PyTorch

PyTorch is the dominant framework for research and production AI. v2.5 (2025) solidifies torch.compile and introduces FlexAttention.

When to Use

• Research: 99% of new papers (Arxiv) use PyTorch.
• Production: Recommended for almost all new DL projects.
• Performance: torch.compile provides C++ level speed with Python ease.

Core Concepts

torch.compile

Just-in-Time (JIT) compilation of your model.
model = torch.compile(model) -> 2x speedup.

Dynamic Graphs (Eager Mode)

Debug line-by-line (print(tensor.shape) works).

Fabric / Lightning

High-level wrappers to simplify training loops and multi-GPU setup.

Best Practices (2025)

Do:

• Use torch.compile: It is now stable and essential for H100 performance.
• Use FlashAttention: Use the scaled dot product attention (SDPA) kernel for Transformers.
• Use PyTorch 2.x: PyTorch 1.x is legacy.

Don't:

• Don't code .cuda() manually: Use .to(device) or Fabric to handle device placement.

References

• PyTorch Documentation