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Building TensorFlow 2.0.0 on Jetson Nano

 4 years ago
source link: https://jkjung-avt.github.io/build-tensorflow-2.0.0/
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Building TensorFlow 2.0.0 on Jetson Nano

Oct 7, 2019

Quick link: jkjung-avt/jetson_nano

The TensorFlow team officially released version 2.0 a week ago. I wanted to test drive it on Jetson Nano. So I modified my previous scripts and built/installed tensorflow-2.0.0 for testing. As always, I shared the script on GitHub.

Reference

Prerequisite

Please refer to my previous TensorFlow 1.12.2 post. The prerequisite is the same.

Step-by-Step

  1. Uninstall tensorboard and tensorflow if a previous version has been installed.

    $ sudo pip3 uninstall -y tensorboard tensorflow

    It is also a good idea to clean up ‘libprotobuf’, the python ‘protobuf’ module and ‘bazel’ if you have installed some older versions previously.

    $ sudo rm /usr/local/lib/libproto*
$ sudo rm /usr/local/bin/protoc
$ sudo pip3 uninstall -y protobuf
$ sudo rm /usr/local/bin/bazel

  2. Clone my ‘jetson_nano’ repository from GitHub, which contains all the scripts.

    $ cd ${HOME}/project
$ git clone https://github.com/jkjung-avt/jetson_nano.git
$ cd jetson_nano

  3. Update ‘libprotobuf’ (3.8.0). This solves the “extremely long model loading time problem” of TF-TRT.

    $ ./install_protobuf-3.8.0.sh

    This script takes 1 hour or so to finish on the Jetson Nano.

    If you do not care about the TF-TRT problem and you do not want to compile libprotobuf by yourself, you might simply do:

    ### Alternative, but not recommended
$ sudo pip3 install protobuf==3.8.0

  4. Install ‘bazel’ (0.26.1), the build tool for tensorflow.

    $ ./install_bazel-0.26.1.sh

  5. Build and install tensorflow-2.0.0 by executing the following script. More specifically, this script would install requirements, download tensorflow-2.0.0 source, configure/build the code, build the pip3 wheel and install it on the system.

    $ ./install_tensorflow-2.0.0.sh

    Note this script would take a very long time (~40 hours) to run. Since bulding tensorflow requires a lot resources (memory & disk I/O), it is suggested all other applications (such as the web browser) and tasks terminated while tensorflow is being built.

    When the script finishes successfully, you’d see the following output:

    ......
tensorflow version: 2.0.0
tensorflow.test.is_built_with_cuda(): True
tensorflow.test.is_gpu_available(): True

Testing the installation

  • The API changed quite a bit in tensorflow-2.0.0 from 1.x. For example, there is no longer placeholder() and feed_dict(), and all the contrib.* stuffs are gone. Most of the libraries relying on tensorflow have not been updated for 2.x API. This makes testing a little bit difficult.

    So I referenced the official TensorFlow 2 quickstart for experts article and created a script to train/test a MNIST model using the 2.x API. (I reduced batch size from 32 to 16 in the code, otherwise the program seemed to hit RAM limit frequently.)

    $ cd ${HOME}/project/jetson_nano/tensorflow
$ python3 mnist.py
Fetching MNIST dataset...
Start training...
Epoch 1, Loss: 0.12405917793512344, Accuracy: 96.29833221435547, Test Loss: 0.05720750242471695, Test Accuracy: 98.16999816894531
Epoch 2, Loss: 0.03967945650219917, Accuracy: 98.74333190917969, Test Loss: 0.06687281280755997, Test Accuracy: 98.04999542236328
Epoch 3, Loss: 0.020061999559402466, Accuracy: 99.34832763671875, Test Loss: 0.05041925981640816, Test Accuracy: 98.48999786376953
Epoch 4, Loss: 0.011295714415609837, Accuracy: 99.61000061035156, Test Loss: 0.07434192299842834, Test Accuracy: 98.1199951171875
Epoch 5, Loss: 0.007720560301095247, Accuracy: 99.7550048828125, Test Loss: 0.07604508101940155, Test Accuracy: 98.18000030517578
MNIST training done.

    The resulting test accuracy (~98%) matched what was stated in the original article.

Additional notes

  • I chose ‘protobuf’ version “3.8.0” since it is the matching version in tensorflow-2.0 source code.

  • I chose ‘bazel’ version “0.26.1” for tensorflow-2.0.0 based on tensorflow’s official documentation: Tested build configurations.

  • My previous notes about --local_resources, environment variable settings, TF_CUDA_COMPUTE_CAPABILITIES and TF_TENSORRT_VERSION, etc. still apply. And I think my scripts would likely also work for Jetson TX1/TX2 and AGX Xavier. Please refer to my TensorFlow 1.12.2 post for details.

I haven’t done too much testing on this newly built tensorflow-2.0.0, so I’m not yet confident it’s without issues. Feel free to give it a try and report problems to me. I will try my best to find a solution and update the scripts if necessary.


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