The Zero Redundancy Optimizer (ZeRO): A Definitive Technical Report on Memory-Efficient, Large-Scale Distributed Training

Section 1: Executive Summary The Zero Redundancy Optimizer (ZeRO) represents a paradigm-shifting technology from Microsoft Research, engineered to dismantle the memory bottlenecks that have historically constrained large-scale distributed training of Read More …

Report on PyTorch Fully Sharded Data Parallel (FSDP): Architecture, Performance, and Practice

Executive Summary The exponential growth in the size of deep learning models has precipitated a significant challenge in high-performance computing: the “memory wall.” Traditional distributed training methods, particularly Distributed Data Read More …

Gradient Accumulation: A Comprehensive Technical Guide to Training Large-Scale Models on Memory-Constrained Hardware

Executive Summary Gradient accumulation is a pivotal technique in modern deep learning, designed to enable the training of models with large effective batch sizes on hardware constrained by limited memory.1 Read More …