ALCF is hosting a series of hands-on courses that will expand upon advanced topics in AI for science, building on their previous Intro to AI-Driven Science on Supercomputers Training Series. Attendees will deepen their understanding of applying AI at scale, learn about coupling science simulations with AI, dig into inference workflows, and explore how AI accelerators are enabling AI for science. This advanced topics for AI training series is open to NERSC users.
Prerequisites
This training series is aimed at undergraduate and graduate students enrolled at U.S. universities. Attendees are expected to
- Have foundational knowledge of Python, and
- Have either attended the ALCF Intro to AI-Driven Science on Supercomputers series or have familiarity with the topics covered in the series, including foundational concepts in parallel computing, neural networks, large-language models, prompt engineering, and AI accelerators. (Those without this background knowledge are welcome to look through the Intro to AI videos and materials before the start of the series.)
Workshop series format
There will be five sessions in total, from October 14 to November 11. Each session takes place on Tuesdays from 1 to 2:30 p.m. Pacific Time. Each session will have both a lecture and hands-on components, along with a talk from an Argonne scientist about the work they do using AI for their science.
Session recordings will be made available shortly after each session.
Attendees who complete all in-class and post-class exercises by the end of the series will receive a certificate of completion and a digital badge.
Presentation Materials
Session materials are hosted on the ALCF AI Science Training series GitHub.