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NVIDIA L4T TensorFlow

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Description

TensorFlow is an open-source software library for numerical computation using data flow graphs. This container contains TensorFlow pre-installed in a Python 3 environment to get up & running quickly with TensorFlow on Jetson.

Publisher

Google Brain Team

Latest Tag

r35.1.0-tf2.9-py3

Modified

November 1, 2022

Compressed Size

6.04 GB

Multinode Support

No

Multi-Arch Support

No

r35.1.0-tf2.9-py3 (Latest) Scan Results

Linux / arm64

TensorFlow Container for Jetson and JetPack

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

  • 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-pytorch images. Note that the TensorFlow 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-tensorflow 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)

    • l4t-tensorflow:r35.1.0-tf1.15-py3
      • TensorFlow 1.15.5
    • l4t-tensorflow:r35.1.0-tf2.9-py3
      • TensorFlow 2.9.1
  • JetPack 5.0.1 Developer Preview (L4T R34.1.1)

    • l4t-tensorflow:r34.1.1-tf1.15-py3
      • TensorFlow 1.15.5
    • l4t-tensorflow:r34.1.1-tf2.8-py3
      • TensorFlow 2.8.0
  • JetPack 5.0.0 Developer Preview (L4T R34.1.0)

    • l4t-tensorflow:r34.1.0-tf1.15-py3
      • TensorFlow 1.15.5
    • l4t-tensorflow:r34.1.0-tf2.8-py3
      • TensorFlow 2.8.0
  • JetPack 4.6.1 (L4T R32.7.1)

    • l4t-tensorflow:r32.7.1-tf1.15-py3
      • TensorFlow 1.15.5
    • l4t-tensorflow:r32.7.1-tf2.7-py3
      • TensorFlow 2.7.0
  • JetPack 4.6 (L4T R32.6.1)

    • l4t-tensorflow:r32.6.1-tf1.15-py3
      • TensorFlow 1.15.5
    • l4t-tensorflow:r32.6.1-tf2.5-py3
      • TensorFlow 2.5.0
  • JetPack 4.5 (L4T R32.5.0)

    • l4t-tensorflow:r32.5.0-tf1.15-py3
      • TensorFlow 1.15
    • l4t-tensorflow:r32.5.0-tf2.3-py3
      • TensorFlow 2.3.1
  • JetPack 4.4.1 (L4T R32.4.4)

    • l4t-tensorflow:r32.4.4-tf1.15-py3
      • TensorFlow 1.15
    • l4t-tensorflow:r32.4.4-tf2.3-py3
      • TensorFlow 2.3
  • JetPack 4.4 (L4T R32.4.3)

    • l4t-tensorflow:r32.4.3-tf1.15-py3
      • TensorFlow 1.15
    • l4t-tensorflow:r32.4.3-tf2.2-py3
      • TensorFlow 2.2
  • JetPack 4.4 Developer Preview (L4T R32.4.2)

    • l4t-tensorflow:r32.4.2-tf1.15-py3
      • TensorFlow 1.15

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

Running the Container

First pull one of the l4t-tensorflow 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 and want to use TensorFlow 1.15:

sudo docker pull nvcr.io/nvidia/l4t-tensorflow:r35.1.0-tf1.15-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-tensorflow:r35.1.0-tf1.15-py3

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

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-tensorflow:r35.1.0-tf1.15-py3

Dockerfiles

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

License

The l4t-tensorflow 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.