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.
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.
Usage
yarn add deeplearn
or npm install deeplearn
TypeScript / ES6 JavaScript
See the TypeScript starter project and the ES6 starter project to get you quickly started. They contain a short example that sums an array with a scalar (broadcasted):
import {Array1D, ENV, Scalar} from 'deeplearn';
const math = ENV.math;
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());
ES3/ES5 JavaScript
You can also use deeplearn.js with plain JavaScript. Load the latest version of the library from jsDelivr or unpkg:
<script src="https://cdn.jsdelivr.net/npm/deeplearn"></script>
<!-- or -->
<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 dl
in the global
namespace.
var math = dl.ENV.math;
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());
Development
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
$ yarn prep # Installs dependencies.
We recommend using Visual Studio Code for
development. Make sure to install TSLint VSCode extension and the clang-format
command line tool with the Clang-Format VSCode extension for auto-formatting (Be sure to use the NPM/Yarn clang-format binary).
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 AM
Then 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:
$ yarn test
$ yarn lint
To run a subset of tests and/or on a specific browser:
$ yarn test --browsers=Chrome --grep='multinomial'
> ...
> Chrome 62.0.3202 (Mac OS X 10.12.6): Executed 28 of 1891 (skipped 1863) SUCCESS (6.914 secs / 0.634 secs)
To run the tests once and exit the karma process (helpful on Windows):
$ yarn test --single-run
To 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).js
To do a dry run and test building an npm package:
$ ./scripts/build-npm.sh
...
Stored standalone library at dist/deeplearn(.min).js
deeplearn-VERSION.tgz
To install it locally, run npm install ./deeplearn-VERSION.tgz
.
On Windows, use bash (available through git) to use the scripts above.
Looking to contribute, and don't know where to start? Check out our "help wanted" issues.
Supported environments
deeplearn.js targets environments with WebGL 1.0 or WebGL 2.0. For devices
without the OES_texture_float
extension, we fall back to fixed precision
floats backed by a gl.UNSIGNED_BYTE
texture. For platforms without WebGL,
we provide CPU fallbacks.
While the library supports most devices, our demos don't currently work on iOS Mobile or Desktop Safari. We are working on updating them, check back soon.