most citedHeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

6 citations · 6 across the 2 of their papers we have counts for

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cs.DC2021

End-to-end Adaptive Distributed Training on PaddlePaddle

Yulong Ao, Zhihua Wu, Dianhai Yu +10

Distributed training has become a pervasive and effective approach for training a large neural network (NN) model with processing massive data. However, it is very challenging to s…

cs.DC2021★ 6 cited

HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

Ji Liu, Zhihua Wu, Dianhai Yu +6

Deep neural networks (DNNs) exploit many layers and a large number of parameters to achieve excellent performance. The training process of DNN models generally handles large-scale…