Spotlight: Optimizing Device Placement for Training Deep Neural Networks

Yuanxiang Gao, Li Chen, Baochun Li
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:1676-1684, 2018.

Abstract

Training deep neural networks (DNNs) requires an increasing amount of computation resources, and it becomes typical to use a mixture of GPU and CPU devices. Due to the heterogeneity of these devices, a recent challenge is how each operation in a neural network can be optimally placed on these devices, so that the training process can take the shortest amount of time possible. The current state-of-the-art solution uses reinforcement learning based on the policy gradient method, and it suffers from suboptimal training times. In this paper, we propose Spotlight, a new reinforcement learning algorithm based on proximal policy optimization, designed specifically for finding an optimal device placement for training DNNs. The design of our new algorithm relies upon a new model of the device placement problem: by modeling it as a Markov decision process with multiple stages, we are able to prove that Spotlight achieves a theoretical guarantee on performance improvements. We have implemented Spotlight in the CIFAR-10 benchmark and deployed it on the Google Cloud platform. Extensive experiments have demonstrated that the training time with placements recommended by Spotlight is 60.9% of that recommended by the policy gradient method.

Cite this Paper


BibTeX
@InProceedings{pmlr-v80-gao18a, title = {Spotlight: Optimizing Device Placement for Training Deep Neural Networks}, author = {Gao, Yuanxiang and Chen, Li and Li, Baochun}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {1676--1684}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/gao18a/gao18a.pdf}, url = {http://proceedings.mlr.press/v80/gao18a.html}, abstract = {Training deep neural networks (DNNs) requires an increasing amount of computation resources, and it becomes typical to use a mixture of GPU and CPU devices. Due to the heterogeneity of these devices, a recent challenge is how each operation in a neural network can be optimally placed on these devices, so that the training process can take the shortest amount of time possible. The current state-of-the-art solution uses reinforcement learning based on the policy gradient method, and it suffers from suboptimal training times. In this paper, we propose Spotlight, a new reinforcement learning algorithm based on proximal policy optimization, designed specifically for finding an optimal device placement for training DNNs. The design of our new algorithm relies upon a new model of the device placement problem: by modeling it as a Markov decision process with multiple stages, we are able to prove that Spotlight achieves a theoretical guarantee on performance improvements. We have implemented Spotlight in the CIFAR-10 benchmark and deployed it on the Google Cloud platform. Extensive experiments have demonstrated that the training time with placements recommended by Spotlight is 60.9% of that recommended by the policy gradient method.} }
Endnote
%0 Conference Paper %T Spotlight: Optimizing Device Placement for Training Deep Neural Networks %A Yuanxiang Gao %A Li Chen %A Baochun Li %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-gao18a %I PMLR %P 1676--1684 %U http://proceedings.mlr.press/v80/gao18a.html %V 80 %X Training deep neural networks (DNNs) requires an increasing amount of computation resources, and it becomes typical to use a mixture of GPU and CPU devices. Due to the heterogeneity of these devices, a recent challenge is how each operation in a neural network can be optimally placed on these devices, so that the training process can take the shortest amount of time possible. The current state-of-the-art solution uses reinforcement learning based on the policy gradient method, and it suffers from suboptimal training times. In this paper, we propose Spotlight, a new reinforcement learning algorithm based on proximal policy optimization, designed specifically for finding an optimal device placement for training DNNs. The design of our new algorithm relies upon a new model of the device placement problem: by modeling it as a Markov decision process with multiple stages, we are able to prove that Spotlight achieves a theoretical guarantee on performance improvements. We have implemented Spotlight in the CIFAR-10 benchmark and deployed it on the Google Cloud platform. Extensive experiments have demonstrated that the training time with placements recommended by Spotlight is 60.9% of that recommended by the policy gradient method.
APA
Gao, Y., Chen, L. & Li, B.. (2018). Spotlight: Optimizing Device Placement for Training Deep Neural Networks. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:1676-1684 Available from http://proceedings.mlr.press/v80/gao18a.html.

Related Material