If left unmanaged, failures and infrastructure inefficiencies can account for as much as 45% of your compute resources and precious engineering time (according to a Stanford University study). In this session, we discuss how to measure and maximize machine learning (ML) productivity for large-scale training jobs, spanning tens of thousands of accelerators. We’ll demonstrate a canonical view of large-scale training infrastructure and patterns our customers are applying that are available to you today.
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E
Speaker
Eric Steinberger
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talks
CEO
magic.dev
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