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Device Placement Optimization with Reinforcement Learning

2017-06-13 · ICML 2017 8 · Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean

The past few years have witnessed a growth in size and computational requirements for training and inference with neural networks. Currently, a common approach to address these requirements is to use a heterogeneous distributed environment with a mixture of hardware devices such as CPUs and GPUs. Importantly, the decision of placing parts of the neural models on devices is often made by human experts based on simple heuristics and intuitions. In this paper, we propose a method which learns to optimize device placement for TensorFlow computational graphs. Key to our method is the use of a sequence-to-sequence model to predict which subsets of operations in a TensorFlow graph should run on which of the available devices. The execution time of the predicted placements is then used as the reward signal to optimize the parameters of the sequence-to-sequence model. Our main result is that on Inception-V3 for ImageNet classification, and on RNN LSTM, for language modeling and neural machine translation, our model finds non-trivial device placements that outperform hand-crafted heuristics and traditional algorithmic methods.

📄 PDF Abstract BibTeX arXiv:1706.04972

Code (1)

indrajeet95/Device-Placement-Optimization-with-Reinforcement-Learning tf

Tasks

Language ModelingLanguage Modelling Machine Translation reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Translation

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2018-07-01 · ICML 2018 7 · Yuanxiang Gao, Li Chen, Baochun Li

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Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization

2018-12-01 · NeurIPS 2018 12 · Yuanxiang Gao, Li Chen, Baochun Li

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A Structure-Aware Framework for Learning Device Placements on Computation Graphs

2024-05-23 · Shukai Duan, Heng Ping, Nikos Kanakaris, Xiongye Xiao 외

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Placement Optimization with Deep Reinforcement Learning

2020-03-18 · Anna Goldie, Azalia Mirhoseini

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A Hierarchical Model for Device Placement

2018-01-01 · ICLR 2018 1 · Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner 외

We introduce a hierarchical model for efficient placement of computational graphs onto hardware devices, especially in heterogeneous environments with a mixture of CPUs, GPUs, and other computational devices. Our method …

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