def _generate_model_monitor()

in src/sagemaker/workflow/check_job_config.py [0:0]

• 68 lines of code
• 5 McCabe index (conditional complexity)

    def _generate_model_monitor(self, mm_type: str) -> Optional[ModelMonitor]:
        """Generates a ModelMonitor object

        Generates a ModelMonitor object with required config attributes for
            QualityCheckStep and ClarifyCheckStep

        Args:
            mm_type (str): The subclass type of ModelMonitor object.
                A valid mm_type should be one of the following: "DefaultModelMonitor",
                "ModelQualityMonitor", "ModelBiasMonitor", "ModelExplainabilityMonitor"

        Return:
            sagemaker.model_monitor.ModelMonitor or None if the mm_type is not valid

        """
        if mm_type == "DefaultModelMonitor":
            monitor = DefaultModelMonitor(
                role=self.role,
                instance_count=self.instance_count,
                instance_type=self.instance_type,
                volume_size_in_gb=self.volume_size_in_gb,
                volume_kms_key=self.volume_kms_key,
                output_kms_key=self.output_kms_key,
                max_runtime_in_seconds=self.max_runtime_in_seconds,
                base_job_name=self.base_job_name,
                sagemaker_session=self.sagemaker_session,
                env=self.env,
                tags=self.tags,
                network_config=self.network_config,
            )
        elif mm_type == "ModelQualityMonitor":
            monitor = ModelQualityMonitor(
                role=self.role,
                instance_count=self.instance_count,
                instance_type=self.instance_type,
                volume_size_in_gb=self.volume_size_in_gb,
                volume_kms_key=self.volume_kms_key,
                output_kms_key=self.output_kms_key,
                max_runtime_in_seconds=self.max_runtime_in_seconds,
                base_job_name=self.base_job_name,
                sagemaker_session=self.sagemaker_session,
                env=self.env,
                tags=self.tags,
                network_config=self.network_config,
            )
        elif mm_type == "ModelBiasMonitor":
            monitor = ModelBiasMonitor(
                role=self.role,
                instance_count=self.instance_count,
                instance_type=self.instance_type,
                volume_size_in_gb=self.volume_size_in_gb,
                volume_kms_key=self.volume_kms_key,
                output_kms_key=self.output_kms_key,
                max_runtime_in_seconds=self.max_runtime_in_seconds,
                base_job_name=self.base_job_name,
                sagemaker_session=self.sagemaker_session,
                env=self.env,
                tags=self.tags,
                network_config=self.network_config,
            )
        elif mm_type == "ModelExplainabilityMonitor":
            monitor = ModelExplainabilityMonitor(
                role=self.role,
                instance_count=self.instance_count,
                instance_type=self.instance_type,
                volume_size_in_gb=self.volume_size_in_gb,
                volume_kms_key=self.volume_kms_key,
                output_kms_key=self.output_kms_key,
                max_runtime_in_seconds=self.max_runtime_in_seconds,
                base_job_name=self.base_job_name,
                sagemaker_session=self.sagemaker_session,
                env=self.env,
                tags=self.tags,
                network_config=self.network_config,
            )
        else:
            logging.warning(
                'Expected model monitor types: "DefaultModelMonitor", "ModelQualityMonitor", '
                '"ModelBiasMonitor", "ModelExplainabilityMonitor"'
            )
            return None
        return monitor