Metadata-Version: 2.1
Name: syft
Version: 0.1.28a1
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 [Federated Learning](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html), [Differential Privacy](https://en.wikipedia.org/wiki/Differential_privacy), and [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=)
        
        ## Pre-Installation
        
        Optionally, we recommend that you install PySyft within the [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/overview.html) virtual environment. If you are using Windows, I suggest installing [Anaconda and using the Anaconda Prompt](https://docs.anaconda.com/anaconda/user-guide/getting-started/) to work from the command line.
        
        ```bash
        conda create -n pysyft python=3
        conda activate pysyft # some older version of conda require "source activate pysyft" instead.
        conda install jupyter notebook
        ```
        
        ## Installation
        
        > PySyft supports Python >= 3.6 and PyTorch 1.1.0
        
        ```bash
        pip install syft
        ```
        
        If you have an installation error regarding zstd, run this command and then re-try installing syft.
        
        ```bash
        pip install --upgrade --force-reinstall zstd
        ```
        If this still doesn't work, and you happen to be on OSX, make sure you have [OSX command line tools](https://railsapps.github.io/xcode-command-line-tools.html) installed and try again.
        
        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
        
        ## Use the Docker image
        
        Instead of installing all the dependencies on your computer, you can run a notebook server (which comes with Pysyft installed) using [Docker](https://www.docker.com/). All you will have to do is start the container like this:
        
        ```bash
        $ docker container run openmined/pysyft-notebook
        ```
        
        You can use the provided link to access the jupyter notebook (the link is only accessible from your local machine).
        
        > **_NOTE:_**
        > If you are using Docker Desktop for Mac, the port needs to be forwarded to localhost. In that case run docker with:
        > ```bash $ docker container run -p 8888:8888 openmined/pysyft-notebook ```
        > to forward port 8888 from the container's interface to port 8888 on localhost and then access the notebook via http://127.0.0.1:8888/?token=... 
        
        
        You can also set the directory from which the server will serve notebooks (default is /workspace).
        
        ```bash
        $ docker container run -e WORKSPACE_DIR=/root openmined/pysyft-notebook
        ```
        
        You could also build the image on your own and run it locally:
        
        ```bash
        $ cd docker-image
        $ docker image build -t pysyft-notebook .
        $ docker container run pysyft-notebook
        ```
        
        More information about how to use this image can be found [on docker hub](https://hub.docker.com/r/openmined/pysyft-notebook)
        
        ## 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.
        
        ## High-level Architecture
        
        ![alt text](art/PySyft-Arch.png "High-level Architecture")
        
        ## 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 5000+ 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://github.com/udacity/private-ai/blob/master/udacity-logo-vert-white.png?raw=true" alt="Udacity" width="200"/>](https://udacity.com/) | [<img src="https://raw.githubusercontent.com/coMindOrg/federated-averaging-tutorials/master/images/comindorg_logo.png" alt="coMind" width="200" height="130"/>](https://github.com/coMindOrg/federated-averaging-tutorials) | [<img src="https://i.ibb.co/vYwcG9N/arkhn-logo.png" alt="Arkhn" width="200" height="150"/>](http://ark.hn) | [<img src="https://raw.githubusercontent.com/dropoutlabs/files/master/dropout-labs-logo-white-2500.png" alt="Dropout Labs" width="200"/>](https://dropoutlabs.com/)
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        ## Disclaimer
        
        Do NOT use this code to protect data (private or otherwise) - at present it is very insecure. Come back in a couple months.
        
        ## 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 federated learning differential privacy
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
