3 libprotobuf23  \
+    && pick_package() { for package in "$@"; do if apt-cache show "$package" > /dev/null 2>&1; then echo "$package"; return 0; fi; done; return 1; } \
+    && PROTOBUF_RUNTIME="$(pick_package libprotobuf23 libprotobuf32t64 libprotobuf32)" \
+    && apt-get install -y python-is-python3 "${PROTOBUF_RUNTIME}" \
     && rm -rf /var/lib/apt/lists/*
 
 COPY --from=jetson-ffmpeg /rootfs /
@@ -134,19 +177,26 @@ ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:${INCLUDED_FFMPEG_VERSIO
 
 # ffmpeg runtime dependencies
 RUN apt-get -qq update \
+    && pick_package() { for package in "$@"; do if apt-cache show "$package" > /dev/null 2>&1; then echo "$package"; return 0; fi; done; return 1; } \
+    && X264_RUNTIME="$(pick_package libx264-163 libx264-164)" \
+    && X265_RUNTIME="$(pick_package libx265-199 libx265-209)" \
     && apt-get -qq install -y --no-install-recommends \
-    libx264-163 libx265-199 libegl1 \
+    "${X264_RUNTIME}" "${X265_RUNTIME}" libegl1 \
     && rm -rf /var/lib/apt/lists/*
 
 # Fixes "Error loading shared libs"
 RUN mkdir -p /etc/ld.so.conf.d && echo /usr/lib/ffmpeg/jetson/lib/ > /etc/ld.so.conf.d/ffmpeg.conf
 
 COPY --from=trt-wheels /etc/TENSORRT_VER /etc/TENSORRT_VER
+COPY --from=trt-wheels /etc/TENSORRT_MAJOR /etc/TENSORRT_MAJOR
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
     --mount=type=bind,from=trt-model-wheels,source=/trt-model-wheels,target=/deps/trt-model-wheels \
     pip3 uninstall -y onnxruntime \
-    && pip3 install -U /deps/trt-wheels/*.whl \
+    # --ignore-installed: on the noble JP7 base, `-U` would try to upgrade distro-managed deps that
-    && pip3 install -U /deps/trt-model-wheels/*.whl \
+    # lack a RECORD file (e.g. wheel/setuptools), failing with "uninstall-no-record-file". Install
+    # the TRT/ORT GPU wheels over them into /usr/local instead. Harmless on JP6.
+    && pip3 install -U --ignore-installed /deps/trt-wheels/*.whl \
+    && pip3 install -U --ignore-installed /deps/trt-model-wheels/*.whl \
     && ldconfig
 
 WORKDIR /opt/frigate/

docker/tensorrt/build_jetson_ffmpeg.sh

@@ -8,18 +8,81 @@ set -euxo pipefail
 INSTALL_PREFIX=/rootfs/usr/lib/ffmpeg/jetson
 
 apt-get -qq update
-apt-get -qq install -y --no-install-recommends build-essential ccache clang cmake pkg-config
+apt-get -qq install -y --no-install-recommends build-essential ccache clang cmake pkg-config unzip
 apt-get -qq install -y --no-install-recommends libx264-dev libx265-dev
 
