69 research outputs found
Distributed Training and Optimization Of Neural Networks
Deep learning models are yielding increasingly better performances thanks to
multiple factors. To be successful, model may have large number of parameters
or complex architectures and be trained on large dataset. This leads to large
requirements on computing resource and turn around time, even more so when
hyper-parameter optimization is done (e.g search over model architectures).
While this is a challenge that goes beyond particle physics, we review the
various ways to do the necessary computations in parallel, and put it in the
context of high energy physics.Comment: 20 pages, 4 figures, 2 tables, Submitted for review. To appear in
"Artificial Intelligence for Particle Physics", World Scientific Publishin
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