What is distributed training architecture for classification models?

Distributed training splits model training across multiple machines or GPUs to handle large-scale classification tasks.

There are two main paradigms: data parallelism (same model, different data shards) and model parallelism (splitting model layers across devices). Frameworks like PyTorch DistributedDataParallel synchronize gradients across nodes. This is essential for deep learning classification tasks with large datasets or models like transformers.