FastTuning: Enabling Fast and Efficient Hyper-Parameter Tuning With Partitioning and Parallelism of Search Space

Hyper-parameter tuning (HPT) for deep learning (DL) models is prohibitively expensive. Sequential model-based optimization (SMBO) emerges as the state-of-the-art (SOTA) approach to automatically optimize HPT performance due to its heuristic advantages. Unfortunately, focusing on algorithm optimizati...

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Vydané v:	IEEE transactions on parallel and distributed systems Ročník 35; číslo 7; s. 1174 - 1188
Hlavní autori:	Li, Xiaqing, Guo, Qi, Zhang, Guangyan, Ye, Siwei, He, Guanhua, Yao, Yiheng, Zhang, Rui, Hao, Yifan, Du, Zidong, Zheng, Weimin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.07.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Computational modeling Deep learning distributed hyper-parameter tuning (HPT) system Dynamic scheduling Heuristic algorithms Optimization parallel computing Parallel processing Parameters Resource allocation Task analysis Tuning
ISSN:	1045-9219, 1558-2183
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