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

A week-long workshop successfully trained biologists in HPC, ML, and DL, enhancing their skills and understanding of advanced computational techniques for scientific discovery. The curriculum's application-driven approach, leveraging accessible HPC technologies like Jupyter Notebooks and containers, proved highly effective.

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.