PyTorch Container for Jetson and JetPack

The l4t-pytorch docker image contains PyTorch and torchvision pre-installed in a Python 3 environment to get up & running quickly with PyTorch on Jetson. These containers support the following releases of JetPack for Jetson Nano, TX1/TX2, Xavier NX, AGX Xavier, AGX Orin, and Orin NX:

• JetPack 5.1 (L4T R35.2.1)
• JetPack 5.0.2 (L4T R35.1.0)
• JetPack 5.0.1 Developer Preview (L4T R34.1.1)
• JetPack 5.0.0 Developer Preview (L4T R34.1.0)
• JetPack 4.6.1 (L4T R32.7.1)
• JetPack 4.6 (L4T R32.6.1)
• JetPack 4.5 (L4T R32.5.0)
• JetPack 4.4.1 (L4T R32.4.4)
• JetPack 4.4 (L4T R32.4.3)
• JetPack 4.4 Developer Preview (L4T R32.4.2)

For additional machine learning containers for Jetson, see the l4t-ml and l4t-tensorflow images. Note that the PyTorch pip wheel installers for aarch64 used by these containers are available to download independently from the Jetson Zoo.

Package Versions

Depending on your version of JetPack-L4T, different tags of the l4t-pytorch container are available, each with support for Python 3. Be sure to clone a tag that matches the version of JetPack-L4T that you have installed on your Jetson.

• JetPack 5.1 (L4T R35.2.1)

  • l4t-pytorch:r35.2.1-pth2.0-py3
    • PyTorch v2.0.0
    • torchvision v0.14.1
    • torchaudio v0.13.1

• JetPack 5.0.2 (L4T R35.1.0)

  • l4t-pytorch:r35.1.0-pth1.11-py3
    • PyTorch v1.11.0
    • torchvision v0.12.0
    • torchaudio v0.11.0
  • l4t-pytorch:r35.1.0-pth1.12-py3
    • PyTorch v1.12.0
    • torchvision v0.13.0
    • torchaudio v0.12.0
  • l4t-pytorch:r35.1.0-pth1.13-py3
    • PyTorch v1.13.0
    • torchvision v0.13.0
    • torchaudio v0.12.0

• JetPack 5.0.1 Developer Preview (L4T R34.1.1)

  • l4t-pytorch:r34.1.1-pth1.11-py3
    • PyTorch v1.11.0
    • torchvision v0.12.0
    • torchaudio v0.11.0
  • l4t-pytorch:r34.1.1-pth1.12-py3
    • PyTorch v1.12.0
    • torchvision v0.12.0
    • torchaudio v0.11.0

• JetPack 5.0.0 Developer Preview (L4T R34.1.0)

  • l4t-pytorch:r34.1.0-pth1.12-py3
    • PyTorch v1.12.0
    • torchvision v0.12.0
    • torchaudio v0.11.0

• JetPack 4.6.1 (L4T R32.7.1)

  • l4t-pytorch:r32.7.1-pth1.9-py3
    • PyTorch v1.9.0
    • torchvision v0.10.0
    • torchaudio v0.9.0
  • l4t-pytorch:r32.7.1-pth1.10-py3
    • PyTorch v1.10.0
    • torchvision v0.11.0
    • torchaudio v0.10.0

• JetPack 4.6 (L4T R32.6.1)

  • l4t-pytorch:r32.6.1-pth1.8-py3
    • PyTorch v1.8.0
    • torchvision v0.9.0
    • torchaudio v0.8.0
  • l4t-pytorch:r32.6.1-pth1.9-py3
    • PyTorch v1.9.0
    • torchvision v0.10.0
    • torchaudio v0.9.0

• JetPack 4.5 (L4T R32.5.0)

  • l4t-pytorch:r32.5.0-pth1.6-py3
    • PyTorch v1.6.0
    • torchvision v0.7.0
    • torchaudio v0.6.0
  • l4t-pytorch:r32.5.0-pth1.7-py3
    • PyTorch v1.7.0
    • torchvision v0.8.0
    • torchaudio v0.7.0

• JetPack 4.4.1 (L4T R32.4.4)

  • l4t-pytorch:r32.4.4-pth1.6-py3
    • PyTorch v1.6.0
    • torchvision v0.7.0
    • torchaudio v0.6.0

• JetPack 4.4 (L4T R32.4.3)

  • l4t-pytorch:r32.4.3-pth1.6-py3
    • PyTorch v1.6.0
    • torchvision v0.7.0
    • torchaudio v0.6.0

• JetPack 4.4 Developer Preview (L4T R32.4.2)

  • l4t-pytorch:r32.4.2-pth1.5-py3
    • PyTorch v1.5.0
    • torchvision v0.6.0
  • l4t-pytorch:r32.4.2-pth1.4-py3
    • PyTorch v1.4.0
    • torchvision v0.5.0
  • l4t-pytorch:r32.4.2-pth1.3-py3
    • PyTorch v1.3.0
    • torchvision v0.4.2
  • l4t-pytorch:r32.4.2-pth1.2-py3
    • PyTorch v1.2.0
    • torchvision v0.4.0

note: the l4t-pytorch containers require JetPack 4.4 or newer

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

Dockerfiles

To access or modify the Dockerfiles and scripts used to build this container, see this GitHub repo.

License

The l4t-pytorch container includes various software packages with their respective licenses included within the container.

Getting Help & Support

If you have any questions or need help, please visit the Jetson Developer Forums and the PyTorch for Jetson topic.