DescriptionDeep 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.
The Deep Learning at Scale tutorial aims to provide attendees with a working knowledge of deep learning on HPC-class systems, including core concepts, scientific applications, performance optimization, tips, and techniques for scaling. We will provide training accounts on some of the world's largest GPU systems, example code, and datasets to allow attendees to experiment hands-on with optimized, scalable distributed training of deep neural network machine learning models.