Machine Learning on HPC – Introduction

Due to the heterogeneity of Machine Learning applications, the motivation to switch to an HPC system can be manifold, e.g. due to large memory requirements, GPU usage or increase of computation speed. The course presents how a typical Machine Learning workflow can be realized in the HPC environment. It is possible to switch to the HPC system at different points in the workflow – depending on the requirements. The development of Machine Learning applications is often done by collaborative work within groups, which is also taken into account in the implementation of the Machine Learning workflow.

Course Details

Title: Machine Learning on HPC – Introduction
Next Session: 27.09.2022, 10 a.m. – 3 p.m. (Speakers: Dr. Iryna Okhrin, Dr. Peter Winkler, Wenyu Zhang)
Registration: https://event.zih.tu-dresden.de/nhr/ml-hpc-b
Target Group: HPC Basics / HPC User
Language: English
Format: Tutorial

Agenda

Handouts

The course material (slides, sample application) will be available.

Pre-Knowledge

Participants should have basic knowledge of Python as well as the use of Tensorflow or Pytorch or R.

Post-Knowledge

Participants will gain knowledge about the implementation of Machine Learning workflows using specific examples, taking into account individual requirements.

Contact

Check out the other trainings by ScaDS.AI Dresden/Leipzig.

TU
Universität
Max
Leibnitz-Institut
Helmholtz
Hemholtz
Institut
Fraunhofer-Institut
Fraunhofer-Institut
Max-Planck-Institut
Institute
Max-Plank-Institut