Grid is a peer-to-peer network of data owners and data scientists who can collectively train AI models using PySyft.
- How to install
- Getting Started
- Try out the Tutorials
- Start Contributing
- High Level Architecture
- Disclaimer
Install requirements and make grid library importable in python:
$ pip install -r requirements.txt
$ python setup.py install
To boot the entire grid platform locally, we will use docker containers.
To install docker the dependencies, just follow docker documentation.
$ docker build -t node ./app/websocket/ # Build grid node image
$ docker build -t gateway ./gateway/ # Build gateway image
PS: Fell free to increase/decrease the number of initial grid nodes (you can do this by changing the docker-compose.yml file).
$ docker-compose up
Done! now we have a gateway and three nodes (by default) running locally.
A comprehensive list of tutorials can be found here. A list with latest experimental tutorials can be found in the dev branch here.
These tutorials cover how to create a grid node and what operations you can perform.
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 Grid/PySyft!
We also have a Github Project page for PySyft and Grid here.
Do NOT use this code to protect data (private or otherwise) - at present it is very insecure.