A Library for Private, Secure, Multi-Owner Deep Learning - Currently Pre Alpha
iamtrask Merge pull request #1517 from OpenMined/trask2
consolidated experimental notebooks
Latest commit 2cf93e3 Sep 19, 2018



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PySyft is a Python library for secure, private Deep Learning. PySyft decouples private data from model training, using Multi-Party Computation (MPC) over PyTorch and tensorflow.
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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).


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

Image size: 644MB

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.


PySyft supports Python >= 3.6 and PyTorch 0.3.1

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

Run Unit Tests

python3 setup.py test

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Organizational Contributions

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


Apache License 2.0

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