Deep learning is rapidly and fundamentally transforming the way science and industry use data to solve problems. Deep neural network models have been shown to be powerful tools for extracting insights from data across a large number of domains. As these models grow in complexity to solve increasingly challenging problems with larger and larger datasets, the need for scalable methods and software to train them grows accordingly.
This tutorial aims to provide attendees with a working knowledge on deep learning on distributed systems such as HPC machines, including core concepts, scientific applications, and techniques for scaling. We will provide code base and datasets to train a deep neural network model on a single node and on multiple nodes. The talks will explain the details of the model architecture and optimization with live demonstrations of model training.
Steve Farrell is a Machine Learning Engineer at the NERSC supercomputing center at Lawrence Berkeley National Laboratory. In this role he supports the ML needs of 7000 users across a wide range of scientific domains, and the ML software stack. He collaborates with science teams to perform applied ML research.
