ALCF AI Training Series: Intro to AI-driven Science on Supercomputers, Feb-Mar 2024
Argonne Leadership Computing Facility (ALCF) is hosting a training series building on its training program in the areas of AI and supercomputing. The series includes hands-on courses that will teach attendees to use leading-edge supercomputers to develop and apply AI solutions to the world’s most challenging problems. This year, the training will focus on understanding the fundamentals of large-language models (LLMs) and their scientific applications. This training series is open to NERSC users.
This training series is aimed at undergraduate and graduate students enrolled at US universities and community colleges. Attendees are expected to have basic experience with Python. No supercomputing or AI knowledge is required.
Workshop Series Format
Each session will have both lecture and hands-on components, along with a talk from an Argonne scientist about the work they do using AI for their science. Each session occurs on Tuesdays from 1-2:30 p.m. Pacific Time. Session recordings will be made available shortly after each event. 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. Session materials are hosted on the ALCF AI Science Training Series GitHub.
Registration, Agenda, and Materials
The registration deadline is January 15, 2024. The series is free to attend, but registration is required. Please register if you are able to commit to attending all 8 sessions. Attendees who want to participate in hands-on sections must submit an ALCF account request before January 15, 2024 to access ALCF machines. Participants without accounts can still attend but will not be able to participate in hands-on exercises.
For more information, topics, and presentations of each session, and to register please visit the ALCF event page and each individual event page below.
- 02/06/2024: Intro to Artificial Intelligence on Supercomputers
- 02/13/2024: Introduction to Neural Networks
- 02/20/2024: Advanced Topics in Neural Networks
- 02/27/2024: Introduction to Large Language Models (LLM)
- 03/05/2024: LLM: Embeddings and Tokenization
- 03/12/2024: Parallel Training Methods for AI
- 03/19/2024: AI Accelerators
- 03/26/2024: Evaluating LLMs and Potential Pitfalls