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      1 # TensorFlow White Papers
      2 
      3 This document identifies white papers about TensorFlow.
      4 
      5 ## Large-Scale Machine Learning on Heterogeneous Distributed Systems
      6 
      7 [Access this white paper.](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf)
      8 
      9 **Abstract:** TensorFlow is an interface for expressing machine learning
     10 algorithms, and an implementation for executing such algorithms.
     11 A computation expressed using TensorFlow can be
     12 executed with little or no change on a wide variety of heterogeneous
     13 systems, ranging from mobile devices such as phones
     14 and tablets up to large-scale distributed systems of hundreds
     15 of machines and thousands of computational devices such as
     16 GPU cards. The system is flexible and can be used to express
     17 a wide variety of algorithms, including training and inference
     18 algorithms for deep neural network models, and it has been
     19 used for conducting research and for deploying machine learning
     20 systems into production across more than a dozen areas of
     21 computer science and other fields, including speech recognition,
     22 computer vision, robotics, information retrieval, natural
     23 language processing, geographic information extraction, and
     24 computational drug discovery. This paper describes the TensorFlow
     25 interface and an implementation of that interface that
     26 we have built at Google. The TensorFlow API and a reference
     27 implementation were released as an open-source package under
     28 the Apache 2.0 license in November, 2015 and are available at
     29 www.tensorflow.org.
     30 
     31 
     32 ### In BibTeX format
     33 
     34 If you use TensorFlow in your research and would like to cite the TensorFlow
     35 system, we suggest you cite this whitepaper.
     36 
     37 <pre>
     38 @misc{tensorflow2015-whitepaper,
     39 title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
     40 url={https://www.tensorflow.org/},
     41 note={Software available from tensorflow.org},
     42 author={
     43     Mart\'{\i}n~Abadi and
     44     Ashish~Agarwal and
     45     Paul~Barham and
     46     Eugene~Brevdo and
     47     Zhifeng~Chen and
     48     Craig~Citro and
     49     Greg~S.~Corrado and
     50     Andy~Davis and
     51     Jeffrey~Dean and
     52     Matthieu~Devin and
     53     Sanjay~Ghemawat and
     54     Ian~Goodfellow and
     55     Andrew~Harp and
     56     Geoffrey~Irving and
     57     Michael~Isard and
     58     Yangqing Jia and
     59     Rafal~Jozefowicz and
     60     Lukasz~Kaiser and
     61     Manjunath~Kudlur and
     62     Josh~Levenberg and
     63     Dandelion~Man\'{e} and
     64     Rajat~Monga and
     65     Sherry~Moore and
     66     Derek~Murray and
     67     Chris~Olah and
     68     Mike~Schuster and
     69     Jonathon~Shlens and
     70     Benoit~Steiner and
     71     Ilya~Sutskever and
     72     Kunal~Talwar and
     73     Paul~Tucker and
     74     Vincent~Vanhoucke and
     75     Vijay~Vasudevan and
     76     Fernanda~Vi\'{e}gas and
     77     Oriol~Vinyals and
     78     Pete~Warden and
     79     Martin~Wattenberg and
     80     Martin~Wicke and
     81     Yuan~Yu and
     82     Xiaoqiang~Zheng},
     83   year={2015},
     84 }
     85 </pre>
     86 
     87 Or in textual form:
     88 
     89 <pre>
     90 Martn Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo,
     91 Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis,
     92 Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow,
     93 Andrew Harp, Geoffrey Irving, Michael Isard, Rafal Jozefowicz, Yangqing Jia,
     94 Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Man, Mike Schuster,
     95 Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Jonathon Shlens,
     96 Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker,
     97 Vincent Vanhoucke, Vijay Vasudevan, Fernanda Vigas,
     98 Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke,
     99 Yuan Yu, and Xiaoqiang Zheng.
    100 TensorFlow: Large-scale machine learning on heterogeneous systems,
    101 2015. Software available from tensorflow.org.
    102 </pre>
    103 
    104 
    105 
    106 ## TensorFlow: A System for Large-Scale Machine Learning
    107 
    108 [Access this white paper.](https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf)
    109 
    110 **Abstract:** TensorFlow is a machine learning system that operates at
    111 large scale and in heterogeneous environments. TensorFlow
    112 uses dataflow graphs to represent computation,
    113 shared state, and the operations that mutate that state. It
    114 maps the nodes of a dataflow graph across many machines
    115 in a cluster, and within a machine across multiple computational
    116 devices, including multicore CPUs, generalpurpose
    117 GPUs, and custom-designed ASICs known as
    118 Tensor Processing Units (TPUs). This architecture gives
    119 flexibility to the application developer: whereas in previous
    120 parameter server designs the management of shared
    121 state is built into the system, TensorFlow enables developers
    122 to experiment with novel optimizations and training algorithms.
    123 TensorFlow supports a variety of applications,
    124 with a focus on training and inference on deep neural networks.
    125 Several Google services use TensorFlow in production,
    126 we have released it as an open-source project, and
    127 it has become widely used for machine learning research.
    128 In this paper, we describe the TensorFlow dataflow model
    129 and demonstrate the compelling performance that TensorFlow
    130 achieves for several real-world applications.
    131 
    132