deeplearn.js

deeplearn.js is an open-source library that brings performant machine learning building blocks to the web, allowing you to train neural networks in a browser or run pre-trained models in inference mode.

We provide two APIs, an immediate execution model (think NumPy) and a deferred execution model mirroring the TensorFlow API.

deeplearn.js was originally developed by the Google Brain PAIR team to build powerful interactive machine learning tools for the browser. You can use the library for everything from education, to model understanding, to art projects.

Examples

Getting started

deeplearn.js is an open source hardware-accelerated JavaScript library for machine intelligence. deeplearn.js brings performant machine learning building blocks to the web, allowing you to train neural networks in a browser or run pre-trained models in inference mode.

deeplearn.js has two APIs, an immediate execution model (think NumPy) and a deferred execution model mirroring the TensorFlow API. deeplearn.js was originally developed by the Google Brain PAIR team to build powerful interactive machine learning tools for the browser, but it can be used for everything from education, to model understanding, to art projects.

Usage

From JavaScript

Typescript is the preferred language of choice for deeplearn.js, however you can use it with plain JavaScript.

For this use case, you can load the latest version of the library directly from Google CDN:

<script src="https://storage.googleapis.com/learnjs-data/deeplearn.js"></script>

To use a different version, see the release page on GitHub.

From TypeScript

To build deeplearn.js from source, we need to clone the project and prepare the dev environment:

$ git clone https://github.com/PAIR-code/deeplearnjs.git
$ cd deeplearnjs
$ npm run prep # Installs node modules and bower components.

To build a standalone library that can be used directly in the browser using a <script> tag:

$ ./scripts/build-standalone.sh # Builds standalone library.
>> Stored standalone library at dist/deeplearn.js

To build a node package/es6 module:

$ ./scripts/build-npm.sh # Builds npm package.
>> Stored npm package at dist/deeplearn-VERSION.tgz

To interactively develop any of the demos (e.g. demos/nn-art/):

$ ./scripts/watch-demo demos/nn-art/nn-art.ts
>> Starting up http-server, serving ./
>> Available on:
>>   http://127.0.0.1:8080
>> Hit CTRL-C to stop the server
>> 1357589 bytes written to dist/demos/nn-art/bundle.js (0.85 seconds) at 10:34:45 AM

Then visit http://localhost:8080/demos/nn-art/nn-art-demo.html. The watch-demo script monitors for changes of typescript code and does incremental compilation (~200-400ms), so users can have a fast edit-refresh cycle when developing apps using deeplearn.js.

To run all the tests:

$ npm run test

Before you submit a pull request, make sure the code is clean of lint errors:

$ npm run lint

Supported environments

deeplearn.js targets WebGL 1.0 devices with the OES_texture_float extension and also targets WebGL 2.0 devices. For platforms without WebGL, we provide CPU fallbacks.

However, currently our demos do not support Mobile, Firefox, and Safari. Please view them on desktop Chrome for now. We are working to support more devices. Check back soon!

Resources

This is not an official Google product.

Acknowledgements

deeplearn.js was originally developed by Nikhil Thorat, Daniel Smilkov and Charles Nicholson.

We would like to acknowledge Chi Zeng, David Farhi, Mahima Pushkarna, Lauren Hannah-Murphy, Minsuk (Brian) Kahng, James Wexler, Martin Wattenberg, Fernanda ViƩgas, Greg Corrado, Jeff Dean for their tremendous help, and the Google Brain team for providing support for the project.