Lecturers: Christoph Lehmann, Elias Werner, Lalith Manjunath, Lena Jurkschat, Apurv Deepak Kulkarni, Norman Koch
On Tuesday we will learn how to bring Machine Learning workflows to HPC. Due to the heterogeneity of Machine Learning applications, the motivation to move to an HPC system can be manifold, e.g. due to large memory requirements, GPU usage or increase in computational speed. Participants will learn how a typical machine learning workflow can be realised 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 in groups, which is also taken into account in the implementation of the machine learning workflow.
The guided hands-on and hackathon format provides a perfect opportunity to get in touch with the ZIH HPC system, to practice the learned HPC principles and to set up a machine learning pipeline for image classification in a competitive way.
A computer or laptop with SSH access to the cluster is required to take part in the hands-on section of this course. No further pre-knowledge is required.
