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"Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano."[1]

If you are porting a Keras program to one of our clusters, you should follow our tutorial on the subject.


  1. Install TensorFlow, CNTK, or Theano in a Python virtual environment.
  2. Activate the Python virtual environment (named $HOME/tensorflow in our example).
    [name@server ~]$ source $HOME/tensorflow/bin/activate
  1. Install Keras in your virtual environment.
    (tensorflow)_[name@server ~]$ pip install keras

R package

This section details how to install Keras for R and use TensorFlow as the backend.

  1. Install TensorFlow for R by following these instructions.
  2. Follow the instructions from the parent section.
  3. Load the required modules.
    [name@server ~]$ module load gcc/7.3.0 r/3.5.2
  1. Launch R.
    [name@server ~]$ R
  1. In R, install the Keras package with devtools.

You are then good to go. Do not call install_keras() in R, as Keras and TensorFlow have already been installed in your virtual environment with pip. To use the Keras package installed in your virtual environment, enter the following commands in R after the environment has been activated.