Workshops Archive

A Model for Teaching Machine Learning, Deep Learning, and Research Computing to Domain Scientists on HPC Resources

Workshop: EduHPC-25: Workshop on Education for High Performance Computing

Authors: William J. Allen, Kelsey M. Beavers, Erik Ferlanti, Lorenzo Concia, Joshua Urrutia, Ernesto A. B. F. Lima, John M. Fonner, Felix Zuo, H. E. Duplechin Seymour, Ari B. Kahn, Joe Stubbs, Anagha Jamthe, Stephanie N. Baker, Tabish Khan, and James P. Carson (Texas Advanced Computing Center)

Abstract: This paper presents outcomes and insights from a one-week workshop designed to teach biologists essential skills in high-performance computing (HPC), machine learning (ML), and deep learning (DL). Participants with little or no prior experience with HPC learned how to navigate file systems via a command-line interface, launch jobs with SLURM, and apply ML and DL techniques to real-world biological datasets. Hands-on activities were delivered with accessible technologies such as Jupyter Notebooks, graphical desktop interfaces (DCV), and software containers, all deployed on HPC systems with minimal user setup required. We propose this workshop model as an adaptable framework for training domain scientists how to effectively use HPC resources to advance scientific discovery, and we present survey data demonstrating its effectiveness in improving participant skills.

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