How to use the stepfunctions.steps.TrainingStep function in stepfunctions

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github aws / aws-step-functions-data-science-sdk-python / src / stepfunctions / template / pipeline / inference.py View on Github external
def build_workflow_definition(self):
        """
        Build the workflow definition for the inference pipeline with all the states involved.

        Returns:
            :class:`~stepfunctions.steps.states.Chain`: Workflow definition as a chain of states involved in the the inference pipeline.
        """
        default_name = self.pipeline_name

        train_instance_type = self.preprocessor.train_instance_type
        train_instance_count = self.preprocessor.train_instance_count

        # Preprocessor for feature transformation
        preprocessor_train_step = TrainingStep(
            StepId.TrainPreprocessor.value,
            estimator=self.preprocessor,
            job_name=default_name + '/preprocessor-source',
            data=self.inputs,
        )
        preprocessor_model = self.preprocessor.create_model()
        preprocessor_model_step = ModelStep(
            StepId.CreatePreprocessorModel.value,
            instance_type=train_instance_type,
            model=preprocessor_model,
            model_name=default_name
        )
        preprocessor_transform_step = TransformStep(
            StepId.TransformInput.value,
            transformer=self.preprocessor.transformer(instance_count=train_instance_count, instance_type=train_instance_type, max_payload=20),
            job_name=default_name,
github aws / aws-step-functions-data-science-sdk-python / src / stepfunctions / template / pipeline / inference.py View on Github external
)
        preprocessor_transform_step = TransformStep(
            StepId.TransformInput.value,
            transformer=self.preprocessor.transformer(instance_count=train_instance_count, instance_type=train_instance_type, max_payload=20),
            job_name=default_name,
            model_name=default_name,
            data=self.inputs['train'],
            compression_type=self.compression_type,
            content_type=self.content_type
        )

        # Training
        train_instance_type = self.estimator.train_instance_type
        train_instance_count = self.estimator.train_instance_count

        training_step = TrainingStep(
            StepId.Train.value,
            estimator=self.estimator,
            job_name=default_name + '/estimator-source',
            data=self.inputs,
        )

        pipeline_model = PipelineModel(
            name='PipelineModel',
            role=self.estimator.role,
            models=[
                self.preprocessor.create_model(),
                self.estimator.create_model()
            ]
        )
        pipeline_model_step = ModelStep(
            StepId.CreatePipelineModel.value,