ONNXPredict | Supported External Model Types | Teradata Package for Python - ONNXPredict - Teradata Vantage

ONNXPredict performs a prediction on each row of the input table using a model previously trained in ONNX and then loaded into the database. The model uses an interchange format called as ONNX and it is loaded to the database in Vantage in a table by the user as a blob.

The following are examples of ONNXPredict() function call.

Example Setup

• Import necessary modules.

  >>> import os, teradataml

  >>> from teradataml.options.configure import configure

  >>> from teradataml import DataFrame, load_example_data, save_byom, retrieve_byom

• Load example data.

  >>> load_example_data("byom", "iris_test")

• Create teradataml DataFrame object.

  >>> iris_test = DataFrame("iris_test")

• Set install location of the BYOM functions.

  >>> configure.byom_install_location = "mldb"

Example 1: Predict the flower species using trained 'skl_model' model

• Load model file into Vantage.

  >>> model_file_path = os.path.join(os.path.dirname(teradataml.__file__), "data", "models")

  >>> skl_model_file = os.path.join(model_file_path,
                                    "iris_db_dt_model_sklearn.onnx")

• Save the model.

  >>> save_byom("iris_db_dt_model_sklearn",
                skl_model_file, "byom_models")

• Retrieve model.

  >>> skl_model = retrieve_byom("iris_db_dt_model_sklearn",
                                table_name="byom_models")

• Using ONNXPredict to score data.

  >>> ONNXPredict_out = ONNXPredict(accumulate="id",
                                    newdata=iris_test,
                                    modeldata=skl_model)