TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
@article{Abadi2016TensorFlowLM, title={TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems}, author={M. Abadi and A. Agarwal and P. Barham and E. Brevdo and Z. Chen and Craig Citro and G. S. Corrado and Andy Davis and J. Dean and M. Devin and Sanjay Ghemawat and Ian J. Goodfellow and A. Harp and Geoffrey Irving and M. Isard and Y. Jia and R. J{\'o}zefowicz and L. Kaiser and M. Kudlur and Josh Levenberg and Dan Man{\'e} and Rajat Monga and Sherry Moore and D. Murray and Chris Olah and Mike Schuster and Jonathon Shlens and B. Steiner and Ilya Sutskever and Kunal Talwar and P. Tucker and V. Vanhoucke and V. Vasudevan and F. Vi{\'e}gas and Oriol Vinyals and Pete Warden and M. Wattenberg and Martin Wicke and Y. Yu and X. Zheng}, journal={ArXiv}, year={2016}, volume={abs/1603.04467} }
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of… CONTINUE READING
Supplemental Content
Figures and Topics from this paper
7,981 Citations
Improving the Performance of Distributed TensorFlow with RDMA
- Computer Science
- International Journal of Parallel Programming
- 2017
- 18
TensorX: Extensible API for Neural Network Model Design and Deployment
- Computer Science
- ArXiv
- 2020
- Highly Influenced
- PDF
SingleCaffe: An Efficient Framework for Deep Learning on a Single Node
- Computer Science
- IEEE Access
- 2018
- 2
- Highly Influenced
Increasing Portable Machine Learning Performance by Application of Rewrite Rules on Google Tensorflow Data Flow Graphs
- 2016
- Highly Influenced
- PDF
Scalability Study of Deep Learning Algorithms in High Performance Computer Infrastructures
- Computer Science
- 2017
- Highly Influenced
- PDF
In-Database Machine Learning: Gradient Descent and Tensor Algebra for Main Memory Database Systems
- Computer Science
- BTW
- 2019
- 3
- PDF
References
SHOWING 1-10 OF 67 REFERENCES
Project Adam: Building an Efficient and Scalable Deep Learning Training System
- Computer Science
- OSDI
- 2014
- 552
- PDF
Caffe: Convolutional Architecture for Fast Feature Embedding
- Computer Science
- ACM Multimedia
- 2014
- 12,491
- PDF
An introduction to computational networks and the computational network toolkit (invited talk)
- Computer Science
- INTERSPEECH
- 2014
- 361
- PDF
Building high-level features using large scale unsupervised learning
- Computer Science, Mathematics
- 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
- 2013
- 1,935
- PDF
Multilingual acoustic models using distributed deep neural networks
- Computer Science
- 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
- 2013
- 264
- PDF
On rectified linear units for speech processing
- Computer Science
- 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
- 2013
- 422
- PDF
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Computer Science
- ICML
- 2015
- 20,832
- PDF