Master advanced PyTorch concepts. Learn efficient training, optimization techniques, custom models, and performance best practices.

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A comprehensive workflow for production PyTorch training covering compilation with torch.compile, profiling bottlenecks with torch.profiler, scaling with DDP vs FSDP, and implementing fault-tolerant checkpointing. The guide walks through establishing a baseline, handling graph breaks and dynamic shapes, interpreting profiler traces, choosing between distributed training strategies based on model size, and using distributed checkpoint (DCP) with async saves for multi-node resilience.

The Practical Guide to Advanced PyTorch

Compile: Accelerate with torch.compile in PyTorch

Profile: Diagnose Bottlenecks with torch.profiler

Scale: Distributed Training via DDP or FSDP

Checkpoint: Recover Training Reliably with Distributed Checkpoints