This project integrates Hydra configs, Optuna optimization, and MLflow tracking to streamline hyperparameter tuning.
Note: This article references the academic demonstration version of the pipeline.
Some implementation details have been simplified or removed for IP protection.
Full implementation available under commercial license.
This project integrates Hydra configs, Optuna optimization, and MLflow tracking to streamline hyperparameter tuning:
Hydra Config: Each model’s tuning parameters-such as n_estimators or alpha-are declared in YAML (e.g., rf_optuna_trial_params.yaml). Hydra merges these configs at runtime, preventing duplication and confusion.
Optuna: Scripts like rf_optuna_trial.py create an Optuna study, sample hyperparameters from the YAML definitions, and perform cross-validation to rank trials by RMSE or R2. This approach is more efficient than naive grid searches.
MLflow Tracking: Each trial logs metrics (RMSE, R2) and final best parameters in MLflow. Artifacts, including permutation importances, are recorded automatically, providing a single interface for comparing multiple runs.
DVC: Because data versions are tracked with DVC, tuning remains tied to the exact dataset used. Incremental changes in code or config re-trigger only the relevant pipeline stages.
Overall, this system provides consistent, reproducible hyperparameter tuning. By confining parameter definitions to YAML, employing Optuna for sampling, and relying on MLflow to log metrics, teams can refine their models quickly while preserving transparency and synergy across the entire pipeline.
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