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A client-side microlibrary for running PySyft operations in Javascript
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README.md

Syft.js

Travis (.org) codecov npm GitHub

Introduction to Syft.js

Of course, PySyft has the ability to run in its own environment. But if you would like to train FL models in the browser, you must resort to using some ML framework like TensorFlow.js.

Syft.js is a microlibrary built on top of TensorFlow.js, allowing for a socket connection with any running PySyft instance.

PySyft acts as the parent node, instructing child nodes (Syft.js instances running in a website on users' browsers) of what tensors to add to a list, remove from a list, and operate against.

Link to full documentation here

Installation

If you're using a package manage like NPM:

npm install --save syft.js @tensorflow/tfjs

Or if Yarn is your cup of tea:

yarn add syft.js @tensorflow/tfjs

When using a package manager, TensorFlow.js will be automatically installed.

If you're not using a package manager, you can also include Syft.js within a <script> tag:

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.2.5/dist/tf.min.js"></script>
<script src="https://unpkg.com/syft.js@latest"></script>

For integration into your client-side application, please check out our guide.

For further API documentation, please check that out here.

Local Development

  1. Fork and clone
  2. Run npm install
  3. Run npm start
  4. Do your work.
  5. Push to your clone
  6. Submit a PR to openmined/syft.js

Contributing

We're accepting PR's for testing at the moment to improve our overall code coverage. In terms of core functionality, we're considering the current version of Syft.js feature complete until a further roadmap is designated.

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