VetiverModel

VetiverModel(
    self,
    model,
    model_name,
    prototype_data=None,
    versioned=None,
    description=None,
    metadata=None,
    **kwargs,
)

Create VetiverModel class for serving.

Parameters

Name	Type	Description	Default
model		A trained model, such as an sklearn or torch model	required
model_name	string	Model name or ID	required
prototype_data	(pd.DataFrame, np.array)	Sample of data model should expect when it is being served	`None`
versioned		Should the model be versioned when created?	`None`
description	str	A detailed description of the model. If omitted, a brief description will be generated.	`None`
metadata	dict	Other details to be saved and accessed for serving	`None`
**kwargs		Deprecated parameters.	`{}`

Attributes

Name	Type	Description
prototype	vetiver.Prototype	Data prototype
handler_predict	Callable	Method to make predictions from a trained model

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'

Methods

Name	Description
from_pin	Create VetiverModel from pinned model.

from_pin

VetiverModel.from_pin(board, name, version=None)

Create VetiverModel from pinned model.

Parameters

Name	Type	Description	Default
board		`pins` board where model is located	required
name	str	Model name inside pins board	required
version	str	What model version should be loaded	`None`