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Hyperparameter Optimization
Demonstrates hyperparameter optimization using Ludwig's in-built capabilities.
Preparatory Steps
- Create
datadirectory - Download Kaggle wine quality data set into the
datadirectory. Directory should appear as follows:
hyperopt/
data/
winequalityN.csv
Description
Jupyter notebook model_hyperopt_example.ipynb demonstrates several hyperparameter optimization capabilities. Key features demonstrated in the notebook:
- Training data is prepared for use
- Programmatically create Ludwig config dictionary from the training data dataframe
- Setup parameter space for hyperparameter optimization
- Perform two hyperparameter runs
- Parallel workers using random search strategy
- Serial processing using random search strategy
- Parallel workers using grid search strategy (Note: takes about 35 minutes)
- Demonstrate various Ludwig visualizations for hyperparameter optimization