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

• Data loading: pandas, numpy
• Visualization: matplotlib, Seaborn, Altair, Plotly, Bokeh
• Tensorflow and tensorboard
• PyTorch
• Scikit-learn

Access

From Open OnDemand

You can access these environments using the JupyterLab app from Open OnDemand by choosing the Python 3.7 or 3.9 kernel.

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

An 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
import socket
hostname = socket.gethostname()
os.environ['TENSORBOARD_PROXY_URL'] = f"/rnode/{hostname}/%PORT%/"

Sample notebooks

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