Mastering PyTorch Hooks: From Basics to Big Model Inference скачать в хорошем качестве

Mastering PyTorch Hooks: From Basics to Big Model Inference

Want X-ray vision into any PyTorch model, without touching its source code? In this beginner-friendly deep-dive you’ll master the four hook types (pre-forward, forward, backward, tensor) and use them to: • Introduction to PyTorch Hooks (00:00) • Inspect activations & gradients on the fly (01:25) • Catch shape mismatches before they crash training (04:17) • Build a mini-Accelerate for big-model inference (21:10) • Cut GPU memory by 30 % with smart caching hooks (27:25) Follow the step-by-step notebook to add hooks in sub-10 lines of code, then apply them to real debugging tasks, model interpretability, and multi-GPU training. Perfect for ML engineers who need to profile, patch, or scale PyTorch code quickly. I'm writing a course: https://maven.com/walk-with-code/scra...