Transcription of TensorFlow: A System for Large-Scale Machine Learning
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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.
mands of Google’s production machine learning work-loads. TensorFlow provides a simple dataflow-based pro-gramming abstraction that allows users to deploy appli-cations on distributed clusters, local workstations, mo-bile devices, and custom-designed accelerators. A high-level scripting interface (Figure 1) wraps the construction
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