ScratchPytorchMLRepo

🚀 Your Foundation for Sustainable ML Projects

This repository provides a well-structured, best-practice layout to kickstart your Machine Learning endeavors in pytorch-(lightning). It embodies my preferred defaults for building maintainable and scalable ML projects. Dive in and adapt it to your specific needs!

🏭 Development Principles

This project adheres to the following development principles to ensure maintainability, reproducibility, and flexibility:

• Configuration-Driven:

  • All critical hyperparameters and training configurations for training, validation, and testing scripts, as well as model classes, are managed through YAML configuration files.
  • A single YAML file encapsulates both training parameters and the model architecture for each training run.
  • This design choice guarantees the reproducibility of machine learning experiments by clearly defining all settings.

• Modular Training with PyTorch Lightning:

  • Training, testing, and validation pipelines are built using a 100% modular PyTorch Lightning framework.
  • Combined with the configuration files, this enables significant flexibility in utilizing various callbacks, learning rate schedules, and multi-GPU training setups with zero code changes.

• Optional Experiment Tracking with Comet ML:

  • Comet ML is integrated as a default yet optional platform for logging and monitoring machine learning experiments.
  • Experiment tracking can be easily disabled by not configuring your Comet API key as an variable at runtime, providing flexibility for users who prefer alternative tools or local runs.

• Automated Code Quality and Consistency:

  • The GitHub repository is configured to automatically enforce code formatting upon each commit using the Black code style, enhancing code readability and maintainability.
  • Furthermore, mypy is employed as a type checking hook to improve code reliability and reduce potential errors.
  • autoflake is integrated to automatically remove unused imports, contributing to improved code efficiency.

• Rigorous Modularization and Execution:

  • A strong emphasis is placed on modularity across all class imports, promoting code organization and reusability.
  • To execute any script within the repository, please use the following consistent invocation method: python -m ML_Module.<path_to_script>. This ensures proper module resolution and execution within the project structure.