G1Joshi 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.compileprovides 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.