A docker container to be used as base builder image for L4T/arm64 Contains build-esentials python3 and CUDA compiler/libraries To build the repository on an amd64 workstation you can use the following script.
#Configure docker for Nvidia
# Add the package repositories
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker
# Configure aarch64 emulation
sudo apt-get install qemu binfmt-support qemu-user-static # Install the qemu packages
docker run --rm --privileged multiarch/qemu-user-static --reset -p yes # This step will execute the registering scripts
To run interactively the builder image you can use the following sintax:
docker run -it --rm --net=host --runtime nvidia -e DISPLAY=$DISPLAY alessiomorale/jetson-builder-jp-r32.4.2-cv-4.3.0-py3Check the blog post Running Docker Containers for the NVIDIA Jetson Nano for more info.
Images are based on mdegans/l4t-base