Use the TDPredictor.predict method to perform prediction using teradataml DataFrame and SageMaker endpoint represented by this predictor object.
Required Arguments:
- input: Specifies the teradataml DataFrame used as input for scoring.
- mode: Specifies the mode for scoring. Permitted values include:
- 'UDF': Score in database using a Teradata UDF. This is the default value.
For this mode, the return is a teradataml DataFrame.
This mode provides faster scoring with the data from Teradata.
- 'CLIENT': Score at client side using a library.
For this mode, the return is an array or JSON.
When using mode, data is pulled from Teradata and serialized for scoring at client.
- 'UDF': Score in database using a Teradata UDF. This is the default value.
Optional Argument:
- options: Specifies the predict method with the following key-value arguments:
- udf_name: Specifies the name of the UDF used to invoke predict with UDF mode. Default value is 'tapidb.API_Request'.
- content_type: Specifies content type required for SageMaker endpoint present in the predictor. Default value is 'csv'.
- key_start_index: Specifies the index in DataFrame columns to be the key for scoring starts. Default value is 0.
Example
from tdapiclient import create_tdapi_context, TDApiClient from teradataml import DataFrame
context = create_tdapi_context("aws", "s3_bucket")
tdapiclient = TDApiClient(context)
# SKlearn takes all parameters that AWS SageMaker Library requires skLearnObject = tdapiclient.SKLearn()
df = DataFrame(tableName='t')
skLearnObject.fit(df)
predictor = skLearnObject.deploy(sagemaker_kw_args={"instance_type": "ml.m5.large", "initial_instance_count": 1})
df = DataFrame(tableName='inputTable')
outputDF = predictor.predict(df, mode='UDF', content_type='csv')