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DTSTAMP:20211207T055407Z
LOCATION:Online
DTSTART;TZID=America/Chicago:20211115T103000
DTEND;TZID=America/Chicago:20211115T110000
UID:submissions.supercomputing.org_SC21_sess423_ws_mlhpce107@linklings.com
SUMMARY:HPCFAIR: Enabling FAIR AI for HPC Applications
DESCRIPTION:Workshop\n\nHPCFAIR: Enabling FAIR AI for HPC Applications\n\n
 Verma, Emani, Liao, Lin, Vanderbruggen...\n\nArtificial Intelligence (AI) 
 is being adopted in different domains at an unprecedented scale. A signifi
 cant interest in the scientific community also involves leveraging machine
  learning (ML) to run high-performance computing applications at scale eff
 ectively. Given multiple efforts in this arena, there are often duplicated
  efforts when existing rich data sets and ML models could be leveraged ins
 tead. The primary challenge is a lack of an ecosystem to reuse and reprodu
 ce the models and datasets. In this work, we propose HPCFAIR, a modular, e
 xtensible framework to enable AI models to be Findable, Accessible, Intero
 perable, and Reproducible (FAIR). It enables users with a structured appro
 ach to search, load, save and reuse the models in their codes. We present 
 the design, implementation of our framework and highlight how it can be se
 amlessly integrated into ML-driven applications for high-performance compu
 ting applications and scientific machine learning workloads.\n\nTag: Onlin
 e Only, Machine Learning and Artificial Intelligence\n\nRegistration Categ
 ory: Workshop Reg Pass
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