 pushd /tmp
 
+CUDA_MAJOR=""
+if [ -f /usr/local/cuda/version.json ]; then
+    CUDA_MAJOR="$(grep -m1 -oE '"version"[[:space:]]*:[[:space:]]*"[^"]+"' /usr/local/cuda/version.json | sed -E 's/.*"([0-9]+)\..*/\1/' || true)"
+fi
+if [ -z "${CUDA_MAJOR}" ] && compgen -G "/usr/local/cuda-13*" > /dev/null; then
+    CUDA_MAJOR=13
+fi
+if [ -z "${CUDA_MAJOR}" ] && command -v nvcc > /dev/null 2>&1; then
+    CUDA_MAJOR="$(nvcc --version | sed -nE 's/.*release ([0-9]+)\..*/\1/p' | head -n1 || true)"
+fi
+
+# Tracks whether this is the JP7/R39 path, so the ffmpeg build below can link the real R39
+# tegra multimedia libs instead of the stale jetson-ffmpeg stubs (see TEGRA_LIB_DIR usage).
+JETSON_R39=0
+TEGRA_LIB_DIR=/usr/lib/aarch64-linux-gnu/nvidia
+
 # Install libnvmpi to enable nvmpi decoders (h264_nvmpi, hevc_nvmpi)
-if [ -e /usr/local/cuda-12 ]; then
+if [[ "${CUDA_MAJOR}" = "13" || -n "${L4T_APT_RELEASE:-}" || -n "${JETSON_SOC_REPO:-}" ]]; then
+    JETSON_R39=1
+    L4T_APT_RELEASE=${L4T_APT_RELEASE:-r39.2}
+    JETSON_SOC_REPO=${JETSON_SOC_REPO:-som}
+
+    apt-key adv --fetch-key https://repo.download.nvidia.com/jetson/jetson-ota-public.asc
+    {
+        echo "deb https://repo.download.nvidia.com/jetson/common ${L4T_APT_RELEASE} main"
+        echo "deb https://repo.download.nvidia.com/jetson/${JETSON_SOC_REPO} ${L4T_APT_RELEASE} main"
+        echo "deb https://repo.download.nvidia.com/jetson/ffmpeg ${L4T_APT_RELEASE} main"
+    } >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
+
+    mkdir -p /opt/nvidia/l4t-packages/
+    touch /opt/nvidia/l4t-packages/.nv-l4t-disable-boot-fw-update-in-preinstall
+
+    apt-get update
+    apt-get -qq install -y --no-install-recommends -o Dpkg::Options::="--force-confold" nvidia-l4t-jetson-multimedia-api
+
+    require_jetson_file() {
+        local description="$1"
+        local pattern="$2"
+        shift 2
+        local directory
+        local found
+
+        # Match case-insensitively: R39.2 (JP7) renamed the multimedia headers to all-lowercase
+        # (e.g. nvbufsurftransform.h) while older L4T shipped CamelCase (NvBufSurfTransform.h). The
+        # jetson-ffmpeg build's nvUtils2NvBuf.h includes the lowercase names, so the lowercase
+        # headers (present on R39.2) satisfy the build; only this validation gate was casing-strict.
+        for directory in "$@"; do
+            found="$(find "${directory}" -type f -iname "${pattern}" -print -quit 2> /dev/null || true)"
+            if [ -n "${found}" ]; then
+                echo "${description}: ${found}"
+                return 0
+            fi
+        done
+
+        echo "Missing ${description}" >&2
+        return 1
+    }
+
+    require_jetson_file "Jetson multimedia header NvBufSurface.h" "NvBufSurface.h" /usr/src/jetson_multimedia_api /usr/include
+    require_jetson_file "Jetson multimedia header NvBufSurfTransform.h" "NvBufSurfTransform.h" /usr/src/jetson_multimedia_api /usr/include
+    require_jetson_file "Jetson multimedia library libnvbufsurface" "libnvbufsurface.so*" /usr/lib /usr/local/lib
+    require_jetson_file "Jetson multimedia library libnvbufsurftransform" "libnvbufsurftransform.so*" /usr/lib /usr/local/lib
+elif [ -e /usr/local/cuda-12 ]; then
     # assume Jetpack 6.2
     apt-key adv --fetch-key https://repo.download.nvidia.com/jetson/jetson-ota-public.asc
-    echo "deb https://repo.download.nvidia.com/jetson/common r36.4 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
+    {
-    echo "deb https://repo.download.nvidia.com/jetson/t234 r36.4 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
+        echo "deb https://repo.download.nvidia.com/jetson/common r36.4 main"
-    echo "deb https://repo.download.nvidia.com/jetson/ffmpeg r36.4 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
+        echo "deb https://repo.download.nvidia.com/jetson/t234 r36.4 main"
+        echo "deb https://repo.download.nvidia.com/jetson/ffmpeg r36.4 main"
+    } >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
 
     mkdir -p /opt/nvidia/l4t-packages/
     touch /opt/nvidia/l4t-packages/.nv-l4t-disable-boot-fw-update-in-preinstall
@@ -38,11 +101,39 @@ fi
 
