Deep Learning
Keras
Log into a GPU node:
Sinteractive -p interactive -m 4G -G 1
Check that the GPU is visible:
nvidia-smi
Load parallel modules and python:
module purge
module load gcc cuda cudnn python/3.8.8
Create a virtual environment:environment. Here we will call it "venv_keras", but you may choose another name:
virtualenv -p python venv_keras
Activate the virtual environment:
source venv_keras/bin/activate
Install TensorFlow and Keras:
pip install tensorflow
pip install keras
Check that Keras was properly installed:
python -c 'import keras; print(keras.__version__)'
There might be a warning message and the output should be something like "2.5.0".
You may install extra packages that you deep learning code will use. For example:
pip install sklearn
pip install pandas
pip install matplotlib
Deactivate your virtual environment and logout from the GPU node:
deactivate
exit
TensorFlow
PyTorch