Metadata-Version: 2.1
Name: syft
Version: 0.1.4a2
Summary: A Library for Private, Secure Deep Learning
Home-page: https://github.com/OpenMined/PySyft
Author: Andrew Trask
Author-email: contact@openmined.org
License: Apache-2.0
Keywords: deep learning artificial intelligence privacy secure multi-party computation
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: black
Requires-Dist: coverage
Requires-Dist: ipykernel
Requires-Dist: lz4
Requires-Dist: msgpack
Requires-Dist: pre-commit
Requires-Dist: pytest
Requires-Dist: sklearn
Requires-Dist: sphinx-rtd-theme
Requires-Dist: torch (>=1.0.0)
Requires-Dist: torchvision
Requires-Dist: websockets
Requires-Dist: zstd

# Introduction

[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/OpenMined/PySyft/master) [![Build Status](https://travis-ci.org/OpenMined/PySyft.svg?branch=torch_1)](https://travis-ci.org/OpenMined/PySyft) [![Chat on Slack](https://img.shields.io/badge/chat-on%20slack-7A5979.svg)](https://openmined.slack.com/messages/team_pysyft) [![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fmatthew-mcateer%2FPySyft.svg?type=small)](https://app.fossa.io/projects/git%2Bgithub.com%2Fmatthew-mcateer%2FPySyft?ref=badge_small)

PySyft is a Python library for secure, private Deep Learning. PySyft decouples private data from model training, using [Multi-Party Computation \(MPC\)](https://en.wikipedia.org/wiki/Secure_multi-party_computation) within PyTorch. Join the movement on [Slack](http://slack.openmined.org/).

## PySyft in Detail

A more detailed explanation of PySyft can be found in the [paper on arxiv](https://arxiv.org/abs/1811.04017)

PySyft has also been explained in video form by [Siraj Raval](https://www.youtube.com/watch?v=39hNjnhY7cY&feature=youtu.be&a=)


## Installation

> PySyft supports Python &gt;= 3.6 and PyTorch 1.0.0

```bash
pip install syft
```
## Run Local Notebook Server
All the examples can be played with by running the command
```bash
make notebook
```
and selecting the pysyft kernel

## Try out the Tutorials

A comprehensive list of tutorials can be found [here](https://github.com/OpenMined/PySyft/tree/master/examples/tutorials)

These tutorials cover how to perform techniques such as federated learning and differential privacy using PySyft.

## Start Contributing

The guide for contributors can be found [here](https://github.com/OpenMined/PySyft/tree/master/CONTRIBUTING.md). It covers all that you need to know to start contributing code to PySyft in an easy way.

Also join the rapidly growing community of 2500+ on [Slack](http://slack.openmined.org). The slack community is very friendly and great about quickly answering questions about the use and development of PySyft!

## Organizational Contributions

We are very grateful for contributions to PySyft from the following organizations!

 ![drawing](https://raw.githubusercontent.com/coMindOrg/federated-averaging-tutorials/master/images/comindorg_logo.png)  

 [coMind Website](https://comind.org/) & [coMind Github](https://github.com/coMindOrg/federated-averaging-tutorials)

## Disclaimer

Do NOT use this code to protect data (private or otherwise) - at present it is very insecure.

## License

[Apache License 2.0](https://github.com/OpenMined/PySyft/blob/master/LICENSE)

[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fmatthew-mcateer%2FPySyft.svg?type=large)](https://app.fossa.io/projects/git%2Bgithub.com%2Fmatthew-mcateer%2FPySyft?ref=badge_large)



