Training deep neural networks involves gradient descent, where the steepest path down a metaphorical hill represents the optimal direction for weight adjustment. When using multiple nodes, each node computes its gradients independently, necessitating extensive communication to merge these gradients, which can create significant networking bottlenecks. Synchronized training requires every node to be aware of all gradients from neighboring nodes, amplifying the demand for bandwidth.
