Use the TDApiClient class to create Azure Machine Learning RunConfig class.
This class can be configured with teradataml DataFrame as the input and start the training operation using 'fit' method and deploy the trained model either in Azure Machine Learning or in Vantage using 'deploy' method.
This class can be used to create Azure Machine Learning RunConfig class through TDApiClient.RunConfig function.
These functions will accept same argument as Azure Machine Learning function (RunConfig.__init__) would. The only difference is that instead of passing an Azure dataset object, you should pass teradataml DataFrame.
Following Azure Machine Learning RunConfigs are supported:
- ScriptRunConfig
- AutoMLConfig
Required Argument to create TDApiClient class:
- tdapi_context: Specifies an instance of TDAPI context created using create_tdapi_context Method. This context will be used with tdapiclient.
Example
- Import necessary packages.
from tdapliclient import create_tdapi_context, TDApiClient
- Create the TDAPI context.
context = create_tdapi_context("azure", "/td-tables") - Create TDApiClient object.
tdapiclient = TDApiClient(context)
- Create Azure Machine Learning RunConfig class using the TDApiClient object.
tdapiclient.ScriptRunConfig(List of parameters required by Azure Machine Learning ScriptRunConfig class)