Metadata-Version: 2.1
Name: syft
Version: 0.1.9a1
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
Description: # 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
        ```
        
        You can also install PySyft from source on a variety of operating systems by following this [installation guide](https://github.com/OpenMined/PySyft/blob/dev/INSTALLATION.md).
        
        ## 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!
        
        ## Troubleshooting
        
        We have written an installation example in [this colab notebook](https://colab.research.google.com/drive/14tNU98OKPsP55Y3IgFtXPfd4frqbkrxK), you can use it as is to start working with PySyft on the colab cloud, or use this setup to fix your installation locally.
        
        ## Organizational Contributions
        
        We are very grateful for contributions to PySyft from the following organizations!
        
         <img src="https://raw.githubusercontent.com/coMindOrg/federated-averaging-tutorials/master/images/comindorg_logo.png" alt="coMind" width="200" height="130"/>  
        
         [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)
        
        
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
