Topic V

In this video lecture, the presenter discusses the Ray module, which is a unified framework for scaling Python applications, particularly in the context of distributed resources for machine learning. The lecture assumes access to an HPC system, specifically HPC Bura, and provides instructions on how to set up and use Ray on this system. The presenter demonstrates how to write a script to define the resources needed for the Ray cluster, including the number of nodes, tasks per node, CPUs per task, memory requirements, and the output file. The concept of head node and worker nodes in Ray clusters is explained. The lecture concludes by showcasing the output of an example case, which illustrates how Ray assigns and distributes resources across different nodes based on the requested number of CPUs.