 wget -q https://github.com/AndBobsYourUncle/jetson-ffmpeg/archive/9c17b09.zip -O jetson-ffmpeg.zip
 unzip jetson-ffmpeg.zip && rm jetson-ffmpeg.zip && mv jetson-ffmpeg-* jetson-ffmpeg && cd jetson-ffmpeg
-LD_LIBRARY_PATH=$(pwd)/stubs:$LD_LIBRARY_PATH   # tegra multimedia libs aren't available in image, so use stubs for ffmpeg build
+# On R39/JP7 the real tegra multimedia libs ARE installed (by nvidia-l4t-jetson-multimedia-api),
+# living in /usr/lib/aarch64-linux-gnu/nvidia. The 2023-era jetson-ffmpeg stubs predate R39 and
+# lack newer symbols (e.g. NvBufSurfaceGetDeviceInfo), so libnvmpi.so links with an unresolved
+# reference and ffmpeg's later `-lnvmpi` configure test fails. Put the real lib dir ahead of the
+# stubs so libnvmpi resolves against R39's libnvbufsurface. Those real libs in turn need the CUDA
+# driver (libcuda.so.1) and the CUDA runtime libs, which are only present at container RUNTIME via
+# the nvidia runtime — so for the build-time link we point -rpath-link at the CUDA *stubs* dir
+# (libcuda.so stub) and the CUDA lib dir. Pre-R39 keeps the stubs-only path.
+if [ "${JETSON_R39}" = "1" ]; then
+    CUDA_STUBS_DIR="$(dirname "$(find /usr/local/cuda*/targets/*/lib/stubs -name libcuda.so 2>/dev/null | head -n1)")"
+    CUDA_LIB_DIR="$(dirname "$(find /usr/local/cuda*/targets/*/lib -maxdepth 1 -name 'libcudart.so*' 2>/dev/null | head -n1)")"
+    # The CUDA stub ships only unversioned libcuda.so, but R39's libnvbufsurface.so.1.0.0 records a
+    # NEEDED entry for the versioned soname libcuda.so.1. -rpath-link looks up that exact soname, so
+    # without a libcuda.so.1 the transitive cu* driver symbols stay unresolved at link time. Provide
+    # a build-only versioned symlink to the stub (real libcuda.so.1 is injected at container runtime).
+    CUDA_SONAME_DIR=/tmp/cuda-soname-compat
+    mkdir -p "${CUDA_SONAME_DIR}"
+    if [ -n "${CUDA_STUBS_DIR}" ]; then
+        ln -sf "${CUDA_STUBS_DIR}/libcuda.so" "${CUDA_SONAME_DIR}/libcuda.so.1"
+        ln -sf "${CUDA_STUBS_DIR}/libcuda.so" "${CUDA_SONAME_DIR}/libcuda.so"
+    fi
+    export LD_LIBRARY_PATH=${TEGRA_LIB_DIR}:${CUDA_SONAME_DIR}:${CUDA_STUBS_DIR}:${CUDA_LIB_DIR}:$(pwd)/stubs:${LD_LIBRARY_PATH:-}
+    EXTRA_NVMPI_LDFLAGS="-L${TEGRA_LIB_DIR} -Wl,-rpath-link,${TEGRA_LIB_DIR}"
+    [ -n "${CUDA_STUBS_DIR}" ] && EXTRA_NVMPI_LDFLAGS="${EXTRA_NVMPI_LDFLAGS} -L${CUDA_STUBS_DIR} -Wl,-rpath-link,${CUDA_STUBS_DIR} -L${CUDA_SONAME_DIR} -Wl,-rpath-link,${CUDA_SONAME_DIR}"
+    [ -n "${CUDA_LIB_DIR}" ] && EXTRA_NVMPI_LDFLAGS="${EXTRA_NVMPI_LDFLAGS} -Wl,-rpath-link,${CUDA_LIB_DIR}"
+else
+    export LD_LIBRARY_PATH=$(pwd)/stubs:${LD_LIBRARY_PATH:-}   # tegra multimedia libs aren't available in image, so use stubs for ffmpeg build
+    EXTRA_NVMPI_LDFLAGS=""
+fi
 mkdir build
 cd build
-cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=$INSTALL_PREFIX
+cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=$INSTALL_PREFIX ${EXTRA_NVMPI_LDFLAGS:+-DCMAKE_SHARED_LINKER_FLAGS="${EXTRA_NVMPI_LDFLAGS}"}
-make -j$(nproc)
+make -j"$(nproc)"
 make install
 cd ../../
 
@@ -57,14 +148,24 @@ wget -q https://ffmpeg.org/releases/ffmpeg-6.0.tar.xz
 tar xaf ffmpeg-*.tar.xz && rm ffmpeg-*.tar.xz && cd ffmpeg-*
 patch -p1 < ../jetson-ffmpeg/ffmpeg_patches/ffmpeg6.0_nvmpi.patch
 export PKG_CONFIG_PATH=$INSTALL_PREFIX/lib/pkgconfig
+# On R39/JP7, ffmpeg's `-lnvmpi` configure probe (and final link) must see the real tegra libs in
+# /usr/lib/aarch64-linux-gnu/nvidia AND the CUDA driver/runtime stubs to resolve everything libnvmpi
+# transitively pulls in (NvBufSurfaceGetDeviceInfo from libnvbufsurface, and that lib's own
+# libcuda.so.1 / cu* driver-API deps). Reuse the same link flags assembled for the libnvmpi build
+# (TEGRA + CUDA stubs + CUDA lib dirs). Pre-R39 leaves configure flags unchanged.
+FFMPEG_EXTRA_LDFLAGS=""
+if [ "${JETSON_R39}" = "1" ]; then
+    FFMPEG_EXTRA_LDFLAGS="${EXTRA_NVMPI_LDFLAGS}"
+fi
 # enable Jetson codecs but disable dGPU codecs
 ./configure --cc='ccache gcc' --cxx='ccache g++' \
             --enable-shared --disable-static --prefix=$INSTALL_PREFIX \
             --enable-gpl --enable-libx264  --enable-libx265 \
             --enable-nvmpi --enable-ffnvcodec --enable-cuda-llvm \
             --disable-cuvid --disable-nvenc --disable-nvdec \
+            ${FFMPEG_EXTRA_LDFLAGS:+--extra-ldflags="${FFMPEG_EXTRA_LDFLAGS}"} \
     || { cat ffbuild/config.log && false; }
-make -j$(nproc)
+make -j"$(nproc)"
 make install
 cd ../
 

