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Distributed training in PyTorch enables efficient scaling of deep learning models across multiple GPUs or nodes. This article explains four core parallelism paradigms—data, pipeline, tensor, and model parallelism—with concise conceptual breakdowns and rewritten, production-ready code examples that avoid redundancy while preserving correctness a ...

Training large-scale neural networks often exceeds the computational and memory capacity of a single device, neecssitating distributed training strategies. Among these, model parallelism (MP) plays a crucial role by partitioning the model itself across multiple devices. Within MP, pipeline parallelism (PP) stands out as an effective technique t ...