Teradata Package for Python Function Reference | 20.00 - publish_model - 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 - 20.00

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Teradata Package for Python
Release Number
20.00
Published
March 2024
Language
English (United States)
Last Update
2024-04-10
dita:id
TeradataPython_FxRef_Enterprise_2000
Product Category
Teradata Vantage
teradataml.catalog.model_cataloging.publish_model = publish_model(name, grantee=None, status=None)
DESCRIPTION:
    Function to publish a model.
    It can be used to update the access level from PRIVATE to PUBLIC or TEAM.
    It can also be used to update the status of the model to one of the expected values.
    A model can be published only by the creator of the model.
 
PARAMETERS:
    name:
        Required Argument.
        Specifies the name of the model to be published.
        Types: str
 
    grantee:
        Optional Argument. Must be specified when status is not specified.
        Specifies a user or role (including PUBLIC) to update the model's access level to.
        Types: str
 
    status:
        Optional Argument. Must be specified when grantee is not specified.
        Specify a string to set the status of the model to.
        Types: str
        Permitted Values: ['ACTIVE', 'RETIRED', 'CANDIDATE', 'PRODUCTION', 'IN-DEVELOPMENT']
 
RETURNS:
    None.
 
RAISES:
    TeradataMlException, TypeError, ValueError
 
EXAMPLES:
    # Load the data to run the example
    load_example_data("decisionforest", ["housing_train"])
 
    # Create teradataml DataFrame objects.
    housing_train = DataFrame.from_table("housing_train")
 
    # The examples use home sales data to create a
    # classification tree that predicts home style, which can be input
    # to the DecisionForestPredict.
    formula = "homestyle ~ driveway + recroom + fullbase + gashw + airco + prefarea + price + lotsize + bedrooms + bathrms + stories + garagepl"
    rft_model = DecisionForest(data=housing_train,
                               formula = formula,
                               tree_type="classification",
                               ntree=50,
                               tree_size=100,
                               nodesize=1,
                               variance=0.0,
                               max_depth=12,
                               maxnum_categorical=20,
                               mtry=3,
                               mtry_seed=100,
                               seed=100
                               )
 
    # Let's save this generated model.
    save_model(model=rft_model, name="decision_forest_model", description="Decision Forest test")
 
    # Example 1 - Update only the access.
    publish_model('decision_forest_model', grantee='john')
 
    # Example 2 - Update only the status.
    publish_model('decision_forest_model', status='Active')
 
    # Example 3 - Update both the access and status.
    publish_model('decision_forest_model', grantee='PUBLIC', status='Production')