TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abstract
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 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
www.tensorflow.org.
Research Areas
