Scaling deep learning models beyond a single GPU requires sophisticated parallelization strategies. This course examines Fully Sharded Data Parallel (FSDP) in PyTorch, a technique essential for training Large Language Models (LLMs) and other parameter-heavy architectures. Content addresses the limitations of Distributed Data Parallel (DDP) and implements the Zero Redundancy Optimizer (ZeRO) algorithms natively. Topics cover sharding strategies, mixed-precision training with BFloat16, activation checkpointing, and CPU offloading. The curriculum extends to multi-node cluster configuration, analyzing network bottlenecks with NCCL, and managing distributed state dicts for fault tolerance. Focus rests on performance tuning and memory efficiency for terabyte-scale models.
Prerequisites Advanced PyTorch, distributed concepts
Level:
FSDP Architecture
Architect scaling solutions using ZeRO stages to partition parameters, gradients, and optimizer states.
Memory Optimization
Implement activation checkpointing and CPU offloading to maximize per-GPU throughput.
Multi-Node Networking
Configure and tune NCCL communications for efficient cross-node scaling.
Performance Profiling
Analyze communication-computation overlap and resolve memory fragmentation issues.