def __init_

def init()

in src/stepfunctions/template/pipeline/train.py [0:0]
12 lines of code
3 McCabe index (conditional complexity)

    def __init__(self, estimator, role, inputs, s3_bucket, client=None, **kwargs):
        """
        Args:
            estimator (sagemaker.estimator.EstimatorBase): The estimator to use for training. Can be a BYO estimator, Framework estimator or Amazon algorithm estimator.
            role (str): An AWS IAM role (either name or full Amazon Resource Name (ARN)). This role is used to create, manage, and execute the Step Functions workflows.
            inputs: Information about the training data. Please refer to the `fit()` method of the associated estimator, as this can take any of the following forms:

                * (str) - The S3 location where training data is saved.
                * (dict[str, str] or dict[str, `sagemaker.inputs.TrainingInput`]) - If using multiple channels for training data, you can specify a dict mapping channel names to strings or `sagemaker.inputs.TrainingInput` objects.
                * (`sagemaker.inputs.TrainingInput`) - Channel configuration for S3 data sources that can provide additional information about the training dataset. See `sagemaker.inputs.TrainingInput` for full details.
                * (`sagemaker.amazon.amazon_estimator.RecordSet`) - A collection of Amazon `Record` objects serialized and stored in S3. For use with an estimator for an Amazon algorithm.
                * (list[`sagemaker.amazon.amazon_estimator.RecordSet`]) - A list of `sagemaker.amazon.amazon_estimator.RecordSet` objects, where each instance is a different channel of training data.
            s3_bucket (str): S3 bucket under which the output artifacts from the training job will be stored. The parent path used is built using the format: ``s3://{s3_bucket}/{pipeline_name}/models/{job_name}/``. In this format, `pipeline_name` refers to the keyword argument provided for TrainingPipeline. If a `pipeline_name` argument was not provided, one is auto-generated by the pipeline as `training-pipeline-<timestamp>`. Also, in the format, `job_name` refers to the job name provided when calling the :meth:`TrainingPipeline.run()` method.
            client (SFN.Client, optional): boto3 client to use for creating and interacting with the training pipeline in Step Functions. (default: None)

        Keyword Args:
            pipeline_name (str, optional): Name of the pipeline. This name will be used to name jobs (if not provided when calling execute()), models, endpoints, and S3 objects created by the pipeline. If a `pipeline_name` argument was not provided, one is auto-generated by the pipeline as `training-pipeline-<timestamp>`. (default:None)
        """
        self.estimator = estimator
        self.inputs = inputs
        
        for key in self.__class__.__allowed_kwargs:
            setattr(self, key, kwargs.pop(key, None))

        if not self.pipeline_name:
            self.__pipeline_name_unique = True
            self.pipeline_name = 'training-pipeline-{date}'.format(date=self._generate_timestamp())

        self.definition = self.build_workflow_definition()
        self.input_template = self._extract_input_template(self.definition)

        workflow = Workflow(name=self.pipeline_name, definition=self.definition, role=role, format_json=True, client=client)

        super(TrainingPipeline, self).__init__(s3_bucket=s3_bucket, workflow=workflow, role=role, client=client)
def __init__()

def init()
