Machine learning on HPC - Introduction

NHR Tutorial (Online - Course language: German)

Tuesday, 01/11/2022, 10:00 am - 3:00 pm

Speaker: Andrei Politov, Elias Werner

Due to the heterogeneity of ML 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 ML 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 ML applications is often done by collaborative work within groups, which is also taken into account in the implementation of the ML workflow.

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
Performance analysis and optimization of applications (Score-P)

Handouts

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

Pre-Knowledge

HPC: none
ML: Basic knowledge of Python as well as the use of Tensorflow or Pytorch or R

Post-Knowledge

Implementation of ML workflows using specific examples, taking into account individual requirements

HPC-Certification Forum Links

The following links show the skill descriptions that should be taught in the respective course.

Registration

Link: https://event.zih.tu-dresden.de/nhr/ml-hpc-b
Registration is closing on 01/03/2022. The NHR tutorial is limited to 15 participants.
You will receive the access data shortly before the event by email to your registered email address.

Useful Information

Course language: German
Target group: HPC Basics / HPC User
If you have any further questions, please contact Anja Gerbes (anja.gerbes@tu-dresden.de).