bendecoste / PySyft

Encrypted Deep Learning Library

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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 a very performant combinations of the two. See the notebooks folder for tutorials on how to use the library. Also, check the main demonstration from the Sonar project.

Setup with Docker

The fastest way to get started is to use the pre-assembled Docker image (works on all major operating systems). Alternatively, one can manually set up all the dependencies and develop locally.

Get Docker

Install Docker following the instructions on the website.

For macOS users with Homebrew installed, use:

brew cask install docker
open -a docker

You can ensure that Docker is properly installed and running by checking: docker --version.

Run PySyft in a container

First, clone this repository and navigate into its folder:

git clone
cd PySyft

Now, start a container based on openmined/pysyft:

make docker-build
make docker-run

In this container you'll be able to open a Python shell, import the library and develop with it.

However, if you want to use Jupyter, try the provided notebooks, and make changes to the source, you should create your personal development image:

make docker-build-dev
make docker-run image=openmined/pysyft-dev:local

Inside the container you can run any make targets such as test or notebook.

Local setup


  • Make sure Python 3.5+ in installed on your machine by checking python3 --version
  • Install the base libraries listed here
  • Set up a virtual environment for the Python libraries (optional, recommended)
  • None if you are using anaconda

Python Requirements

The Python dependencies are listed in requirements.txt and can be installed through

pip3 install -r requirements.txt

Additional test and development dependencies such as scikit-learn and Jupyter are listed in dev-requirements.txt:

pip3 install -r dev-requirements.txt

PySyft installation

If you simply want to to use PySyft, it is enough to install the library with:

python3 install

Instead, if you want to make changes to the source, fix bugs, add features etc., it's recommended to install PySyft in development mode:

python3 develop


Set up a new virutal environment (here called openmind), the former for user, the latter for developers.

install_for_anaconda_users.bat openmined install openmined develop


Run below, where "mode" is either "install" or "develop"

conda install -c conda-forge gmpy2
pip install -r requirements.txt
python mode


The make target notebook will launch a Jupyter server (either locally or in the container).

make notebook


The make target test will run all tests with pytest and flake8 (either locally or in the container).

make test


python 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:


Apache-2.0 by OpenMined contributors


Encrypted Deep Learning Library

License:Apache License 2.0


Language:Python 98.1%Language:Batchfile 1.9%