Add nvidia-l4t-base

.github/workflows/nvidia-l4t-base.yml

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+name: shellcheck
+
+on:
+  push:
+    branches: [ main ]
+    paths:
+      - 'nvidia/l4t-base/**'
+  pull_request:
+    branches: [ main ]
+    paths:
+      - 'nvidia/l4t-base/**'
+  workflow_dispatch:
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: nvidia/l4t-base
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install QEMU static binaries
+        uses: docker/setup-qemu-action@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to DockerHub
+        if: github.event_name != 'pull_request'
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.CONTAINER_REGISTRY_USERNAME }}
+          password: ${{ secrets.CONTAINER_REGISTRY_PASSWORD }}
+
+      - name: Build and push
+        uses: docker/bake-action@v4
+        with:
+          workdir: nvidia/l4t-base
+          push: ${{ github.event_name != 'pull_request' }}
+
+      - name: Get the image description
+        if: github.event_name != 'pull_request'
+        id: image_description
+        run: |
+          echo "image_description=$(docker buildx bake --print 2> /dev/null | jq -r '.target.nvidia-l4t-base-35-3-1.labels."org.opencontainers.image.description"')" >> $GITHUB_ENV
+        
+      - name: Update Docker Hub Description
+        if: github.event_name != 'pull_request'
+        uses: peter-evans/dockerhub-description@v3
+        with:
+          username: ${{ secrets.CONTAINER_REGISTRY_USERNAME }}
+          password: ${{ secrets.CONTAINER_DESCRIPTION_PASSWORD }}
+          repository: boxcutter/nvidia-l4t-base
+          short-description: ${{ env.image_description }}
+          readme-filepath: nvidia/l4t-base/README.md

nvidia/l4t-base/Containerfile

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+FROM docker.io/arm64v8/ubuntu:20.04 as nvidia-l4t-base
+
+ARG RELEASE=r35.4
+ARG DEBIAN_FRONTEND=noninteractive
+RUN apt-get update && \
+    apt-get upgrade -y && \
+    apt-get install -qq -y --no-install-recommends \
+    bc \
+    bzip2 \
+    can-utils \
+    ca-certificates \
+    freeglut3-dev \
+    gnupg2 \
+    gstreamer1.0-alsa \
+    gstreamer1.0-libav \
+    gstreamer1.0-plugins-bad \
+    gstreamer1.0-plugins-base \
+    gstreamer1.0-plugins-good \
+    gstreamer1.0-plugins-ugly \
+    gstreamer1.0-tools \
+    i2c-tools \
+    iw \
+    kbd \
+    kmod \
+    language-pack-en-base \
+    libapt-pkg-dev \
+    libcanberra-gtk3-module \
+    libgles2 \
+    libglu1-mesa-dev \
+    libglvnd-dev \
+    libgtk-3-0 \
+    libpython2.7 \
+    libudev1 \
+    libvulkan1 \
+    libzmq5 \
+    mtd-utils \
+    parted \
+    pciutils \
+    python \
+    python-pexpect \
+    python3-distutils \
+    python3-numpy \
+    sox \
+    udev \
+    vulkan-utils \
+    wget \
+    wireless-tools wpasupplicant && \
+    rm -rf /var/lib/apt/lists/* && apt-get clean
+
+COPY jetson-ota-public.key /etc/jetson-ota-public.key
+RUN apt-key add /etc/jetson-ota-public.key
+RUN echo "deb https://repo.download.nvidia.com/jetson/common $RELEASE main" >> /etc/apt/sources.list
+
+RUN echo "/usr/lib/aarch64-linux-gnu/tegra" >> /etc/ld.so.conf.d/nvidia-tegra.conf && \
+    echo "/usr/lib/aarch64-linux-gnu/tegra-egl" >> /etc/ld.so.conf.d/nvidia-tegra.conf
+
+RUN rm /usr/share/glvnd/egl_vendor.d/50_mesa.json
+RUN mkdir -p /usr/share/glvnd/egl_vendor.d/ && echo '\
+{\
+    "file_format_version" : "1.0.0",\
+    "ICD" : {\
+        "library_path" : "libEGL_nvidia.so.0"\
+    }\
+}' > /usr/share/glvnd/egl_vendor.d/10_nvidia.json
+
+RUN mkdir -p /usr/share/egl/egl_external_platform.d/ && echo '\
+{\
+    "file_format_version" : "1.0.0",\
+    "ICD" : {\
+        "library_path" : "libnvidia-egl-wayland.so.1"\
+    }\
+}' > /usr/share/egl/egl_external_platform.d/nvidia_wayland.json
+
+RUN ldconfig
+
+ENV NVIDIA_VISIBLE_DEVICES all
+ENV NVIDIA_DRIVER_CAPABILITIES all
+
+CMD ["/bin/bash"]

