Appendix H: Best Coding Practices and Style Guidelines for PyTorch
Appendix H: Best Coding Practices and Style Guidelines for PyTorch Writing clean, efficient, and maintainable PyTorch code is essential for building scalable machine learning projects. This appendix outlines recommended best practices, architectural guidelines, coding conventions, and workflow optimizations for PyTorch development. 1. Code Structure and Organization A clean project structure improves readability and supports faster debugging. 1.1 Recommended Folder Structure project/ │ ├── data/ # datasets, raw and processed ├── models/ # model definitions (nn.Module files) ├── utils/ # helper functions (visualization, metrics) ├── configs/ # hyperparameters, settings ├── notebooks/ # experiments and prototyping ├── checkpoints/ # saved models ├── logs/ # training logs (TensorBoard, JSON) └── train.py # training script └── inference.py # testing/inference scrip...