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DTSTART;TZID=America/Denver:20231113T083000
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UID:submissions.supercomputing.org_SC23_sess239_tut143@linklings.com
SUMMARY:Deep Learning at Scale
DESCRIPTION:Peter Harrington, Steven Farrell, and Shashank Subramanian (La
 wrence Berkeley National Laboratory (LBNL)); Thorsten Kurth and Josh Romer
 o (NVIDIA Corporation); Wahid Bhimji (Lawrence Berkeley National Laborator
 y (LBNL)); and Andrew Naylor (Lawrence Berkeley National Laboratory)\n\nDe
 ep 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 larg
 e number of domains, from large language models (LLMs) to protein folding.
  As these models grow in complexity to solve increasingly challenging prob
 lems with larger and larger datasets, the need for scalable methods and so
 ftware to train them grows accordingly.\n\nThe Deep Learning at Scale tuto
 rial aims to provide attendees with a working knowledge of deep learning o
 n HPC-class systems, including core concepts, scientific applications, per
 formance optimization, tips, and techniques for scaling. We will provide t
 raining accounts on some of the worlds largest GPU systems, example code, 
 and datasets to allow attendees to experiment hands-on with optimized, sca
 lable distributed training of deep neural network machine learning models 
 from real scientific computing applications.\n\nTag: Artificial Intelligen
 ce/Machine Learning, Performance Optimization\n\nRegistration Category: Tu
 torial Reg Pass\n\n
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