def fromEndpoint()

in sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerModel.py [0:0]
28 lines of code
2 McCabe index (conditional complexity)

    def fromEndpoint(cls,
                     endpointName,
                     requestRowSerializer,
                     responseRowDeserializer,
                     modelEnvironmentVariables=None,
                     sagemakerClient=SageMakerClients.create_sagemaker_client(),
                     prependResultRows=True,
                     namePolicy=RandomNamePolicy(),
                     uid="sagemaker"):

        """ Creates a JavaSageMakerModel from existing model data in S3.

        The returned JavaSageMakerModel can be used to transform Dataframes.

        Args:
            endpointName (str): The name of an endpoint that is currently in service.
            requestRowSerializer (RequestRowSerializer): Serializes a row to an array of bytes.
            responseRowDeserializer (ResponseRowDeserializer): Deserializes an array of bytes to a
                series of rows.
            modelEnvironmentVariables: The environment variables that SageMaker will set on the
                model container during execution.
            sagemakerClient (AmazonSageMaker) Amazon SageMaker client. Used to send
                CreateTrainingJob, CreateModel, and CreateEndpoint requests.
            prependResultRows (bool): Whether the transformation result should also include the
                input Rows. If true, each output Row is formed by a concatenation of the input Row
                with the corresponding Row produced by SageMaker invocation, produced by
                responseRowDeserializer. If false, each output Row is just taken from
                responseRowDeserializer.
            namePolicy (NamePolicy): The NamePolicy to use when naming SageMaker entities created
                during usage of the returned model.
            uid (String): The unique identifier of the SageMakerModel. Used to represent the stage
                in Spark ML pipelines.

        Returns:
            JavaSageMakerModel:
                A JavaSageMakerModel that sends InvokeEndpoint requests to an endpoint hosting
                the training job's model.

        """

        scala_function = "%s.fromEndpoint" % SageMakerModel._wrapped_class

        if modelEnvironmentVariables is None:
            modelEnvironmentVariables = {}

        model_java_obj = SageMakerJavaWrapper()._new_java_obj(
            scala_function,
            endpointName,
            requestRowSerializer,
            responseRowDeserializer,
            modelEnvironmentVariables,
            sagemakerClient,
            prependResultRows,
            namePolicy,
            uid)

        return SageMakerModel(
            endpointInstanceType=None,
            endpointInitialInstanceCount=None,
            requestRowSerializer=requestRowSerializer,
            responseRowDeserializer=responseRowDeserializer,
            javaObject=model_java_obj)