anoff/pysyft repository overview

⁠Introduction

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

⁠See PySyft in Action

• Emulate remote PyTorch execution⁠ - This notebook demonstrates the tensor passing between workers, though both the workers live in the same environment.

• Emulate remote PyTorch execution using sockets: Server⁠ | Client⁠ - This notebook demonstrates the tensor passing and remote execution, with workers living in different environments.

  Note: Run Server before Client

• Federated Learning⁠ - This notebook demonstrates the model training over distributed data (data belonging to multiple owners).

⁠Docker

git clone https://github.com/OpenMined/PySyft.git
cd PySyft
scripts/run_docker.sh

The container mount the examples folder on a volume so every change on the notebooks is persistent. Furthermore the container is deleted when it is stopped, in a way to facilitate development. You just have to change PySyft code, and run the run_docker.sh script to observe changes you've made on notebooks.

⁠Installation

Pick the proper PyTorch version according to your machine: CPU⁠ | CUDA9.1⁠ | CUDA9.0⁠ | CUDA8.0⁠

conda install pytorch=0.3.1 -c soumith
pip3 install -r requirements.txt
python3 setup.py install

On Windows use the following steps to install PyTorch 0.3.1-

conda install -c peterjc123 pytorch
conda install -c peterjc123 pytorch-cpu

⁠Run Unit Tests

python3 setup.py test

Alternatively, tests can be run with

make test

⁠Run Local Notebook Server

All the examples can be played with by running the command

make notebook

and selecting the pysyft kernel

Join the rapidly growing community of 2500+ on Slack⁠ and help us in our mission. We are really friendly people!

⁠Organizational Contributions

We are very grateful for contributions to PySyft from the following organizations!

coMind Website⁠ & coMind Github⁠

⁠Disclaimer

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

⁠License

Apache License 2.0⁠