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 1.1.0
pip install syft
You can also install PySyft from source on a variety of operating systems by following this installation guide.
Run Local Notebook Server
All the examples can be played with by running the command
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.
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 3700+ 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.
We are very grateful for contributions to PySyft from the following organizations!
Do NOT use this code to protect data (private or otherwise) - at present it is very insecure. Come back in a couple months.