Building a Production-Grade Multi-Node Training Pipeline with PyTorch DDP

A comprehensive guide to building a production-grade multi-node distributed training pipeline using PyTorch DistributedDataParallel (DDP). Covers the mental model behind DDP (process groups, ranks, all-reduce), a modular six-file project structure, centralized dataclass-based configuration, distributed lifecycle management with proper error handling, rank-aware checkpointing, efficient data loading with DistributedSampler, a training loop with AMP and gradient accumulation, multi-node torchrun launch scripts, and common performance pitfalls. Also discusses when DDP is insufficient and when to consider FSDP or DeepSpeed ZeRO.