"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.
- Install either TensorFlow, CNTK or Theano in a Python virtual environment.
- Activate the Python virtual environment (named $HOME/tensorflow in our example).
[name@server ~]$ source $HOME/tensorflow/bin/activate
- Install Keras in your virtual environment.
(tensorflow)_[name@server ~]$ pip install keras
This section details how to install Keras for R and use TensorFlow as the backend.
- Install TensorFlow for R by following the instructions provided here.
- Follow the instructions from the parent section.
- Load the required modules.
[name@server ~]$ module load gcc/7.3.0 r/3.5.2
- Launch R.
[name@server ~]$ R
- In R, install the Keras package with
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.