Configuration

Ludwig models are configured by a single config with the following keys:

model_type: ecd
input_features: []
combiner: {}
output_features: []
preprocessing: {}
defaults: {}
trainer: {}
hyperopt: {}

The config specifies input features, output features, preprocessing, model architecture, training loop, hyperparameter search, and backend infrastructure -- everything that's needed to build, train, and evaluate a model.

The Ludwig configuration mixes ease of use, by means of reasonable defaults, with flexibility, by means of detailed control over the parameters of your model. Only input_features and output_features are required while all other fields use reasonable defaults, but can be optionally set or modified manually.

The config can be expressed as a python dictionary (--config_str for Ludwig's CLI), or as a YAML file (--config).

YAMLPython Dict

input_features:
    -
        name: Pclass
        type: category
    -
        name: Sex
        type: category
    -
        name: Age
        type: number
        preprocessing:
            missing_value_strategy: fill_with_mean
    -
        name: SibSp
        type: number
    -
        name: Parch
        type: number
    -
        name: Fare
        type: number
        preprocessing:
            missing_value_strategy: fill_with_mean
    -
        name: Embarked
        type: category

output_features:
    -
        name: Survived
        type: binary

{
    "input_features": [
        {
            "name": "Pclass",
            "type": "category"
        },
        {
            "name": "Sex",
            "type": "category"
        },
        {
            "name": "Age",
            "type": "number",
            "preprocessing": {
                "missing_value_strategy": "fill_with_mean"
            }
        },
        {
            "name": "SibSp",
            "type": "number"
        },
        {
            "name": "Parch",
            "type": "number"
        },
        {
            "name": "Fare",
            "type": "number",
            "preprocessing": {
                "missing_value_strategy": "fill_with_mean"
            }
        },
        {
            "name": "Embarked",
            "type": "category"
        }
    ],
    "output_features": [
        {
            "name": "Survived",
            "type": "binary"
        }
    ]
}