TensorFlow: A System for Large-Scale Machine Learning

This paper is included in the Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16).November 2 4, 2016 Savannah, GA, USAISB N 978 -1- 931971-33 -1 Open access to the Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation is sponsored by : A System for Large-Scale Machine LearningMart n Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng, Google : A System for Large-Scale Machine learningMart n Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean,Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur,Josh Levenberg, Rajat Monga, Sherry Moore, Derek G.

with a focus on training and inference on deep neural net-works. Several Google services use TensorFlow in pro- ... commonly held belief that asynchronous replication is re-quired for scalable learning [14, 20, 49]. ... and reinforcement learning models, where the loss function is computed by some agent in a separate system, such as a video ...

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  System, Large, Scale, Machine, Learning, Deep, Reinforcement, Asynchronous, Tensorflow, System for large scale machine learning

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