Scaling Up Optuna: P2P Distributed Hyperparameters Optimization

ABSTRACT In machine learning (ML), hyperparameter optimization (HPO) is the process of choosing a tuple of values that ensures an efficient deployment and training of an AI model. In practice, HPO not only applies to ML tuning but can also be used to tune complex numerical simulations. In this conte...

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Veröffentlicht in:	Concurrency and computation Jg. 37; H. 4-5
1. Verfasser:	Cudennec, Loïc
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 28.02.2025 Wiley Subscription Services, Inc Wiley
Schriftenreihe:	e70008
Schlagworte:	Algorithms Computer Science Computer simulation distributed computing Energy consumption high‐performance computing hyperparameters optimization Machine learning Numerical models operational research Optimization optuna Parameters peer‐to‐peer Scaling up Simulation Tuning Energy consumption Operational Research Peer-to-peer High-Performance Computing Optuna Distributed Computing Machine Learning Hyperparameters Optimization
ISSN:	1532-0626, 1532-0634
Online-Zugang:	Volltext
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