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

This article provides a practical guide to scaling deep learning across multiple machines using PyTorch Distributed Data Parallel (DDP). It covers essential concepts like NCCL process groups and gradient synchronization, offering code-driven solutions for building production-ready multi-node training pipelines. The guide helps practitioners efficiently distribute training workloads across GPUs on different machines.