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.
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.
For this use case, you can load the latest version of the library directly from Google CDN:
To use a different version, see the release page on GitHub.
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
$ ./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.
$ ./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
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
deeplearn.js targets WebGL 1.0 devices with the
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!
This is not an official Google product.
We would like to acknowledge Chi Zeng, David Farhi, Mahima Pushkarna, Lauren Hannah-Murphy, Minsuk (Brian) Kahng, James Wexler, Martin Wattenberg, Fernanda Viégas, Greg Corrado, Jeff Dean for their tremendous help, and the Google Brain team for providing support for the project.