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Machine learning with Python

The cluster offers ready-to-use Python environments including a set of libraries and tools adapted to data analysis and machine learning:


From Jupyter#

You can access these environments from JupyterHub by choosing the Python 3.7 or 3.9 kernel.

Python 3.7 Python 3.9

From the Unix / SLURM shell#

You can access these environments from module: module load python/3.7 or module load python/3.9.

Tensorflow and Tensorboard in notebooks#

By default, Tensorflow logs all information. To disable warning or error logs, you can change the value of the environment variable TF_CPP_MIN_LOG_LEVEL :

# disable tensorflow debug message
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# 0 = all messages are logged (default behavior)
# 1 = INFO messages are not printed
# 2 = INFO and WARNING messages are not printed
# 3 = INFO, WARNING, and ERROR messages are not printed

A iPython magic command allows the integration of Tensorboard directly into notebooks. In order for this integration to work on JupyterHub, you need to set the environment variable TENSORBOARD_PROXY_URL to tell Jupyter that it needs to access Tensorboard through the JupyterHub proxy. To do this, simply add this cell to your notebook before calling Tensorboard command:

# Set proxy fro tensorboard access through JupyterHub
import os
os.environ['TENSORBOARD_PROXY_URL'] = f"/user/{os.environ.get('USER')}/proxy/%PORT%/"

Sample notebooks#

The following sample notebooks have been tested on the cluster with Python 3.7 and 3.9