docker/tensorrt/detector/build_onnxruntime_arm64.sh

@@ -0,0 +1,72 @@
+#!/bin/bash
+
+set -euxo pipefail
+
+ONNXRUNTIME_VERSION=${ONNXRUNTIME_VERSION:-1.25.1}
+ONNXRUNTIME_BRANCH=${ONNXRUNTIME_BRANCH:-rel-1.25.1}
+ORT_PARALLEL=${ORT_PARALLEL:-4}
+ORT_NVCC_THREADS=${ORT_NVCC_THREADS:-1}
+
+mkdir -p /trt-wheels /workspace
+
+pip3 install --upgrade pip setuptools wheel packaging numpy
+
+command -v nvcc
+test -f /usr/local/cuda/include/cuda.h
+
+require_header() {
+    local pattern="$1"
+    local header
+
+    for header in /usr/include/${pattern} /usr/include/aarch64-linux-gnu/${pattern}; do
+        if [[ -e "${header}" ]]; then
+            echo "${header}"
+            return 0
+        fi
+    done
+
+    echo "Missing required header matching ${pattern}" >&2
+    return 1
+}
+
+require_ldconfig_entry() {
+    local lib_name="$1"
+
+    if ! ldconfig -p | grep -q "${lib_name}\\.so"; then
+        echo "Missing ldconfig entry for ${lib_name}" >&2
+        return 1
+    fi
+}
+
+require_header "cudnn.h"
+require_header "cudnn_version*.h"
+require_header "NvInfer.h"
+require_header "NvOnnxParser.h"
+
+require_ldconfig_entry "libnvinfer"
+require_ldconfig_entry "libnvinfer_plugin"
+require_ldconfig_entry "libnvonnxparser"
+
+cd /workspace
+rm -rf onnxruntime
+git clone --recursive --branch "${ONNXRUNTIME_BRANCH}" https://github.com/microsoft/onnxruntime.git onnxruntime
+
+cd /workspace/onnxruntime
+./build.sh \
+    --config Release \
+    --update \
+    --build \
+    --build_wheel \
+    --parallel "${ORT_PARALLEL}" \
+    --nvcc_threads "${ORT_NVCC_THREADS}" \
+    --allow_running_as_root \
+    --compile_no_warning_as_error \
+    --skip_tests \
+    --use_cuda \
+    --cuda_home /usr/local/cuda \
+    --use_tensorrt \
+    --cudnn_home /usr/lib/aarch64-linux-gnu \
+    --tensorrt_home /usr/lib/aarch64-linux-gnu \
+    --cmake_extra_defines onnxruntime_BUILD_UNIT_TESTS=OFF
+
+cp build/Linux/Release/dist/onnxruntime_gpu-"${ONNXRUNTIME_VERSION}"-*.whl /trt-wheels/

docker/tensorrt/detector/build_python_tensorrt.sh

@@ -2,6 +2,19 @@
 
 set -euxo pipefail
 
+TENSORRT_PYTHON_BRANCH=${TENSORRT_PYTHON_BRANCH:-release/8.6}
+
+if [[ -n "${TENSORRT_VER:-}" ]]; then
+  TENSORRT_MAJOR_MINOR="${TENSORRT_VER%.*}"
+  EXPECTED_TENSORRT_PYTHON_BRANCH="release/${TENSORRT_MAJOR_MINOR}"
+
+  if [[ "${TENSORRT_PYTHON_BRANCH}" == "auto" ]]; then
+    TENSORRT_PYTHON_BRANCH="${EXPECTED_TENSORRT_PYTHON_BRANCH}"
+  elif [[ "${TENSORRT_PYTHON_BRANCH}" != "${EXPECTED_TENSORRT_PYTHON_BRANCH}" ]]; then
+    echo "WARNING: TENSORRT_PYTHON_BRANCH=${TENSORRT_PYTHON_BRANCH} does not match TENSORRT_VER=${TENSORRT_VER}; auto would select ${EXPECTED_TENSORRT_PYTHON_BRANCH}" >&2
+  fi
+fi
+
 mkdir -p /trt-wheels
 
 if [[ "${TARGETARCH}" == "arm64" ]]; then
@@ -12,13 +25,44 @@ if [[ "${TARGETARCH}" == "arm64" ]]; then
   # Get python-tensorrt source
   mkdir -p /workspace
   cd /workspace
-  git clone -b release/8.6 https://github.com/NVIDIA/TensorRT.git --depth=1
+  git clone -b "${TENSORRT_PYTHON_BRANCH}" https://github.com/NVIDIA/TensorRT.git --depth=1
+
+  # The TensorRT python build runs the legacy `setup.py bdist_wheel`, which on the JP7 base
+  # otherwise resolves the deadsnakes/Debian system setuptools in /usr/lib/python3/dist-packages.
+  # That copy's wheel.bdist_wheel routes through distutils `install` -> `install_lib`, whose
+  # finalize_options reads a Debian-only `install_layout` option that the install command lacks
+  # under this setuptools, crashing with "AttributeError: install_layout". Install a clean,
+  # non-Debian setuptools+wheel into /usr/local (which python3.11 imports ahead of /usr/lib)
+  # so bdist_wheel uses a consistent toolchain. JP6 already had a compatible setuptools.
+  pip3 install --upgrade --ignore-installed 'setuptools>=70.1,<81' 'wheel>=0.43'
 
