Hyperparameter Optimization

Ludwig supports hyperparameter optimization using Ray Tune or a local executor.

The hyperparameter optimization strategy is specified as part of the Ludwig configuration and run using the ludwig hyperopt command. Every parameter within the config can be tuned using hyperopt.

Hyperopt Configuration

Most parameters or nested parameters of a ludwig configuration may be optimized, including input_features, output_features, combiner, preprocessing, and trainer. Supported types are float, int and category.

To enable hyperparameter optimization, add the hyperopt dictionary at the top level of your config.yaml. The hyperopt section declares which parameters to optimize, the search strategy, and the optimization goal.

hyperopt:
  parameters:
    training.learning_rate:
      space: loguniform
      lower: 0.0001
      upper: 0.1
    combiner.num_fc_layers:
      space: randint
      lower: 2
      upper: 6
  goal: minimize
  metric: loss

Running Hyperparameter Optimization

Use the ludwig hyperopt command to run hyperparameter optimization.

ludwig hyperopt --dataset reuters-allcats.csv --config hyperopt_config.yaml

For a complete reference of hyperparameter search and execution options, see the Hyperopt page of the Ludwig configuration guide.