Valhalla Protocol is a decentralized & distributed layer-2 deep-learning training protocol for neural networks. Valhalla removes added dependency on the Model replication and keeps the Model intact at the Master Node. The training of any deeper neural network and its large datasets are distributed over the network of contributors. Data is also sharded in smaller snippets. This makes it easier for calculations to pass through from machine to machine, rather than creating multiple replicas of a complicated Mode.

Valhalla crowd-sources compute power using Data and Model Parallelisation distribution approaches. Security and anonymity are guaranteed while distributing the training across multiple devices over the Internet, by being set on a blockchain. This also allows for new revenue opportunities for the contributors and partners; who are contributing to the ecosystem growth, in the form of a constant source of income from such DL training.

The nodes in a network are executed without the need for any placeholder tensors, giving more options to researchers and developers to fiddle around with their creativity and imagination. The benefit of a dynamic graph is its concurrency, and it is robust enough to handle the contributor addition or deletion, making the whole Valhalla training sustainable.