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Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads
[article]
2020
arXiv
pre-print
The last decade has witnessed growth in the computational requirements for training deep neural networks. Current approaches (e.g., data/model parallelism, pipeline parallelism) parallelize training tasks onto multiple devices. However, these approaches always rely on specific deep learning frameworks and requires elaborate manual design, which make it difficult to maintain and share between different type of models. In this paper, we propose Auto-MAP, a framework for exploring distributed
arXiv:2007.04069v1
fatcat:asjj6wtwgnb4dcp5sis2oaogn4