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  • Using the Tensorflow C library on Blue Waters with the bwpy environment
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When logging onto Blue Waters:

  • Use the module load bwpy and bwpy-environ commands to set up the bwpy environment

The Tensorflow C Library is located in: /mnt/bwpy/single/usr/include/tensorflow/c:

  • In the C folder, one will find the Tensorflow c_api.h
    • Through what I could find, the Tensorflow C library on bwpy does not provide machine learning functions/ops that Tensorflow Python and Tensorflow C++ have. For example:
      • tf.matmul (Tensorflow Python)
      • tensorflow::ops::SparseReshape (Tensorflow Core)
    • This is why the Tensorflow Core Library is useful for pure c/c++ Tensorflow programs.
      • Tensorflow Core (C++)provides machine learning functions through a library of ops that the c_api.h can use in graph/model creation.

With only the c_api.h provided by the bwpy environment:

  • Given only the c_api.h we can still train an exported model created through a higher level Tensorflow language such as Tensorflow Python.
    • For this project, a basic MNIST Tensorflow Python script is used to export a .pb model. From there, the c_api.h is used to load the .pb model and then to train it with the downloaded MNIST data from

To compile/run the Tensorflow C program:

  • run the with the default python2 on bwpy to create the graph.pb
  • Use the module swap PrgEnv-cray PrgEnv-gnu commands to get an up to date gcc compiler that will be able to compile Tensorflow C.
  • Run the Makefile
  • Use ./train.exe

Readme, Files, and Resources for this project are in gitlab


  • Ultimately, the Tensorflow c_api.h is not enough on its own to create and train a model without the help of other Tensorflow libraries. The c_api.h is used as a "pipe lining" api to provide Tensorflow libraries like Tensorflow Core with a graph/model framework where Users can:
    • Input their custom/provided ops into.
    • Input prebuilt models into for training. 
  • Combined with a higher level Tensorflow language to pre-build a model, a User can still be able to avoid bulky compilation/execution (Python) by using a lightweight executable to train the model as produced by a Tensorflow C/C++ program.
  • If Users on Blue Waters want to do pure Tensorflow C++ programming, Tensorflow Core should be included in the next Tensorflow build. (If already included, make it easier to access/find) 

2:35 PM

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