URL: https://github.com/cyberaide/poster-summit-mlcommons-impact-education
Authors: Gregor von Laszewski, Geoffrey C. Fox
Today's Educational efforts often only showcase limited and small artificial intelligence and deep learning DL applications, limiting the experiences to examples that do not take into account the cyberinfrastructure opportunities provided by leadership class comput infrastructures. Furthermore, customizable lessons are needed in that regard to be integratable in a variety of educational opportunities.
Our solution is to leverage MLCommon's rich set of application benchmarks to gain insight into various aspects of deep learning. We focus on a holistic view of the interplay between applications, cyberinfrastructure, and benchmark requirements that are typically not covered in elementary educational activities. The outcome will be a well-educated research and professional workforce. Rapid changes in these fields require quick adaptation of material and customization based on the various backgrounds of students which we deliver through our open source customizable educational component framework. Furthermore, we developed open source tools called cloudmesh compute coordinator and experiment executor to coordinate AI applications on hybrid machines integrating University, NSF, and DOE cyberinfrastructure.
References
- Opportunities for Enhancing MLCommons Efforts while leveraging Insights in High-Performance Big Data Systems Gained from Educational MLCommons Earthquake Benchmarks Efforts, G. von Laszewski, J.P. Fleischer, Robert Knuuti, Geoffrey. C. Fox, et.al. Frontiers, 2023, Sep. accepted
- Artificial Intelligence for Science A Deep Learning Revolution, 2023 A. Choudhary, G. C Fox, T. Hey doi: 10.1142/13123
- Cloudmesh Experiment Executor, https://github.com/cloudmesh/cloudmesh-ee
- Cloudmesh Compute Coordinator, https://github.com/cloudmesh/cloudmesh-cc
Poster: