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Machine Learning with Python: Distributed Training and Data Resources on Blue Waters

Aaron Saxton from NCSA

In this video from the 2018 Blue Waters Symposium, Aaron Saxton from NCSA presents a tutorial entitled “Machine Learning with Python: Distributed Training and Data Resources on Blue Waters.”

“Blue Waters currently supports TensorFlow 1.3, PyTorch 0.3.0 and we hope to support CNTK and Horovod in the near future. This tutorial will go over the minimum ingredients needed to do distributed training on Blue Waters with these packages. What’s more, we also maintain an ImageNet data set to help researchers get started training CNN models. I will review the process by which a user can get access to this data set.”

Aaron Saxton is a Data Scientist who works in the Blue Waters project office at the National Center for Super Computing Applications (NCSA). His current interest is in machine learning, data, and migrating popular data/ML techniques to HPC environments. Aaron’s career has shifted back and forth between industry and academic ventures. Most recently he was a data scientist and founding member of the agricultural data company Agrible Inc. Before that, Aaron worked at Neustar Inc, University of Kentucky, and SAIC. In the summer of 2014, shortly after joining Neustar, Aaron graduated from University of Kentucky to earn his PhD in Mathematics by studying Partial Differential Equations, Operator Theory, and Schrödinger’s equation.

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