Module 6PyTorch Foundations
Training Loop Patterns
Structure a training loop that supports validation, checkpoints, and debugging.
Why this module matters
Messy training code makes reproducibility and debugging nearly impossible.
Prerequisites
- ▸ DataLoader usage
- ▸ Optimizers and losses
Learning objectives
- ▸ Separate train and eval phases clearly
- ▸ Track metrics and checkpoints
- ▸ Organize configs and seeds
Core concepts
Epoch structure
Validation discipline
Checkpoint recovery
Hands-on practice
- ▸ Refactor a messy script into clear functions
- ▸ Add checkpoint save/load
- ▸ Log train/val accuracy every epoch
Expected output
A production-style training loop skeleton.
Study checklist
- ✅ Separate train and eval phases clearly
- ✅ Track metrics and checkpoints
- ✅ Organize configs and seeds
Common mistakes
- ⚠️ Evaluating with model.train()
- ⚠️ Saving only model weights and losing optimizer state
- ⚠️ Changing too many variables between runs
Module rhythm
- 1. Read the summary and why-it-matters section first.
- 2. Work through concepts before rushing into practice.
- 3. Use the checklist to verify real understanding, not just completion.
How to continue
Next, make the loop hardware-aware so it can scale beyond CPU.
Back to course overview →How to use this page well
Treat each module as a compact learning system: understand the intuition, verify the concepts, do one hands-on task, then use the checklist and mistakes section to pressure-test your understanding.