🎓Machine Learning on HPC - Introduction

The use of High Performance Computing (HPC) systems can have huge advantages for Machine Learning methods. Due to the heterogeneity of Machine Learning applications, the motivation to switch to an HPC system can be manifold, e.g. large memory requirements, GPU usage or increase of computational speed. This 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 this course.

Course Details

Agenda

• Access to the HPC system (e.g. ssh, Jupyterhub)
• Data transfer and storage of training data, models, source codes etc. (e.g. scp, dtcp, user space, workspaces)
• Setup of the required software environment (e.g. using module system, virtual environments, containers)
• Execution/testing/debugging of applications  (e.g. batch jobs, interactive jobs)
• Evaluation and storage of results
• Simple monitoring to optimize applications (Pika)

Registration

Register for the tutorial “Machine Learning on HPC – Introduction” until October 1, 2025 <<here>>. 

Handouts

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

Prerequisites

Participants should have knowledge of Python, Tensorflow or Pytorch and the use of the Linux shell. 

Learning Outcomes

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