nvidia/l4t-base/README.md

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+# l4t-base
+
+Linux for Tegra (L4T) base image. Tegra is a system on a chip (SoC) series
+developed by NVIDIA.
+
+Based on:
+- https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-base
+- https://gitlab.com/nvidia/container-images/l4t-base
+
+## What is L4T?
+
+NVIDIA Linux4Tegra (L4T) package provides the bootloader, kernel, necessary
+firmwares, NVIDIA drivers for various accelerators present on Jetson modules,
+flashing utilities and a sample filesystem to be used on Jetson systems. The
+software packages contained in L4T provide the functionality necessary to run
+Linux on Jetson modules. For a detailed software overview please refer
+documentation provided here: (https://docs.nvidia.com/jetson/l4t/index.html)
+
+## Overview of the l4t-base container
+
+l4t-base docker image enables applications to be run in a container using the
+Nvidia Container Runtime on Jetson. It has a subset of packages from the l4t
+rootfs included within (Multimedia, Gstreamer, Camera, Core, 3D Core, Vulkan,
+Weston). The platform specific libraries providing hardware dependencies and
+select device nodes for a particular device are mounted by the NVIDIA container
+runtime into the l4t-base container from the underlying host, thereby providing
+necessary dependencies for l4t applications to execute within the container.
+This approach enables the l4t-base container to be shared between various
+Jetson devices.
+
+The image is tagged with the version corresponding to the release version of
+the associated l4t release. Based on this, the l4t-base:r34.1 container is
+intended to be run on devices executing the l4t r34.1 release.
+
+Starting with the r32.4.3 release, the Dockerfile for the l4t-base docker image
+is also being provided. This can be accessed at this [link](https://gitlab.com/nvidia/container-images/l4t-base). Users can use this to modify the contents to
+suit their needs.
+
+**Starting with the r34.1 release (JetPack 5.0 Developer Preview), the l4t-base
+will not bring CUDA, CuDNN and TensorRT from the host file system.** The
+l4t-base is meant to be used as the base container for containerizing
+applications for Jetson. Users can apt install Jetson packages and other
+software of their choice to extend the l4t-base dockerfile (see above) while
+building application containers. All JetPack components are hosted in the
+Debian Package Management server [here](https://repo.download.nvidia.com/jetson/).
+
+For CUDA and TensorRT applications, users can use the L4T CUDA and TensorRT
+runtime containers which have CUDA and CUDA/CuDNN/TensorRT respectively in the
+container itself. They can be used as base containers to containerize [CUDA](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-cuda)
+and [TensorRT](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-tensorrt) applications on Jetson.
+
+## Running l4t-base container
+
+### Prerequisites
+
+Ensure these prerequisites are available on your system:
+
+1. [NVIDIA Container Runtime on Jetson](https://github.com/nvidia/nvidia-docker/wiki#platform-support) Note that NVIDIA Container Runtime is available for install as part of [Nvidia JetPack](https://developer.nvidia.com/embedded/jetpack) in version 4.3 or newer
+
+### Pull the container
+
+Before running the l4t-base container, use Docker pull to ensure an up-to-date image is installed. Once the pull is complete, you can run the container image.
+
+Procedure
+
+1. In the Pull column, click the icon to copy the Docker pull command for the l4t-base container.
+
+1. Open a command prompt and paste the pull command. Docker will initiate a pull of the container from the NGC registry. Ensure the pull completes successfully before proceeding to the next step.
+
+### Run the container
+
+To run the container:
+
+1. Allow external applications to connect to the host's X display:
+
+       xhost *
+
+1. Run the docker container using the docker command
+
+       sudo docker run -it --rm \
+           --net=host \
+           --runtime nvidia \
+           --env DISPLAY=$DISPLAY \
+           --mount type=bind,source=/tmp/.X11-unix/,target=/tmp/.X11-unix \
+           docker.io/polymathrobotics/nvidia-l4t-base:r35.4.1
+
+### Exposing additional GPU features
+
+By default a limited set of device nodes and associated functionality is
+exposed within the l4t-base containers using the [mount plugin](https://github.com/NVIDIA/nvidia-docker/wiki/NVIDIA-Container-Runtime-on-Jetson#mount-plugins)
+capability. The list is documented [here](https://github.com/NVIDIA/nvidia-docker/wiki/NVIDIA-Container-Runtime-on-Jetson#supported-devices).
+
+User can expose additional devices using the [--device](https://docs.docker.com/engine/reference/commandline/run/#add-host-device-to-container---device)
+command option provided by docker. Directories and files can be bind mounted
+using the [-v](https://docs.docker.com/storage/bind-mounts/) option.
+
+Note that usage of some devices might need associated libraries to be available inside the container.
+
+### Creating Containers for Jetson
+
+The following L4T containers can be readily leveraged as base containers to
+create application containers: [CUDA](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-cuda), [TensorRT](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-tensorrt), [Deepstream](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/deepstream-l4t), [TensorFlow](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-tensorflow), [PyTorch](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch), [ML](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-ml).
+The [NVIDIA Container RUntime on Jetson](https://github.com/nvidia/nvidia-docker/wiki#platform-support) documentation has a FAQ on container usage.
+
+Refer to [this](https://gitlab.com/nvidia/container-images/l4t-jetpack)
+git repo for sample dockerfile. The JetPack dockerfile in that repo uses L4T
+container as base and creates a development container by installing CUDA,
+cuDNN, TensorRT, VPI and OpenCV inside the container.
+
+### License
+
+The l4t-base container includes various software packages with their respective licenses included within the container.
+
+### Suggested Reading
+
+For more information about l4t refer [Jetson Download Center](https://developer.nvidia.com/embedded/downloads).
+
+If you have questions, please refer to the [Jetson Forums](https://devtalk.nvidia.com/default/board/139/embedded-systems/1).

