Skip to content

Latest commit

 

History

History
executable file
·
140 lines (104 loc) · 6.88 KB

README.md

File metadata and controls

executable file
·
140 lines (104 loc) · 6.88 KB

Syft

Chat on Slack Build Status

Homomorphically Encrypted Deep Learning Library

The goal of this library is to give the user the ability to efficiently train Deep Learning models in a homomorphically encrypted state without needing to be an expert in either. Furthermore, by understanding the characteristics of both Deep Learning and Homomorphic Encryption, we hope to find very performant combinations of the two. See notebooks folder for tutorials on how to use the library.

Setup

Prerequisites

# Get dependencies ready
pip install -r requirements.txt
# install the lib locally
python setup.py install

Installation

The recommended method is using Docker (works on all major operating systems).

For Docker Users

Install Docker from its website. For macOS users with Homebrew installed, use brew cask install docker. Once installed, launch the Docker application. Ensure that docker is installed and running properly by checking the version: docker -v.

For Anaconda Users

bash install_for_anaconda_users.sh

Note: If you are having trouble with installation, after you run the above script, while you try to import Syft in your Jupyter notebook, try this:

source activate openmined  # activate your env, if you haven't already
pip install ipykernel
python -m ipykernel install --user --name=openmined
Windows
conda install -c conda-forge gmpy2
pip install -r requirements.txt
python setup.py install

Usage

Start

Then, run:

git clone https://github.com/OpenMined/PySyft.git
cd PySyft
make run

If you want create a local Docker with Jupyter:

docker build -f Development-Dockerfile -t "pysyft" .
make custom docker=pysyft

Tests

cd PySyft
make test

For Contributors

If you are interested in contributing to Syft, first check out our Contributor Quickstart Guide and then sign into our Slack Team channel #team_pysyft to let us know which projects sound interesting to you! (or propose your own!).

Relevant Literature

As both Homomorphic Encryption and Deep Learning are still somewhat sparsely known, below is a curated list of relevant reading materials to bring you up to speed with the major concepts and themes of these exciting fields.

Encrypted Deep Learning - Recommended Reading:

Homomorphic Encryption - Recommended Reading:

Relevant Papers:

Related Libraries:

Related Blogs:

License

Apache-2.0 by OpenMined contributors