   # Collect dependencies
   EXT_PATH=/workspace/external && mkdir -p $EXT_PATH
   pip3 install pybind11 && ln -s /usr/local/lib/python3.11/dist-packages/pybind11 $EXT_PATH/pybind11
   ln -s /usr/include/python3.11 $EXT_PATH/python3.11
-  ln -s /usr/include/aarch64-linux-gnu/NvOnnxParser.h /workspace/TensorRT/parsers/onnx/
+
+  # TensorRT 10's python onnx bindings (pyOnnx.cpp) #include onnx-tensorrt headers such as
+  # errorHelpers.hpp that live ONLY in the parsers/onnx submodule (onnx-tensorrt), not in the
+  # base image's system include or the shallow TensorRT clone. TRT 8.6 bindings did not need
+  # these, so a lone NvOnnxParser.h symlink sufficed there. Populate the submodule at its pinned
+  # ref so CMake's ONNX_INC_DIR=${TENSORRT_ROOT}/parsers/onnx resolves every header it needs.
+  cd /workspace/TensorRT
+  if git submodule update --init --depth=1 parsers/onnx 2>/dev/null && [[ -e parsers/onnx/errorHelpers.hpp ]]; then
+    :
+  else
+    # Fallback: clone onnx-tensorrt at the branch recorded in .gitmodules for this TRT release.
+    ONNX_TRT_BRANCH="$(git config -f .gitmodules submodule.parsers/onnx.branch || echo '')"
+    rm -rf parsers/onnx
+    git clone --depth=1 ${ONNX_TRT_BRANCH:+-b "${ONNX_TRT_BRANCH}"} https://github.com/onnx/onnx-tensorrt.git parsers/onnx
+  fi
+  # Prefer the system NvOnnxParser.h (matches the installed libnvonnxparser) when present.
+  for header in /usr/include/aarch64-linux-gnu/NvOnnxParser.h /usr/include/NvOnnxParser.h; do
+    if [[ -e "$header" ]]; then
+      ln -sf "$header" /workspace/TensorRT/parsers/onnx/NvOnnxParser.h
+      break
+    fi
+  done
+  cd /workspace
 
   # Build wheel
   cd /workspace/TensorRT/python

docker/tensorrt/detector/rootfs/etc/s6-overlay/s6-rc.d/trt-model-prepare/run

@@ -10,11 +10,12 @@ set -o errexit -o nounset -o pipefail
 
 MODEL_CACHE_DIR=${MODEL_CACHE_DIR:-"/config/model_cache/tensorrt"}
 TRT_VER=${TRT_VER:-$(cat /etc/TENSORRT_VER)}
+TRT_MAJOR=${TRT_VER%%.*}
 OUTPUT_FOLDER="${MODEL_CACHE_DIR}/${TRT_VER}"
 YOLO_MODELS=${YOLO_MODELS:-""}
 
 # Create output folder
-mkdir -p ${OUTPUT_FOLDER}
+mkdir -p "${OUTPUT_FOLDER}"
 
 FIRST_MODEL=true
 MODEL_DOWNLOAD=""
@@ -28,9 +29,9 @@ fi
 for model in ${YOLO_MODELS//,/ }
 do
     # Remove old link in case path/version changed
-    rm -f ${MODEL_CACHE_DIR}/${model}.trt
+    rm -f "${MODEL_CACHE_DIR}/${model}.trt"
 
-    if [[ ! -f ${OUTPUT_FOLDER}/${model}.trt ]]; then
+    if [[ ! -f "${OUTPUT_FOLDER}/${model}.trt" ]]; then
         if [[ ${FIRST_MODEL} = true ]]; then
             MODEL_DOWNLOAD="${model%-dla}";
             MODEL_CONVERT="${model}"
@@ -40,7 +41,7 @@ do
             MODEL_CONVERT+=",${model}";
         fi
     else
-        ln -s ${OUTPUT_FOLDER}/${model}.trt ${MODEL_CACHE_DIR}/${model}.trt
+        ln -s "${OUTPUT_FOLDER}/${model}.trt" "${MODEL_CACHE_DIR}/${model}.trt"
     fi
 done
 
