Yet Another Lightning Hydra Template
Machine learning projects demand efficient workflows and reproducibility to quickly iterate on models, compare approaches, and save time and resources. This is where the "Yet Another Lightning Hydra Template" project steps in. It provides a sturdy framework based on PyTorch Lightning and Hydra, two powerful tools in the machine learning world.
Introduction
This template is designed for deep learning prototyping across various hardware accelerators including CPUs, GPUs, and TPUs. It provides a comprehensive solution that integrates well-documented best practices. Ideal for basic tasks such as Classification, Segmentation, or Metric Learning, this template can also be expanded for more complex tasks due to its modular and scalable structure.
Key Technologies
PyTorch Lightning
PyTorch Lightning is a lightweight deep learning framework that acts as a wrapper for PyTorch. It offers maximum flexibility for AI researchers and machine learning engineers without sacrificing performance, making it perfect for building and training machine learning models at scale.
Hydra
Hydra simplifies the configuration of complex applications by allowing dynamic creation of hierarchical configurations. It facilitates changing configurations easily via config files and the command line, providing a smoother workflow.
Project Structure
The project follows a common machine learning project structure but with enhancements for modularity and scalability:
- src/: Contains the source code.
- data/: Houses the project data.
- logs/: Stores logs generated by Hydra and Lightning loggers.
- tests/: Contains test scripts to ensure the reliability of your code.
- notebooks/: For Jupyter notebooks if exploratory data analysis is needed.
This setup facilitates better management and understanding of the project components, enhancing collaboration and reproducibility.
Workflow
The template's workflow is designed to boost reproducibility and efficiency. It involves:
- Setting up a Docker environment.
- Freezing Python package versions.
- Employing version control for code and data.
- Leveraging experiment tracking tools like Weights & Biases, Neptune, or CSV files for tracking progress and outcomes.
Basic Workflow
Here's a simplified guide to using the template:
- Write a custom PyTorch Lightning DataModule, which wraps around your datasets.
- Develop a PyTorch Lightning Module to define your model architecture and training steps.
- Configure your experiment settings using YAML config files.
- Execute training and evaluation commands to run experiments and analyze results.
For a quick test, the template includes a pre-built MNIST classification example. Simply run python src/train.py to see it in action.
Extending the Template
The template's high-level modularity allows you to easily switch out components for other tasks. Examples include adding new datasets via LightningDataModules, integrating different models, or employing advanced loss functions.
Conclusion
The "Yet Another Lightning Hydra Template" is an exemplary starting point for machine learning projects, streamlining the model development process with sophisticated tools and a well-organized codebase. This setup not only accelerates experimentation but also bolsters confidence in your results, paving the way for reliable and efficient machine learning solutions.