This tutorial explores Machine Learning using GPU-enabled PyTorch for applications in high energy physics.
It follows directly from the Introduction to Machine Learning lesson written by Meirin Evans.
For physicists working on analysis in data-intensive fields such as particle physics, it’s quite common these days to start developing new machine learning applications. But many machine learning applications run more efficiently on GPU.
The aim of this lesson is to:
- demonstrate how to move an existing machine learning model onto a GPU
- discuss some of the common issues that come up when using machine learning applications on GPUs
The skills we’ll focus on:
- Understanding a bit about GPUs
- Using Python & PyTorch to discover what kind of GPU is available to you
- Moving a machine learning model onto the GPU
- Comparing the performance of the machine learning model between the CPU and the GPU
HSF Software TrainingThis training module is part of the HSF Software Training Center, a series of training modules that serves HEP newcomers the software skills needed as they enter the field, and in parallel, instill best practices for writing software.
Videos are provided at the top of each page to help guide you. For the Introduction section, which has no coding, the video simply takes you through the text, so choose whichever way you learn best: video or reading. For the remaining sections, the videos take you through the coding live.