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 an API that closely mirrors the TensorFlow eager 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
You can install this library via yarn/npm: yarn add deeplearn
or npm install deeplearn
Alternatively you can use a script tag. Here we load it from a CDN.
In this case it will be available as a global variable named dl
.
You can replace also specify which version to load replacing @latest
with a specific
version string (e.g. 0.5.0
).
<!-- or -->
TypeScript / ES6 JavaScript
Let's add a scalar value to a 1D Tensor. Deeplearn js supports broadcasting the value of scalar over all the elements in the tensor.
; // If not loading the script as a global const a = dl;const b = dl; const result = a; // a is not modified, result is a new tensorresultdata; // Float32Array([3, 4, 5] // Alternatively you can use a blocking call to get the data.// However this might slow your program down if called repeatedly.console; // Float32Array([3, 4, 5]
See the TypeScript starter project and the ES6 starter project to get you quickly started.
ES5 JavaScript
Let's do the same thing in ES5 Javascript. Remember to include the script tag to load deeplearn.js
var a = dl;var b = dl; var result = a; // a is not modified, result is a new tensor // Option 1: With a Promise.resultdata; // Float32Array([3, 4, 5]) // Option 2: Synchronous download of data. Blocks the UI.console;
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.
Yarn vs NPM
Generally speaking it's up to you. Yarn is fully interoperable with npm. You can either do yarn
or npm install
. However we use yarn, and if you are adding or removing dependencies you should use yarn to keep the yarn.lock
file up to date.
Code editor
We recommend using Visual Studio Code for
development. Make sure to install
TSLint VSCode extension
and the npm clang-format 1.2.2
or later
with the
Clang-Format VSCode extension
for auto-formatting.
Interactive development
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 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.
Testing
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 : Executed 28 of 1891 SUCCESS
To run the tests once and exit the karma process (helpful on Windows):
$ yarn test --single-run
Packaging (browser and npm)
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.js
To build an npm package:
$ ./scripts/build-npm.sh...Stored standalone library at dist/deeplearn.jsdeeplearn-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.