Keras

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

If you are porting a Keras program to a Compute Canada cluster, you should follow our tutorial on the subject.

Installing

1. Install either 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 the instructions provided here.
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.

   devtools::install_github('rstudio/keras')
   

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.

library(keras)
use_virtualenv(Sys.getenv('VIRTUAL_ENV'))