TensorFlow.js: Machine Learning For The Web and Beyond

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

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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


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.