BT

Propel Shifts Plans to Leverage TensorFlow.js

| by Dylan Schiemann Follow 4 Followers on May 21, 2018. Estimated reading time: 1 minute |

The Propel JavaScript scientific computing and machine learning library has announced a change in the project's direction. Just a few weeks after Propel's initial launch in March 2018, TensorFlow.js announced its release. Propel's initial efforts extended deeplearn.js and the C implementation of TensorFlow. Tensorflow.js is an evolution of deeplearn.js, a JavaScript library released by Google.

Given the similarity between TensorFlow.js and Propel's lower-level approach, the Propel project team quickly realized that it would be better to converge on a shared platform:

TensorFlow.js was recently released. It is well engineered, provides an autograd-style interface to backprop, and has committed to supporting Node. This satisfies our requirements. It is counterproductive to pursue a parallel effort. Thus we are abandoning our backprop implementation, TF C binding, and the TF/DL bridge, which made up the foundation of the Propel library. We intend to rebase our work on top of TFJS.

As such, the Propel project is currently rebooting. The website and examples for using Propel are no longer available, and public-facing activity within the project has been minimal over the past few weeks as the Propel team works to define their new direction:

Our high-level goal continues to be a productive workflow for scientific computing in JavaScript. Building on top of TFJS allows us to focus on higher-level functionality.     

Similar to the original early work on Propel, TensorFlow.js also leverages WebGL for GPU-supported numerical operations. According to the TensorFlow.js team, support for Node.js is now available:

Yes! We recently released Node.js bindings for TensorFlow. This allows the same JavaScript code to work on both the browser and Node.js, while binding to the underlying TensorFlow C implementation in node. You can follow its development on GitHub or try out our NPM package.

As part of the release of TensorFlow.js, the deeplearn.js library has become TensorFlow.js Core. Beyond the core library, TensorFlow.js adds a Layers API for building machine learning models and tools for automatically porting TensorFlow SavedModels and Keras HDF5 models.

Propel and TensorFlow.js are both open source projects under the Apache 2.0 license. Contributions are encouraged via GitHub repositories for TensorFlow.js and Propel.

Rate this Article

Adoption Stage
Style

Hello stranger!

You need to Register an InfoQ account or or login to post comments. But there's so much more behind being registered.

Get the most out of the InfoQ experience.

Tell us what you think

Allowed html: a,b,br,blockquote,i,li,pre,u,ul,p

Email me replies to any of my messages in this thread
Community comments

Allowed html: a,b,br,blockquote,i,li,pre,u,ul,p

Email me replies to any of my messages in this thread

Allowed html: a,b,br,blockquote,i,li,pre,u,ul,p

Email me replies to any of my messages in this thread

Discuss

Login to InfoQ to interact with what matters most to you.


Recover your password...

Follow

Follow your favorite topics and editors

Quick overview of most important highlights in the industry and on the site.

Like

More signal, less noise

Build your own feed by choosing topics you want to read about and editors you want to hear from.

Notifications

Stay up-to-date

Set up your notifications and don't miss out on content that matters to you

BT