@@ -50,8 +51,8 @@ if [[ -z ${MODEL_CONVERT} ]]; then
 fi
 
 # Setup ENV to select GPU for conversion
-if [ ! -z ${TRT_MODEL_PREP_DEVICE+x} ]; then
+if [ -n "${TRT_MODEL_PREP_DEVICE+x}" ]; then
-    if [ ! -z ${CUDA_VISIBLE_DEVICES+x} ]; then
+    if [ -n "${CUDA_VISIBLE_DEVICES+x}" ]; then
         PREVIOUS_CVD="$CUDA_VISIBLE_DEVICES"
         unset CUDA_VISIBLE_DEVICES
     fi
@@ -67,12 +68,30 @@ if [[ "$(arch)" == "aarch64" ]]; then
     if [[ ! -e /usr/lib/aarch64-linux-gnu/tegra && ! -e /usr/lib/aarch64-linux-gnu/tegra-egl ]]; then
         echo "ERROR: Container must be launched with nvidia runtime"
         exit 1
-    elif [[ ! -e /usr/lib/aarch64-linux-gnu/libnvinfer.so.8 ||
+    fi
-            ! -e /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.8 ||
+
-            ! -e /usr/lib/aarch64-linux-gnu/libnvparsers.so.8 ||
+    TRT_LIB_DIR=/usr/lib/aarch64-linux-gnu
-            ! -e /usr/lib/aarch64-linux-gnu/libnvonnxparser.so.8 ]]; then
+    REQUIRED_TRT_LIBS=(
-        echo "ERROR: Please run the following on the HOST:"
+        "${TRT_LIB_DIR}/libnvinfer.so.${TRT_MAJOR}"
-        echo "  sudo apt install libnvinfer8 libnvinfer-plugin8 libnvparsers8 libnvonnxparsers8 nvidia-container"
+        "${TRT_LIB_DIR}/libnvinfer_plugin.so.${TRT_MAJOR}"
+        "${TRT_LIB_DIR}/libnvonnxparser.so.${TRT_MAJOR}"
+    )
+
+    if (( TRT_MAJOR < 10 )) || [[ -e "${TRT_LIB_DIR}/libnvparsers.so.${TRT_MAJOR}" ]]; then
+        REQUIRED_TRT_LIBS+=("${TRT_LIB_DIR}/libnvparsers.so.${TRT_MAJOR}")
+    fi
+
+    MISSING_TRT_LIBS=()
+    for TRT_LIB in "${REQUIRED_TRT_LIBS[@]}"; do
+        if [[ ! -e "${TRT_LIB}" ]]; then
+            MISSING_TRT_LIBS+=("${TRT_LIB}")
+        fi
+    done
+
+    if (( ${#MISSING_TRT_LIBS[@]} > 0 )); then
+        echo "ERROR: Missing TensorRT runtime libraries:"
+        printf '  %s\n' "${MISSING_TRT_LIBS[@]}"
+        echo "ERROR: Install the matching TensorRT ${TRT_MAJOR} runtime packages and nvidia-container on the HOST."
         exit 1
     fi
 fi
@@ -83,33 +102,33 @@ echo "Generating the following TRT Models: ${MODEL_CONVERT}"
 cd /usr/local/src/tensorrt_demos/yolo
 
 echo "Downloading yolo weights"
-./download_yolo.sh $MODEL_DOWNLOAD 2> /dev/null
+./download_yolo.sh "${MODEL_DOWNLOAD}" 2> /dev/null
 
 for model in ${MODEL_CONVERT//,/ }
 do
-    python3 yolo_to_onnx.py -m ${model%-dla} > /dev/null
+    python3 yolo_to_onnx.py -m "${model%-dla}" > /dev/null
 
     echo -e "\nGenerating ${model}.trt. This may take a few minutes.\n"; start=$(date +%s)
     if [[ $model == *-dla ]]; then
-        cmd="python3 onnx_to_tensorrt.py -m ${model%-dla} --dla_core 0"
+        cmd=(python3 onnx_to_tensorrt.py -m "${model%-dla}" --dla_core 0)
     else
-        cmd="python3 onnx_to_tensorrt.py -m ${model}"
+        cmd=(python3 onnx_to_tensorrt.py -m "${model}")
     fi
-    $cmd > /tmp/onnx_to_tensorrt.log || { cat /tmp/onnx_to_tensorrt.log && continue; }
+    "${cmd[@]}" > /tmp/onnx_to_tensorrt.log || { cat /tmp/onnx_to_tensorrt.log && continue; }
 
-    mv ${model%-dla}.trt ${OUTPUT_FOLDER}/${model}.trt;
+    mv "${model%-dla}.trt" "${OUTPUT_FOLDER}/${model}.trt";
-    ln -s ${OUTPUT_FOLDER}/${model}.trt ${MODEL_CACHE_DIR}/${model}.trt
+    ln -s "${OUTPUT_FOLDER}/${model}.trt" "${MODEL_CACHE_DIR}/${model}.trt"
     echo "Generated ${model}.trt in $(($(date +%s)-start)) seconds"
 done
 
 # Restore ENV after conversion
-if [ ! -z ${TRT_MODEL_PREP_DEVICE+x} ]; then
+if [ -n "${TRT_MODEL_PREP_DEVICE+x}" ]; then
     unset CUDA_VISIBLE_DEVICES
-    if [ ! -z ${PREVIOUS_CVD+x} ]; then
+    if [ -n "${PREVIOUS_CVD+x}" ]; then
         export CUDA_VISIBLE_DEVICES="$PREVIOUS_CVD"
     fi
 fi
 
 # Print which models exist in output folder
 echo "Available tensorrt models:"
-cd ${OUTPUT_FOLDER} && ls *.trt;
+cd "${OUTPUT_FOLDER}" && ls -- *.trt;

docker/tensorrt/detector/tensorrt_libyolo.sh

@@ -7,6 +7,21 @@ SCRIPT_DIR="/usr/local/src/tensorrt_demos"
 # Clone tensorrt_demos repo
 git clone --depth 1 https://github.com/NateMeyer/tensorrt_demos.git -b conditional_download
 
