Teradata Package for Python Function Reference on VantageCloud Lake - _LightgbmSklearnWrapper.deploy - Teradata Package for Python - Look here for syntax, methods and examples for the functions included in the Teradata Package for Python.
Teradata® Package for Python Function Reference on VantageCloud Lake
- Deployment
- VantageCloud
- Edition
- Lake
- Product
- Teradata Package for Python
- Release Number
- 20.00.00.03
- Published
- December 2024
- ft:locale
- en-US
- ft:lastEdition
- 2024-12-19
- dita:id
- TeradataPython_FxRef_Lake_2000
- Product Category
- Teradata Vantage
- teradataml.opensource._lightgbm._LightgbmSklearnWrapper.deploy = deploy(self, model_name, replace_if_exists=False)
- DESCRIPTION:
Deploys the model held by interface object to Vantage.
PARAMETERS:
model_name:
Required Argument.
Specifies the unique name of the model to be deployed.
Types: str
replace_if_exists:
Optional Argument.
Specifies whether to replace the model if a model with the same name already
exists in Vantage. If this argument is set to False and a model with the same
name already exists, then the function raises an exception.
Default Value: False
Types: bool
RETURNS:
The opensource object wrapper.
RAISES:
TeradataMLException if model with "model_name" already exists and the argument
"replace_if_exists" is set to False.
EXAMPLES:
# Import the required libraries and create LGBMClassifier Opensource object wrapper.
>>> from teradataml import td_lightgbm
>>> model = td_lightgbm.LGBMClassifier()
>>> model
LGBMClassifier()
# Example 1: Deploy the model held by LGBMClassifier Opensource object to Vantage.
>>> lgbm_cls = model.deploy("lgbm_cls_model_ver_2")
Model is saved.
>>> lgbm_cls
LGBMClassifier()
# Example 2: Deploy the model held by LGBMClassifier Opensource object to Vantage with
# the name same as that of model that already existed in Vantage.
>>> lgbm_cls = model.deploy("lgbm_cls_model_ver_2", replace_if_exists=True)
Model is deleted.
Model is saved.
>>> lgbm_cls
LGBMClassifier()