vetiver.VetiverModel

class vetiver.VetiverModel(model, model_name: str, prototype_data=None, versioned=None, description: Optional[str] = None, metadata: Optional[dict] = None, **kwargs)

Create VetiverModel class for serving.

Parameters

• model – A trained model, such as an sklearn or torch model

• model_name (string) – Model name or ID

• prototype_data (pd.DataFrame, np.array) – Sample of data model should expect when it is being served

• versioned – Should the model be versioned when created?

• description (str) – A detailed description of the model. If omitted, a brief description will be generated.

• metadata (dict) – Other details to be saved and accessed for serving

• **kwargs (dict) – Deprecated parameters.

prototype

Data prototype

Type

vetiver.Prototype

handler_predict

Method to make predictions from a trained model

Type

Callable

Notes

VetiverModel can also take an initialized custom VetiverHandler as a model, for advanced use cases or non-supported model types. Parameter ptype_data was changed to prototype_data. Handling of ptype_data will be removed in a future version.

Examples

>>> from vetiver import mock, VetiverModel
>>> X, y = mock.get_mock_data()
>>> model = mock.get_mock_model().fit(X, y)
>>> v = VetiverModel(model = model, model_name = "my_model", prototype_data = X)
>>> v.description
'A scikit-learn DummyRegressor model'

__init__(model, model_name: str, prototype_data=None, versioned=None, description: Optional[str] = None, metadata: Optional[dict] = None, **kwargs)

Methods

__init__(model, model_name[, ...])
from_pin(board, name[, version])