Distributed Machine Learning:Based on Computational resources available

We propose a novel dynamic distributed training framework that applies distributed learning principles over a local area network (LAN). Each node in the network runs a unified Python-based agent (using Flask and PyTorch) and self-benchmarks its GPU performance using glmark2. The node with the lowest benchmark score is elected master, while others become workers.