Skip to content
A library for encrypted, privacy preserving deep learning
Branch: dev
Clone or download
LaRiffle Merge pull request #1999 from OpenMined/fix_fixed_precision
changed fixed precision tensor to use mod as well as correct field
Latest commit b7731f3 Mar 18, 2019
Type Name Latest commit message Commit time
Failed to load latest commit information.
syft Merge branch 'dev' into fix_fixed_precision Mar 18, 2019
test Merge branch 'dev' into ryffel_remote_command_with_complex_response Mar 12, 2019
.pre-commit-config.yaml fix travis Feb 5, 2019
Makefile changed make test to work Mar 1, 2019 Update proportions for logo Mar 4, 2019
setup.cfg Using pytest for executing tests now. Nov 19, 2018 update dev version to 017a1 Mar 12, 2019


Binder Build Status Chat on Slack FOSSA Status

PySyft is a Python library for secure, private Deep Learning. PySyft decouples private data from model training, using Multi-Party Computation (MPC) within PyTorch. Join the movement on Slack.

PySyft in Detail

A more detailed explanation of PySyft can be found in the paper on arxiv

PySyft has also been explained in video form by Siraj Raval


PySyft supports Python >= 3.6 and PyTorch 1.0.0

pip install syft

Run Local Notebook Server

All the examples can be played with by running the command

make notebook

and selecting the pysyft kernel

Try out the Tutorials

A comprehensive list of tutorials can be found here

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. 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. The slack community is very friendly and great about quickly answering questions about the use and development of PySyft!


We have written an installation example in this colab notebook, 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!


coMind Website & coMind Github


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


Apache License 2.0

FOSSA Status

You can’t perform that action at this time.
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session.