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PyTorch · Module 10 of 10

PyTorch projects and best practices

Write clean, reproducible PyTorch code.

📖 Key ideas

  1. Organise: data.py, model.py, train.py, config.
  2. Track experiments with TensorBoard or Weights & Biases.
  3. Set seeds and log hyperparameters.
  4. Use Lightning or Accelerate for multi-GPU scaling.
  5. Projects: medical image classifier, review sentiment, YOLO detector.
💡 Real-life example: Clean project structure is a tidy kitchen: everything in its place, easy to cook again.
Weights & BiasesExperiment tracking platform
AccelerateLibrary for multi-device training
Config fileStores hyperparameters
ReproducibilitySame results when rerun