Tuesday, November 10, 2015

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems - implementation -

Unless you have been hiding in a cave that has no Wifi and 3G, you may have heard of Google releasing TensorFlow. Here is the whitepaper and the attendant video of Jeff Dean at the recent Baylearn meetup. Of note since the release this question on Reddit ( So, should I scrap theano, torch, caffe, and dive into TensorFlow? ), this blog post, and a tip on running it on Amazon EC3.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems by Martın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng

TensorFlow [1] 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 algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at http://www.tensorflow.org

Slides are here.
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rrtucci said...

What is Microsoft using instead of TensorFlow? Is it Infer.net, or something else? Whatever it is, do you think MS will make a response in kind by open sourcing some of their Bing AI code?

bpchesney said...

Hey Igor - do you know of any compressive sensing examples in tensorflow that have been made available yet? Do you think there would be interest in that?

Igor said...

No I don't know of any.