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AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodes
April 16, 2024, 4:44 a.m. | Youshao Xiao, Lin Ju, Zhenglei Zhou, Siyuan Li, Zhaoxin Huan, Dalong Zhang, Rujie Jiang, Lin Wang, Xiaolu Zhang, Lei Liang, Jun Zhou
cs.LG updates on arXiv.org arxiv.org
Abstract: Many distributed training techniques like Parameter Server and AllReduce have been proposed to take advantage of the increasingly large data and rich features. However, stragglers frequently occur in distributed training due to resource contention and hardware heterogeneity, which significantly hampers the training efficiency. Previous works only address part of the stragglers and could not adaptively solve various stragglers in practice. Additionally, it is challenging to use a systematic framework to address all stragglers because different …
abstract arxiv cs.dc cs.lg data distributed efficiency features framework hardware however leader nodes server training type
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