TensorFlow for NVIDIA Jetson TX2

NVIDIA now has an official release for TensorFlow on the NVIDIA Jetson TX2 Development Kit!

This makes installing TensorFlow on the Jetson much less challenging. Here’s the shortcut version:

For Python 2.7

$ pip install –extra-index-url=https://developer.download.nvidia.com/compute/redist/jp33 tensorflow-gpu

For Python 3.5

pip3 install –extra-index-url=https://developer.download.nvidia.com/compute/redist/jp33 tensorflow-gpu

Here is the original announcement and the full installation document.

Here are some other useful links

NVIDIA DL frameworks guides

Jetson Downloads

Enjoy!

12 Comments

  1. Invalid requirement: ‘–extra-index-url=https://developer.download.nvidia.com/compute/redist/jp33’
    It looks like a path. File ‘–extra-index-url=https://developer.download.nvidia.com/compute/redist/jp33’ does not exist.

    I can’t use it.Why?

  2. Jim,

    Thanks for making everyone Life so much better.
    I also wanted to let everyone know that here is another Lecture that you can take online.

    https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class01_intro_python.ipynb

    Module Video Material

    Main video lecture (complete YouTube Playlist):

    Part 1.1: Course Overview
    Part 1.2: Machine Learning Background for Deep Learning, Keras and Tensorflow
    Part 1.3: Python Anaconda for Deep Learning, Keras and Tensorflow
    How to Submit a Module Assignment

    Watch one (or more) of these depending on how you want to setup your Python TensorFlow environment:

    Installing TensorFlow, Keras, and Python in Windows
    Installing TensorFlow, Keras, and Python in Mac
    Installing/Using IBM Cognitive Class Labs with TensorFlow/Keras
    Docker Image – A docker image that I created specifically for this class. Always tested with all class assignments and notes.

  3. Hello,Jim,I just install Jetpack4.1.1 on my nvidia xavier and then wanna install the tensorflow on the board.Thus I follow the official docunment:

    https://docs.nvidia.com/deeplearning/dgx/install-tf-xavier/index.html

    Unfortunately comfront some errors.
    Firstly,I install Nvidia SDK Manager:
    $ sudo apt install ./sdkmanager_0.9.11-3405_amd64.deb
    Some packages could not be installed. This may mean that you have
    requested an impossible situation or if you are using the unstable
    distribution that some required packages have not yet been created
    or been moved out of Incoming.
    The following information may help to resolve the situation:

    The following packages have unmet dependencies:
    sdkmanager:amd64 : Depends: libgconf-2-4:amd64 but it is not installable
    Depends: libcanberra-gtk-module:amd64 but it is not installable
    E: Unable to correct problems, you have held broken packages.

    Then,
    $ sudo apt install libgconf-2-4
    libgconf-2-4 is already the newest version (3.2.6-4ubuntu1).
    The following packages were automatically installed and are no longer required:
    libdbusmenu-gtk4 libdbusmenu-qt5-2 libgsettings-qt1 liblockfile-bin
    liblockfile1 libqt5sql5 libqt5sql5-sqlite lockfile-progs x11proto-dri2-dev
    x11proto-gl-dev
    Use ‘sudo apt autoremove’ to remove them.

    The dependencies are exist,but I still couldn’t install SDK. Why?

    Appreciate to receive your answers,thanks.

  4. Thx for the great article!

    Unluckily the NVIDIA l4t docker image (as supported by JetPack 4.2.1) or pip packages do not contain TensorFlow *Serving* which is quite interesting for inferencing on the edge.

    Have a look at https://github.com/helmuthva/jetson/tree/master/workflow/deploy/tensorflow-serving-base/src for a Dockerfile and .bazelrc to build the latest TensorFlow Serving (inc. TensorFlow Core) from master for NVIDIA Jetson devices.

    See https://github.com/helmuthva/jetson for the bigger picture – a multi-arch Kubernetes cluster with edge devices for inferencing.

  5. I have been watching your Github repository for a while now, very nice work! It seems the whole container area isn’t very well explained/explored on the Jetson currently. Hopefully your work helps clarify some of these issues. Also, you should put your project in the ‘Projects’ area in the ‘Jetson Projects’ area of the NVIDIA forums. That way it will get more exposure from people who are trying to spread the word. Thanks for reading!

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