nvidia/l4t-base/docker-bake.hcl

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+variable "CONTAINER_REGISTRY" {
+  default = "docker.io/boxcutter"
+}
+
+variable "IMAGE_NAME" {
+  default = "nvidia-l4t-base"
+}
+
+target "default" {
+  name = "${IMAGE_NAME}-${replace(item.version, ".", "-")}"
+  target = "nvidia-l4t-base"
+  args = {
+    RELEASE = item.release
+  }
+  tags = [
+    "${CONTAINER_REGISTRY}/${IMAGE_NAME}:${item.version}"
+  ]
+  dockerfile = "Containerfile"
+  platforms = ["linux/arm64/v8"]
+  labels = {
+    "org.opencontainers.image.created" = timestamp()
+    "org.opencontainers.image.description" = "Linux for Tegra (L4T) base image for the NVIDIA Jetson embedded computing platform."
+    "org.opencontainers.image.licenses" = "Apache-2.0"
+    "org.opencontainers.image.source" = "https://github.com/boxcutter/oci"
+  }
+  matrix = {
+    item = [
+      { version = "35.3.1", release = "r35.3" },
+      { version = "35.4.1", release = "r35.4" },
+    ]
+  }
+}

nvidia/l4t-base/jetson-ota-public.key

@@ -0,0 +1,86 @@
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