Distributed Data Parallelism (Distributed Parallel Training, often abbreviated as DDp) represents a essential technique for scaling ML model training across several devices, like GPUs or machines. This approach https://ddp578921.mpeblog.com/77909601/parallelism-guide-to-distributed-processing