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Part IV: torch in the Real World

You’ve learned tensors, mastered operations, and explored specialized modules. Now it’s time to put it all into practice in real-world workflows.

This part is all about performance, deployment readiness, debugging, and integration. Whether you're running models on GPUs, using NumPy, or profiling performance bottlenecks, this section shows you how to keep your models fast, safe, and reliable in production or research settings.

Chapter 17: Using torch with CUDA

This chapter unlocks the power of the GPU:

  • Check if CUDA is available and set the right device
  • Move tensors and models with .to(device)
  • Use multiple GPUs with DataParallel
  • Track memory with memory_allocated() and empty_cache()
  • Optimize training with AMP (Automatic Mixed Precision) using autocast() and GradScaler
  • Tune performance with torch.backends.cudnn.benchmark

💡 Key Insight: Speed doesn’t just come from .to('cuda') — managing memory, AMP, and reproducibility settings is just as crucial.

Chapter 18: Integration with NumPy

PyTorch plays beautifully with NumPy:

  • Convert NumPy → Tensor: torch.from_numpy(arr)
  • Convert Tensor → NumPy: tensor.numpy() (CPU-only)
  • Shared memory = fast, zero-copy — but be careful with in-place changes
  • Interfacing with: matplotlib, pandas, OpenCV, SciPy, etc.
  • Safely detach for export: .detach().cpu().numpy()

📌 Warning: .numpy() drops autograd history and fails on GPU tensors — use only in eval, visualization, or exporting.

Chapter 19: Debugging, Profiling, and Best Practices

This is your PyTorch survival guide:

  • Debug with:
  • print(tensor.shape)
  • torch.isnan(), torch.isinf()
  • .grad.norm() to catch exploding gradients
  • Catch autograd issues with torch.autograd.set_detect_anomaly(True)
  • Profile runtime using torch.profiler.profile() to spot bottlenecks
  • Track GPU memory usage: memory_summary()
  • Organize code into model.py, train.py, utils.py, debug.py
  • Best practices:
  • Zero gradients every step
  • Avoid .data (use .detach() instead)
  • Use .float() consistently with inputs/targets
  • Assert input/output shapes often

✔ Sanity Checklist Included — the go-to debugging flow for PyTorch practitioners.

Summary of Part IV

Chapter Focus Area Real-World Benefit
17 CUDA & GPU Acceleration Speed up training, scale to large models
18 NumPy Integration Seamless data exchange with the NumPy ecosystem
19 Debugging & Profiling Build robust, clean, and scalable ML systems

Part IV makes you production-ready. You’ll be faster, more efficient, and better equipped to debug and deploy real deep learning applications.

→ Next up: Appendices, Cheatsheets, and Cross-Reference Guides for quick access and review.