AI & Data
Best Practice: Use version control for model code and experiments
Sep 12, 2024
In the fast-paced world of AI and machine learning, tracking changes to model code and hyperparameters is critical for ensuring reproducibility and collaboration. Version control for both code and experiments provides a structured way to document, manage, and revert changes, ensuring that model development follows a clear, organised process.
Why Version Control for Models and Experiments Matters
- Reproducibility: Version control ensures that all changes to model code, configurations, and data are tracked, making it easy to reproduce experiments and validate results.
- Collaboration: By using version control, teams can work collaboratively on model development, track individual contributions, and merge changes without losing progress.
- Experiment tracking: Documenting hyperparameter settings, training configurations, and results allows teams to compare different model versions and select the best-performing one.
- Traceability: Version control provides a detailed history of all changes, allowing teams to trace the evolution of the model and quickly revert to previous versions if necessary.
Implementing This Best Practice
- Use Git for code versioning: Git is the industry standard for version control, allowing teams to track changes to model code, manage branches, and collaborate efficiently. Ensure that all model code is stored in a Git repository for easy access and version tracking.
- Track experiments with MLflow or Weights & Biases: Use experiment tracking tools like MLflow or Weights & Biases to log hyperparameters, configurations, and training results. These tools provide dashboards and reports that make it easy to compare different model versions.
- Document changes: Make sure to write clear commit messages and maintain detailed documentation for all changes made to the model code or hyperparameters. This makes it easier for other team members to understand the context of changes.
Conclusion
Using version control for model code and experiments is essential for maintaining reproducibility, collaboration, and traceability in AI development. By leveraging tools like Git, MLflow, or Weights & Biases, teams can manage model evolution effectively and ensure that the best models are deployed.
