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Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads
The last decade has witnessed growth in the computational requirements for training deep neural networks.
DaSGD: Squeezing SGD Parallelization Performance in Distributed Training Using Delayed Averaging
The state-of-the-art deep learning algorithms rely on distributed training systems to tackle the increasing sizes of models and training data sets.
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