Python Data Analysis on JupyterHub

Published: 3 Nov 2021 by HPC Team Freiburg

The bwHPC project cordially invites you to the workshop

Python Data Analysis with Dask, Pandas on JupyterHub

on 11.11.2021 starting at 14:00 CET.

The workshop briefly introduces the basics of Python. In the main part, the use of NumPy, Pandas and parallel Dask for Machine Learning under JuyterHub is presented with an application example. In this respect, the course is suitable for beginners and advanced students with a focus on parallel computing.

Access is via browser or WebEx client at: WebEx Link

The workshop is organized by the HS Esslingen, the University of Konstanz and the KIT. An account on the bwUniCluster is helpful, access to a JupyterHub with a (Conda) installation of the above mentioned packages is necessary.

NEMO2 has officially launched, transitioning from a testing phase to full production with expanded hardware, including AMD Instinct MI300A and Nvidia L40A nodes. NEMO1 is being phased out, with limited resources available until May 31st. Users are encouraged to transition to NEMO2 and consult the wiki for details.

NEMO2 uses Miniforge for conda environments, offering a streamlined setup with conda-forge as the default repository. The Miniforge module auto-initializes conda, simplifying environment activation without modifying shell profiles.

The AMD Genoa, Machine Learning and AI partitions for NEMO2 were delivered on December 4th. The acceptance of the storage has been delayed, so that NEMO2 could not yet start this year. However, calculations with the Milan nodes in NEMO1 are still possible.