Add l4t-jetpack

nvidia/l4t-jetpack/README.md

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+# l4t-jetpack
+
+## NVIDIA Jetpack
+
+NVIDIA JetPack SDK is the most comprehensive solution for building end-to-end
+accelerated AI applications. JetPack SDK provides a full development
+environment for hardware-accelerated AI-at-the-edge development. It includes
+complete set of libraries for acceleration of GPU computing, multimedia,
+graphics, and computer vision.
+
+Based on:
+- https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-jetpack
+- https://gitlab.com/nvidia/container-images/l4t-jetpack
+
+## Overview of Image
+
+NVIDIA L4T JetPack container containerizes all accelrated libraries that are
+included in JetPack SDK, which includes CUDA, cuDNN, TensorRT, VPI, Jetson
+Multimedia, and so on. This container can be used a development container for
+containerized development as it includes all JetPack SDK components. The docker
+file for this container can be found at this link. You can refer to the
+dockerfile and use that recipe as a reference to create your own development
+container (with both dev and runtime components) or deployment container (with
+only runtime components)
+
+## Running the container
+
+## Prerequisites
+
+Ensure that [NVIDIA Container Runtime on Jetson](https://github.com/nvidia/nvidia-docker/wiki#platform-support) is running on Jetson.
+
+You can run this container on top of JetPack SDK installation. Note that NVIDIA
+Container Runtime is available for install as part of [Nvidia JetPack](https://developer.nvidia.com/embedded/jetpack).
+
+You can also run this container on top of Jetson Linux BSP after installing NVIDIA Container Runtime using
+
+```
+sudo apt install nvidia-container
+```
+
+## Pull the container
+
+Before running the l4t-jetpack 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-jetpack 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 \
+        -e DISPLAY=$DISPLAY \
+        -v /tmp/.X11-unix/:/tmp/.X11-unix \
+        nvcr.io/nvidia/l4t-jetpack:r35.3.1
+
+Options explained:
+
+-it means run in interactive mode
+--rm will delete the container when finished
+--runtime nvidia will use the NVIDIA container runtime while running the l4t-base container
+-v is the mounting directory, and used to mount host's X11 display in the container filesystem to render output videos
+r35.3.1 is the tag for the jetpack image corresponding to the l4t release
+
+## Exposing addition features
+
+By default a limited set of device nodes and associated functionality is
+exposed within the cuda-runtime containers using the mount plugin capability.
+This 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.
+
+## Run a sample application
+
+Once you have successfully launched the l4t-jetpack container, you can run some tests inside it.
+
+1. To run the CUDA sample test, run the following commands within the container:
+
+    apt-get update && apt-get install -y --no-install-recommends make g++
+    cp -r /usr/local/cuda/samples /tmp
+    cd /tmp/samples/1_Utilities/deviceQuery
+    make
+    ./deviceQuery
+
+Output should indicate that the sample passed.
+
+1. To run the cuDNN sample test, run the following commands within the container:
+
+    cp -r /usr/src/cudnn_samples_v8/ /tmp/
+    cd /tmp/cudnn_samples_v8/conv_sample/
+    make
+    ./conv_sample
+
+Output should indicate that the sample passed.
+
+3. To run the TensorRT sample test, run the following commands within the container:
+
+    /usr/src/tensorrt/bin/trtexec --model=/usr/src/tensorrt/data/googlenet/googlenet.caffemodel --deploy=/usr/src/tensorrt/data/googlenet/googlenet.prototxt --output=prob
+    /usr/src/tensorrt/bin/trtexec --model=/usr/src/tensorrt/data/googlenet/googlenet.caffemodel --deploy=/usr/src/tensorrt/data/googlenet/googlenet.prototxt --output=prob --useDLACore=0 --allowGPUFallback
+
+Outputs should indicate that the samples passed.
+
+Note: DLA is not supported on Orin Nano
+
+1. To run the VPI sample test, run the following commands within the container:
+
+    apt install cmake
+    cp -r /opt/nvidia/vpi*/samples /tmp
+    cd /tmp/samples/01-convolve_2d/
+    mkdir build
+    cd build
+    cmake ..
+    make -j4
+    ./vpi_sample_01_convolve_2d cuda /opt/nvidia/vpi2/samples/assets/kodim08.png
+    ./vpi_sample_01_convolve_2d cpu /opt/nvidia/vpi2/samples/assets/kodim08.png
+
+Note: VPI currently does not support PVA backend within containers.
+
+## End User License Agreement
+
+By pulling and using the container, you accept the terms and conditions of this [End User License Agreement](https://docs.nvidia.com/jetson/jetpack/eula/index.html).
+
+## Documentation
+
+For more information on JetPack, including the release notes, programming model, APIs and developer tools, visit the [JetPack documentation site](https://developer.nvidia.com/embedded/jetpack).

nvidia/l4t-jetpack/r35.4.1/Containerfile

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+FROM docker.io/polymathrobotics/nvidia-l4t-base:35.4.1
+
+#
+# Install any utils needed for execution
+#
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    sudo \
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-get clean
+
+#
+# Install nvidia-cuda-dev for CUDA developer packages
+# Use nvidia-cuda if need CUDA runtime only
+#
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    nvidia-cuda-dev \
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-get clean
+
+#
+# Install nvidia-cudnn8-dev for CuDNN developer packages
+# Use nvidia-cudnn8 if need CuDNN runtime only
+#
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    nvidia-cudnn8-dev \
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-get clean
+
+#
+# Install nvidia-tensorrt-dev for TensorRT developer packages
+# Use nvidia-tensorrt if need TensorRT runtime only
+#
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    nvidia-tensorrt-dev \
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-get clean
+
+#
+# Install nvidia-vpi-dev for VPI developer packages
+# Use nvidia-vpi if need VPI runtime only
+#
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    nvidia-vpi-dev \
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-get clean
+
+#
+# Install nvidia-opencv-dev for OpenCV developer packages
+# Use nvidia-opencv if need OpenCV runtime only
+#
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    nvidia-opencv-dev \
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-get clean
+
+#
+# Install Multimedia API samples & libs
+#
+RUN apt-get update && apt-get download nvidia-l4t-jetson-multimedia-api \
+    && dpkg-deb -R ./nvidia-l4t-jetson-multimedia-api_*_arm64.deb ./mm-api \
+    && cp -r ./mm-api/usr/src/jetson_multimedia_api /usr/src/jetson_multimedia_api \
+    && ./mm-api/DEBIAN/postinst \
+    && rm -rf ./nvidia-l4t-jetson-multimedia-api_*_arm64.deb ./mm-api
+
+#Update libraries
+RUN ldconfig 
+
+#
+# Setup environment variables
+#
+ENV CUDA_HOME="/usr/local/cuda"
+ENV PATH="/usr/local/cuda/bin:${PATH}"
+ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:${LD_LIBRARY_PATH}"
+

nvidia/l4t-jetpack/r35.4.1/Polly.toml

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+[container_image]
+name = "nvidia-l4t-jetpack"
+tags = ["r35.4.1"]
+platforms = ["linux/arm64"]
+readme = "nvidia/l4t-jetpack/README.md"

nvidia/l4t-jetpack/r35.4.1/docker-bake.hcl

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+target "default" {
+  tags = ["docker.io/polymathrobotics/nvidia-l4t-jetpack:r35.4.1"]
+  dockerfile = "Containerfile"
+  platforms = ["linux/arm64"]
+}