+# CUDA 13 removed libnvToolsExt.so (NVTX is now header-only nvtx3). The plugin Makefile links
+# -lnvToolsExt only for optional NVTX profiling annotations, so strip it when the lib is absent —
+# the link then succeeds on JP7 / CUDA 13. No-op on CUDA 12 (JP6), where the lib still exists.
+if ! ldconfig -p | grep -q 'libnvToolsExt\.so'; then
+    sed -i 's/-lnvToolsExt//g' ./tensorrt_demos/plugins/Makefile
+fi
+
+# TensorRT 10 (JP7 base) dropped libnvparsers.so (the legacy UFF/Caffe parsers). The plugin
+# Makefile links -lnvparsers from an over-broad LIBS list, but the YOLO custom layer never uses
+# it, so strip it when the lib is absent — the link then succeeds on TRT 10. No-op on TRT 8
+# (JP5/JP6), where libnvparsers still ships.
+if ! ldconfig -p | grep -q 'libnvparsers\.so'; then
+    sed -i 's/-lnvparsers//g' ./tensorrt_demos/plugins/Makefile
+fi
+
 # Build libyolo
 if [ ! -e /usr/local/cuda ]; then
     ln -s /usr/local/cuda-* /usr/local/cuda

docker/tensorrt/trt.hcl

@@ -13,6 +13,24 @@ variable "TRT_BASE" {
 variable "COMPUTE_LEVEL" {
   default = ""
 }
+variable "BUILD_ONNXRUNTIME_FROM_SOURCE" {
+  default = "0"
+}
+variable "ONNXRUNTIME_VERSION" {
+  default = "1.25.1"
+}
+variable "ONNXRUNTIME_BRANCH" {
+  default = "rel-1.25.1"
+}
+variable "TENSORRT_PYTHON_BRANCH" {
+  default = "release/8.6"
+}
+variable "L4T_APT_RELEASE" {
+  default = ""
+}
+variable "JETSON_SOC_REPO" {
+  default = ""
+}
 variable "BASE_HOOK" {
   # Ensure an up-to-date python 3.11 is available in jetson images
   default = <<EOT
@@ -35,6 +53,12 @@ target "_build_args" {
     SLIM_BASE = SLIM_BASE,
     TRT_BASE = TRT_BASE,
     COMPUTE_LEVEL = COMPUTE_LEVEL,
+    BUILD_ONNXRUNTIME_FROM_SOURCE = BUILD_ONNXRUNTIME_FROM_SOURCE,
+    ONNXRUNTIME_VERSION = ONNXRUNTIME_VERSION,
+    ONNXRUNTIME_BRANCH = ONNXRUNTIME_BRANCH,
+    TENSORRT_PYTHON_BRANCH = TENSORRT_PYTHON_BRANCH,
+    L4T_APT_RELEASE = L4T_APT_RELEASE,
+    JETSON_SOC_REPO = JETSON_SOC_REPO,
     BASE_HOOK = BASE_HOOK
   }
   platforms = ["linux/${ARCH}"]

docker/tensorrt/trt.mk

@@ -2,9 +2,11 @@ BOARDS += trt
 
 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
+JETPACK7_BASE ?= nvcr.io/nvidia/tensorrt:26.02-py3-igpu
 X86_DGPU_ARGS := ARCH=amd64 COMPUTE_LEVEL="50 60 70 80 90"
 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)
+JETPACK7_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK7_BASE) SLIM_BASE=$(JETPACK7_BASE) TRT_BASE=$(JETPACK7_BASE) BUILD_ONNXRUNTIME_FROM_SOURCE=1 ONNXRUNTIME_VERSION=1.25.1 ONNXRUNTIME_BRANCH=rel-1.25.1 TENSORRT_PYTHON_BRANCH=auto L4T_APT_RELEASE=r39.2 JETSON_SOC_REPO=som
 
 local-trt: version
 	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
@@ -21,6 +23,11 @@ local-trt-jp6: version
 		--set tensorrt.tags=frigate:latest-tensorrt-jp6 \
 		--load
 
+local-trt-jp7: version
+	$(JETPACK7_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
+		--set tensorrt.tags=frigate:latest-tensorrt-jp7 \
+		--load
+
 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
@@ -28,6 +35,8 @@ build-trt:
 		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5
 	$(JETPACK6_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
 		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp6
+	$(JETPACK7_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
+		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp7
 
 push-trt: build-trt
 	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
@@ -39,3 +48,6 @@ push-trt: build-trt
 	$(JETPACK6_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
 		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp6 \
 		--push
+	$(JETPACK7_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
+		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp7 \
+		--push

docs/docs/configuration/hardware_acceleration_enrichments.md

@@ -20,7 +20,7 @@ Object detection and enrichments (like Semantic Search, Face Recognition, and Li
 
 - **Nvidia**
   - Nvidia GPUs will automatically be detected and used for enrichments in the `-tensorrt` Frigate image.
-  - Jetson devices will automatically be detected and used for enrichments in the `-tensorrt-jp6` Frigate image.
+  - 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.
 
 - **RockChip**
   - RockChip NPU will automatically be detected and used for semantic search v1 and face recognition in the `-rk` Frigate image.

docs/docs/configuration/hardware_acceleration_video.md

@@ -359,7 +359,7 @@ Or map in all the `/dev/video*` devices.
 
