Training Pipeline

The decentralized machine learning (ML) compute pipeline within the iAgent protocol revolutionizes the way we train, use, and trade game Agents. This pipeline offers transparency, accessibility, and fair rewards distribution.

Training Pipeline

Deployment Deployment refers to the process of making a trained intelligent model available for use within the protocol, particularly within GameHub and MarketHub. Models are hosted on ComputeHub, utilizing its decentralized resources for hosting and running game agents in a game or tournament environment.

Fine-tuning Optimizing pre-trained models for a specific task, game, or environment can be achieved by fine-tuning. Through ComputeHub, users can utilize the combined computational power of the network's GPUs to fine-tune models, improving performance and Its scalability.

Training The iAgent protocol provides a platform for users to train intelligent agents within TrainHub. The Agents are considered digital assets on the blockchain, and their training is managed on the blockchain powered by decentralized computing power, ensuring transparency and fairness. Users can buy, sell, or rent agents according to their needs.

Pre-processing Pre-processing is an essential step in the ML pipeline, involving the preparation and cleaning of data before it is used for training models. With the decentralized ComputeHub, pre-processing tasks can be distributed across many GPUs, speeding up the process and allowing larger datasets to be handled.

Decentralized storage To complement ComputeHub, the iAgent protocol uses decentralized storage solutions to store intelligent models, training data, and other relevant information. This storage system ensures data integrity, redundancy, and accessibility, which are essential for the smooth operation of the ML pipeline. By storing data on a decentralized network, the protocol ensures data is secure, resilient, and permanently accessible. This adds another layer of reliability to the ML pipeline.