Skill: Use PyTorch FSDP2 (fully_shard) correctly in a training script

This skill teaches a coding agent how to add PyTorch FSDP2 to a training loop with correct initialization, sharding, mixed precision/offload configuration, and checkpointing.

FSDP2 in PyTorch is exposed primarily via torch.distributed.fsdp.fully_shard and the FSDPModule methods it adds in-place to modules. See: references/pytorch_fully_shard_api.md, references/pytorch_fsdp2_tutorial.md.

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When to use this skill

Use FSDP2 when:

• Your model doesn’t fit on one GPU (parameters + gradients + optimizer state).
• You want an eager-mode sharding approach that is DTensor-based per-parameter sharding (more inspectable, simpler sharded state dicts) than FSDP1.
• You may later compose DP with Tensor Parallel using DeviceMesh.

Avoid (or be careful) if:

• You need strict backwards-compatible checkpoints across PyTorch versions (DCP warns against this).
• You’re forced onto older PyTorch versions without the FSDP2 stack.

Alternatives (when FSDP2 is not the best fit)

• DistributedDataParallel (DDP): Use the standard data-parallel wrapper when you want classic distributed data parallel training.
• FullyShardedDataParallel (FSDP1): Use the original FSDP wrapper for parameter sharding across data-parallel workers.

Reference: references/pytorch_ddp_notes.md, references/pytorch_fsdp1_api.md.

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Contract the agent must follow