The open source GPU-accelerated library supports TypeScript and JavaScript, allowing you to train neural networks or run pre-trained models

Google’s Deeplearn.js brings machine learning to the browser

Google is offering an open source, hardware-accelerated library for machine learning that runs in a browser. The library is currently supported only in the desktop version of Google Chrome, but the project is working to support more devices.

The Deeplearn.js library enables training of neural networks within a browser, requiring no software installation or back end. “A client-side ML library can be a platform for interactive explanations, for rapid prototyping and visualization, and even for offline computation,” Google researchers said. “And if nothing else, the browser is one of the world’s most popular programming platforms.”

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Using the WebGL JavaScript API for 2D and 3D graphics, Deeplearn.js can conduct computations on the GPU. This offers significant performance, thus getting past the speed limits of JavaScript, the researchers said.

Deeplearn.js imitates the structure of the company’s TensorFlow machine intelligence library and NumPy, a scientific computing package based on Python. “We have also implemented versions of some of the most commonly used TensorFlow operations. With the release of Deeplearn.js, we will be providing tools to export weights from TensorFlow checkpoints, which will allow authors to import them into webpages for Deeplearn.js inference.”

Although Microsoft’s TypeScript is the language of choice, Deeplearn.js can be used with plain JavaScript. Demos of Deeplearn.js are featured on the project’s homepage . Deeplearn.js joins other projects that bring machine learning to JavaScript and the browser, including TensorFire , which allows execution of neural networks within a webpage, and ML.js , which provides machine learning and numerical analysis tools in JavaScript for Node.js.


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