Taming Resource Heterogeneity In Distributed ML Training With Dynamic Batching

20 May 2023

Papers citing "Taming Resource Heterogeneity In Distributed ML Training With Dynamic Batching"

10 / 10 papers shown

Title
OmniLearn: A Framework for Distributed Deep Learning over Heterogeneous Clusters S. Tyagi Prateek Sharma 63 0 0 21 Mar 2025
Sustainable self-supervised learning for speech representations Luis Lugo Valentin Vielzeuf 31 2 0 11 Jun 2024
AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodes Youshao Xiao Lin Ju Zhenglei Zhou Siyuan Li Zhaoxin Huan ... Rujie Jiang Lin Wang Xiaolu Zhang Lei Liang Jun Zhou 34 1 0 15 Apr 2024
Efficiency-oriented approaches for self-supervised speech representation learning Luis Lugo Valentin Vielzeuf SSL 29 1 0 18 Dec 2023
MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation Yunming Liao Yang Xu Hong-Ze Xu Lun Wang Zhiwei Yao C. Qiao FedML MoMe 38 10 0 22 Nov 2023
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset Weijie Liu Xiaoxi Zhang Jingpu Duan Carlee Joe-Wong Zhi Zhou Xu Chen 23 7 0 20 Oct 2023
ABS-SGD: A Delayed Synchronous Stochastic Gradient Descent Algorithm with Adaptive Batch Size for Heterogeneous GPU Clusters Xin Zhou Ling Chen Houming Wu 19 0 0 29 Aug 2023
Accelerating Distributed ML Training via Selective Synchronization S. Tyagi Martin Swany FedML 32 3 0 16 Jul 2023
ScaDLES: Scalable Deep Learning over Streaming data at the Edge S. Tyagi Martin Swany 14 6 0 21 Jan 2023
SpotLake: Diverse Spot Instance Dataset Archive Service Sungjae Lee Jaeil Hwang Kyungyong Lee 4 12 0 07 Feb 2022