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.
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, but it can be used for everything from education, to model understanding, to art projects.
npm install deeplearn
A simple example that sums an array with a scalar (broadcasted):
import {Array1D, NDArrayMathGPU, Scalar} from 'deeplearn';
const math = new NDArrayMathGPU();
const a = Array1D.new([1, 2, 3]);
const b = Scalar.new(2);
const result = math.add(a, b);
// Option 1: With async/await.
// Caveat: in non-Chrome browsers you need to put this in an async function.
console.log(await result.data()); // Float32Array([3, 4, 5])
// Option 2: With a Promise.
result.data().then(data => console.log(data));
// Option 3: Synchronous download of data.
// This is simpler, but blocks the UI until the GPU is done.
console.log(result.dataSync());You can also use deeplearn.js with plain JavaScript. Load the latest version of the library from unpkg:
<script src="https://unpkg.com/deeplearn"></script>To use a specific version, add @version to the unpkg URL above
(e.g. https://unpkg.com/[email protected]), which you can find in the
releases page on GitHub.
After importing the library, the API will be available as deeplearn in the
global namespace:
var dl = deeplearn;
var math = new dl.NDArrayMathGPU();
var a = dl.Array1D.new([1, 2, 3]);
var b = dl.Scalar.new(2);
var result = math.add(a, b);
// Option 1: With a Promise.
result.data().then(data => console.log(data)); // Float32Array([3, 4, 5])
// Option 2: Synchronous download of data. This is simpler, but blocks the UI.
console.log(result.dataSync());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.We recommend using Visual Studio Code for
development. Make sure to install the clang-format command line tool as
well as the Clang-Format VSCode extension for auto-formatting.
To interactively develop any of the demos (e.g. demos/nn-art/):
$ ./scripts/watch-demo demos/nn-art
>> 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 AMThen visit http://localhost:8080/demos/nn-art/. 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.
Before submitting a pull request, make sure the code passes all the tests and is clean of lint errors:
$ npm run test
$ npm run lintTo build a standalone ES5 library that can be imported in the browser with a
<script> tag:
$ ./scripts/build-standalone.sh # Builds standalone library.
>> Stored standalone library at dist/deeplearn(.min).jsTo do a dry run and test building an npm package:
$ ./scripts/build-npm.sh
>> Stored npm package at dist/deeplearn-VERSION.tgzTo install it locally, run npm install ./dist/deeplearn-VERSION.tgz.
On Windows, use bash (available through git) to use the scripts above.
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!
This is not an official Google product.