Fully Sharded Data Parallel: faster AI training with fewer GPUs

What this post added

This post introduces Fully Sharded Data Parallel (FSDP), a new tool developed by FAIR Engineering to address the engineering complexity and high computational cost of training large AI models. FSDP shards model parameters across data parallel workers, enabling more efficient training of significantly larger models with fewer GPUs. It offers a simpler conceptual model compared to other parallelism techniques and improves memory and computational efficiency through communication and computation overlapping. The post details how FSDP works, its benefits over standard data parallel training, and provides guidance on its usage in language models, computer vision models, and through PyTorch Lightning and the FairScale library.