TensorFlow.js: Machine Learning For The Web and Beyond

Part of Proceedings of Machine Learning and Systems 1 (MLSys 2019)

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Authors

Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Charles Nicholson, Nick Kreeger, Ping Yu, Shanqing Cai, Eric Nielsen, David Soegel, Stan Bileschi, Michael Terry, Ann Yuan, Kangyi Zhang, Sandeep Gupta, Sarah Sirajuddin, D Sculley, Rajat Monga, Greg Corrado, Fernanda Viegas, Martin M Wattenberg

Abstract

TensorFlow.js is a library for building and executing machine learning algorithms in JavaScript. TensorFlow.js models run in a web browser and in the Node.js environment. The library is part of the TensorFlow ecosystem, providing a set of APIs that are compatible with those in Python, allowing models to be ported between the Python and JavaScript ecosystems. TensorFlow.js has empowered a new set of developers from the extensive JavaScript community to build and deploy machine learning models and enabled new classes of on-device computation. This paper describes the design, API, and implementation of TensorFlow.js, and highlights some of the impactful use cases.