skip to main content
10.1145/3088525.3088527acmconferencesArticle/Chapter ViewAbstractPublication PagespldiConference Proceedingsconference-collections
research-article
Open Access

A computational model for TensorFlow: an introduction

Published:18 June 2017Publication History

ABSTRACT

TensorFlow is a powerful, programmable system for machine learning. This paper aims to provide the basics of a conceptual framework for understanding the behavior of TensorFlow models during training and inference: it describes an operational semantics, of the kind common in the literature on programming languages. More broadly, the paper suggests that a programming-language perspective is fruitful in designing and in explaining systems such as TensorFlow.

References

  1. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. J. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. J´ozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. G. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. A. Tucker, V. Vanhoucke, V. Vasudevan, F. B. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng. TensorFlow: Large-scale machine learning on heterogeneous distributed systems. CoRR, abs/1603.04467, 2016.Google ScholarGoogle Scholar
  2. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng. Tensorflow: A system for largescale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation, Proceedings, pages 265–283, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. M. Abadi and M. Isard. Timely dataflow: A model. In Formal Techniques for Distributed Objects, Components, and Systems - 35th IFIP WG 6.1 International Conference, FORTE 2015, Proceedings, pages 131–145, 2015.Google ScholarGoogle Scholar
  4. M. Abadi and M. Isard. Timely rollback: Specification and verification. In K. Havelund, G. Holzmann, and R. Joshi, editors, NASA Formal Methods – 7th International Symposium, Proceedings, pages 19–34. Springer, 2015.Google ScholarGoogle Scholar
  5. Arvind and D. E. Culler. Dataflow architectures. In J. F. Traub, B. J. Grosz, B. W. Lampson, and N. J. Nilsson, editors, Annual Review of Computer Science Vol. 1, 1986, pages 225–253. Annual Reviews Inc., Palo Alto, CA, USA, 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Arvind and R. S. Nikhil. Executing a program on the MIT taggedtoken dataflow architecture. IEEE Trans. Comput., 39(3):300–318, Mar. 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. J. Goodfellow, A. Bergeron, N. Bouchard, D. Warde-Farley, and Y. Bengio. Theano: new features and speed improvements. CoRR, abs/1211.5590, 2012.Google ScholarGoogle Scholar
  8. M. Giraud, D. G. Murray, and P. Tucker. control dependencies and assign new shape not working (using validate shape=false). Discussion at https://github.com/tensorflow/tensorflow/ issues/7782, 2017.Google ScholarGoogle Scholar
  9. T. T. Hildebrandt, P. Panangaden, and G. Winskel. A relational model of non-deterministic dataflow. Mathematical Structures in Computer Science, 14(5):613–649, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. B. Jonsson. A fully abstract trace model for dataflow and asynchronous networks. Distributed Computing, 7(4):197–212, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. G. Kahn. The semantics of a simple language for parallel programming. In J. L. Rosenfeld, editor, Information processing, pages 471– 475, Stockholm, Sweden, Aug 1974. North Holland, Amsterdam.Google ScholarGoogle Scholar
  12. M. Kudlur. Imperative programming in TensorFlow. Code repository at https://github.com/tensorflow/tensorflow/blob/ master/tensorflow/contrib/imperative, 2017.Google ScholarGoogle Scholar
  13. L. Lamport. Specifying Systems, The TLA+ Language and Tools for Hardware and Software Engineers. Addison-Wesley, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. M. Looks, M. Herreshoff, D. Hutchins, and P. Norvig. Deep learning with dynamic computation graphs. CoRR, abs/1702.02181, 2017.Google ScholarGoogle Scholar
  15. D. G. Murray, F. McSherry, M. Isard, R. Isaacs, P. Barham, and M. Abadi. Incremental, iterative data processing with timely dataflow. Communications of the ACM, 59(10):75–83, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. B. Recht, C. Ré, S. J. Wright, and F. Niu. HOGWILD!: A lock-free approach to parallelizing stochastic gradient descent. In 25th Annual Conference on Neural Information Processing Systems, Proceedings, pages 693–701, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. The XLA Team. XLA – TensorFlow compiled. Post in the Google Developers Blog, at https://developers.googleblog.com/2017/ 03/xla-tensorflow-compiled.html, 2017.Google ScholarGoogle Scholar

Index Terms

  1. A computational model for TensorFlow: an introduction

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          MAPL 2017: Proceedings of the 1st ACM SIGPLAN International Workshop on Machine Learning and Programming Languages
          June 2017
          50 pages
          ISBN:9781450350716
          DOI:10.1145/3088525

          Copyright © 2017 Owner/Author

          This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 18 June 2017

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Upcoming Conference

          PLDI '24

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader