Ravenverse
Last updated
Last updated
The foundation for any Machine Learning or Deep Learning Framework. Simply put, it is more like a decentralized calculator, comparable to a decentralized version of the IBM machines that were used to launch the Apollo astronauts. Apart from building ML/DL frameworks, a lot more can be done on it, such as maximizing yield on your favorite DeFi protocols like Compound and more!
Ravenverse is a community-developed implementation of the decentralized computing model outlined by Raven Protocol. Within the Raven ecosystem today, there are two main actors:
Developers: Create models that need to be trained
Clients: Provide computational power to train the models
For Developers, there are three core libraries that drive the main function for computation distribution (RavOP, RavSock, and RavFTP), along with a growing list of libraries that extend the core to be more developer-friendly to use.
For Clients, there are two libraries, Ravpy is the python client for federated and distributed computing and the javascript library RavJS enables anyone with a browser to contribute processing power. Additional clients are in consideration, such as Go and Rust (looking for community devs!).
RavOP: Core operations models for distributed computation
RavSock: Socket server to moderate client connections
RavFTP: FTP server to facilitate the transfer of files
RavML: Machine learning specific library
RavDL (Coming soon): Deep learning specific library
RavViz: A dashboard to visualize operations and client connections
Ravpy: Python client for federated and distributed computing
RavJS: Javascript library to retrieve and calculate operations
Create a virtual environment with Python 3.8 before you install RDF libraries
Clone the Repository
Set up everything and install dependencies
For the latest build
For the stable build
Navigate to ravsock/config.py and set the FTP_ENVIRON_DIR variable to the bin folder of your python virtual environment. For instance:
Note: Set ENCRYPTION = True in the same file if a layer of homomorphic encryption needs to be added for Federated Analytics.
Set RDF_DATABASE_URI in the same file.
Create database with tables required for the project.
The server is now configured correctly and ready to be fired up.
Ravsock is a crucial component of RDF that facilitates both federated and distributed functionalities of the framework.
It sits between the developer(who creates ops and writes algorithms) and the contributor who contributes the idle computing power. Its scheduling algorithm oversees the distribution and statuses of different Ops, Graphs, and Subgraphs across multiple Clients.
Kindly visit FEDERATED_ANALYTICS.md for more info on creating and working with custom Federated Ops.
Kindly visit DISTRIBUTED_COMPUTING.md for more on creating graphs, initializing distributed clients in a web browser, and working with custom Ops to develop distributed ML algorithms.
Contributions are what make the open source community such a wonderful place to learn, be inspired, and create. You may contribute to our individual Repositories.
Fork
Write your code
Create a pull request
Any help you can give is much appreciated.
This project is licensed under the MIT License - see the LICENSE file for details
