Deep Learning

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. 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

If you want to make your installation more reproducible, you may proceed as follows:

1. Create a file called "requirements.txt" and write the package names inside:

tensorflow==2.4.1
keras==2.4.0
sklearn==0.24.2
pandas==1.2.4
mathplotlib==3.4.2

2. Proceed as above, but instead of installing the packages individually, type

pip install -r requirements.txt