The Practical Guide to Advanced PyTorch

PyTorch mastery requires following a systematic engineering workflow rather than randomly applying optimization features, with experts recommending a five-step process: baseline → compile → profile → scale → checkpoint. This approach starts with establishing a correct single-GPU reference point, then progressively adds torch.compile for acceleration, torch.profiler for bottleneck identification, distributed training via DDP or FSDP for scaling, and distributed checkpointing for fault tolerance in production workloads.