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Fully Sharded Data Parallel
Fully Sharded Data Parallel (FSDP) is a data parallel method that shards a model's parameters, gradients and optimizer states across the number of available GPUs (also called workers or rank). Unlike DistributedDataParallel (DDP), FSDP reduces memory-usage because a model is replicated on each GPU. This improves GPU memory-efficiency and allows you to train much larger models on fewer GPUs.