a project where AI can be trained decentralized, using the decentralized storage provider, BNB Greenfield.
- Install the requirements
pip install - r requirements.txt
- Run the
parameter server
or professor nodes (PN
), that will listen to the AI train requests from the BNB Chain
python main.py
- Once the PN is up and running, connect the worker nodes, this will be incharge of computing the model and maintaining a two-way communication with the professor nodes.
python .\workers\main.py
As the worker establishes the connection, the PN will coordinate with the given model and data, and using tensorflow's
ParameterServerStrategy
, it will distribute the model to the workers, and will start the training process.
-
Currently, it’s all backend, will have to work on the user interface side as well
-
Professor nodes are listening to the events
-
Worker nodes are connected to the professor nodes
-
The model is distributed to the workers
-
Downloading the files from Greenfield and then processing it locally, and then uploading the model with the user only access to the file (encrypted model)
-
while downloading, the speed of accessing the resources is very slow.
Further, I need more help from the BNB Chain team, to get the things done and in the correct way.