Linux / arm64
l4t-ml docker image contains TensorFlow, PyTorch, JupyterLab, and other popular ML and data science frameworks such as scikit-learn, scipy, and Pandas pre-installed in a Python 3 environment. These containers support the following releases of JetPack for Jetson Nano, TX1/TX2, Xavier NX, AGX Xavier, and AGX Orin:
For additional machine learning containers for Jetson, see the
l4t-tensorflow images. Note that the TensorFlow and PyTorch pip wheel installers for aarch64 are available to download independently from the Jetson Zoo.
Depending on your version of JetPack-L4T, different tags of the
l4t-ml 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 5.0.2 (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)
l4t-ml containers require JetPack 4.4 or newer
First pull one of the
l4t-ml 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 5.0.2 (L4T R35.1.0) release:
sudo docker pull nvcr.io/nvidia/l4t-ml:r35.1.0-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-ml:r35.1.0-py3
You should then be able to start a Python3 interpreter and
import the packages above.
Unless a user-provided run command overrides the default, a JupyterLab server instance is automatically started along with the container.
You can connect to it by navigating your browser to
http://localhost:8888 (or substitute the IP address of your Jetson device if you wish to connect from a remote host, i.e. with the Jetson in headless mode). Note that the default password used to login to JupyterLab is
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-ml:r35.1.0-py3
To access or modify the Dockerfiles and scripts used to build this container, see this GitHub repo.
l4t-ml container includes various software packages with their respective licenses included within the container.
If you have any questions or need help, please visit the Jetson Developer Forums.