 ## NVIDIA Jetson
 
-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.
+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.
 
 You will need to use the image with the nvidia container runtime:
 
@@ -369,7 +369,7 @@ You will need to use the image with the nvidia container runtime:
 docker run -d \
   ...
   --runtime nvidia
-  ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6
+  ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp7
 ```
 
 ### Docker Compose - Jetson
@@ -378,10 +378,12 @@ docker run -d \
 services:
   frigate:
     ...
-    image: ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6
+    image: ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp7
     runtime: nvidia   # Add this
 ```
 
+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.
+
 :::note
 
 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`:

docs/docs/configuration/object_detectors.md

@@ -46,8 +46,8 @@ Frigate supports multiple different detectors that work on different types of ha
 
 **Nvidia Jetson** <CommunityBadge />
 
-- [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Jetson devices, using one of many default models.
+- [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on JetPack 6 / L4T R36 Jetson devices, using one of many default models.
-- [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.
+- [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.
 
 **Rockchip** <CommunityBadge />
 
@@ -437,7 +437,7 @@ If the correct build is used for your GPU then the GPU will be detected and used
 
 - **Nvidia**
   - Nvidia GPUs will automatically be detected and used with the ONNX detector in the `-tensorrt` Frigate image.
-  - Jetson devices will automatically be detected and used with the ONNX detector in the `-tensorrt-jp6` Frigate image.
+  - 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.
 
 :::
 
@@ -567,7 +567,9 @@ For detailed instructions on compiling models, refer to the [MemryX Compiler](ht
 
 ## NVidia TensorRT Detector
 
-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.
+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.
+
+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.
 
 ### Generate Models
 

docs/docs/frigate/hardware.md

@@ -90,7 +90,7 @@ Frigate supports multiple different detectors that work on different types of ha
   - [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx)
   - Runs well with any size models including large
 
-- <CommunityBadge /> [Jetson](#nvidia-jetson): Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6.
+- <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.
 
 **Rockchip** <CommunityBadge />
 
@@ -258,9 +258,9 @@ Inference speeds may vary depending on the host platform. The above data was mea
 
 ### Nvidia Jetson
 
-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).
+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).
 
-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.
+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.
 
 ### Rockchip platform
 

docs/docs/frigate/installation.md

@@ -556,7 +556,8 @@ 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-jp6` - Frigate build optimized for Nvidia Jetson devices running Jetpack 6
+- `stable-tensorrt-jp6` - Frigate build optimized for Nvidia Jetson devices running JetPack 6 / L4T R36
+- `stable-tensorrt-jp7` - Frigate build optimized for Nvidia Jetson devices running JetPack 7.2 / L4T R39.2
 - `stable-rk` - Frigate build for SBCs with Rockchip SoC
 
 ## Home Assistant App

docs/src/components/DockerComposeGenerator/config/config.yaml

@@ -35,9 +35,10 @@ devices:
     helpType: "warning"
     needsNvidiaConfig: true
 
-  - id: "stable-tensorrt-jp6"
+  - &jetsonDevice
-    name: "NVIDIA Jetson"
+    id: "stable-tensorrt-jp6"
-    description: "Jetson development board"
+    name: "NVIDIA Jetson JP6"
+    description: "JetPack 6 / L4T R36"
     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>'
     svgStyle:
       width: 50px
@@ -46,10 +47,17 @@ devices:
       padding-bottom: 15px
     imageTag: "stable-tensorrt-jp6"
     autoHardware: []
-    helpText: "NVIDIA Jetson devices automatically configure runtime: nvidia."
+    helpText: "JetPack 6 / L4T R36 Jetson devices automatically configure runtime: nvidia."
     helpType: "info"
     runtime: "nvidia"
 
+  - <<: *jetsonDevice
+    id: "stable-tensorrt-jp7"
+    name: "NVIDIA Jetson JP7"
+    description: "JetPack 7.2 / L4T R39.2"
+    imageTag: "stable-tensorrt-jp7"
+    helpText: "JetPack 7.2 / L4T R39.2 Jetson devices automatically configure runtime: nvidia."
+
   - id: "stable-rocm"
     name: "AMD GPU"
     description: "ROCm acceleration"
@@ -176,6 +184,7 @@ hardware:
     description: "Pass through /dev/dri for GPU hardware acceleration (Intel/AMD)."
     disabledWhen:
       - "stable-tensorrt-jp6"
+      - "stable-tensorrt-jp7"
       - "apple-silicon"
     devices:
       - host: "/dev/dri"
@@ -187,6 +196,7 @@ hardware:
     description: "Pass through /dev/accel for Intel NPU acceleration."
     disabledWhen:
       - "stable-tensorrt-jp6"
+      - "stable-tensorrt-jp7"
       - "apple-silicon"
       - "stable-rocm"
       - "stable-rk"
@@ -247,6 +257,7 @@ hardware:
     disabledWhen:
       - "stable-tensorrt"
       - "stable-tensorrt-jp6"
+      - "stable-tensorrt-jp7"
       - "stable-rocm"
       - "stable-rk"
       - "stable-synaptics"