[[Category:Software]][[Category:AI and Machine Learning]] "Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano."https://keras.io/ If you are porting a Keras program to one of our clusters, you should follow [[Tutoriel Apprentissage machine/en|our tutorial on the subject]]. ==Installing== #Install [[TensorFlow]], CNTK, or Theano in a Python [[Python#Creating_and_using_a_virtual_environment|virtual environment]]. #Activate the Python virtual environment (named $HOME/tensorflow in our example). #:{{Command2|source $HOME/tensorflow/bin/activate}} #Install Keras in your virtual environment. #:{{Command2 |prompt=(tensorflow)_[name@server ~]$ |pip install keras}} === R package === This section details how to install Keras for R and use TensorFlow as the backend. #Install TensorFlow for R by following [[Tensorflow#R_package | these instructions]]. #Follow the instructions from the parent section. #Load the required modules. #:{{Command2|module load gcc/7.3.0 r/3.5.2}} # Launch R. #:{{Command2|R}} #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')) == References ==