PyTorch Container for Jetson and JetPack

sudo apt-get install nvidia-jetpack

Running the Container

First pull one of the l4t-pytorch container tags from above, corresponding to the version of JetPack-L4T that you have installed on your Jetson. For example, if you are running the latest JetPack 5.1 (L4T R35.2.1) release:

sudo docker pull nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3

Then to start an interactive session in the container, run the following command:

sudo docker run -it --rm --runtime nvidia --network host nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3

You should then be able to start a Python3 interpreter and import torch and import torchvision.

Mounting Directories from the Host Device

To mount scripts, data, ect. from your Jetson’s filesystem to run inside the container, use Docker’s -v flag when starting your Docker instance:

sudo docker run -it --rm --runtime nvidia --network host -v /home/user/project:/location/in/container nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3

Note that if you want to use dual CSI cameras inside of container, you should include the following flags in your docker runcommand:

--volume /tmp/argus_socket:/tmp/argus_socket \
--device /dev/video0 \
--device /dev/video1