Source code for airflow.providers.amazon.aws.operators.sagemaker_transform

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from typing import List, Optional

from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.base_aws import AwsBaseHook
from airflow.providers.amazon.aws.operators.sagemaker_base import SageMakerBaseOperator
from airflow.utils.decorators import apply_defaults


[docs]class SageMakerTransformOperator(SageMakerBaseOperator):
    """
    Initiate a SageMaker transform job.

    This operator returns The ARN of the model created in Amazon SageMaker.

    :param config: The configuration necessary to start a transform job (templated).

        If you need to create a SageMaker transform job based on an existed SageMaker model::

            config = transform_config

        If you need to create both SageMaker model and SageMaker Transform job::

            config = {
                'Model': model_config,
                'Transform': transform_config
            }

        For details of the configuration parameter of transform_config see
        :py:meth:`SageMaker.Client.create_transform_job`

        For details of the configuration parameter of model_config, See:
        :py:meth:`SageMaker.Client.create_model`

    :type config: dict
    :param aws_conn_id: The AWS connection ID to use.
    :type aws_conn_id: str
    :param wait_for_completion: Set to True to wait until the transform job finishes.
    :type wait_for_completion: bool
    :param check_interval: If wait is set to True, the time interval, in seconds,
        that this operation waits to check the status of the transform job.
    :type check_interval: int
    :param max_ingestion_time: If wait is set to True, the operation fails
        if the transform job doesn't finish within max_ingestion_time seconds. If you
        set this parameter to None, the operation does not timeout.
    :type max_ingestion_time: int
    """

    @apply_defaults
    def __init__(
        self,
        *,
        config: dict,
        wait_for_completion: bool = True,
        check_interval: int = 30,
        max_ingestion_time: Optional[int] = None,
        **kwargs,
    ):
        super().__init__(config=config, **kwargs)
        self.config = config
        self.wait_for_completion = wait_for_completion
        self.check_interval = check_interval
        self.max_ingestion_time = max_ingestion_time
        self.create_integer_fields()

[docs]    def create_integer_fields(self) -> None:
        """Set fields which should be casted to integers."""
        self.integer_fields: List[List[str]] = [
            ['Transform', 'TransformResources', 'InstanceCount'],
            ['Transform', 'MaxConcurrentTransforms'],
            ['Transform', 'MaxPayloadInMB'],
        ]
        if 'Transform' not in self.config:
            for field in self.integer_fields:
                field.pop(0)

[docs]    def expand_role(self) -> None:
        if 'Model' not in self.config:
            return
        config = self.config['Model']
        if 'ExecutionRoleArn' in config:
            hook = AwsBaseHook(self.aws_conn_id, client_type='iam')
            config['ExecutionRoleArn'] = hook.expand_role(config['ExecutionRoleArn'])

[docs]    def execute(self, context) -> dict:
        self.preprocess_config()

        model_config = self.config.get('Model')
        transform_config = self.config.get('Transform', self.config)

        if model_config:
            self.log.info('Creating SageMaker Model %s for transform job', model_config['ModelName'])
            self.hook.create_model(model_config)

        self.log.info('Creating SageMaker transform Job %s.', transform_config['TransformJobName'])
        response = self.hook.create_transform_job(
            transform_config,
            wait_for_completion=self.wait_for_completion,
            check_interval=self.check_interval,
            max_ingestion_time=self.max_ingestion_time,
        )
        if response['ResponseMetadata']['HTTPStatusCode'] != 200:
            raise AirflowException(f'Sagemaker transform Job creation failed: {response}')
        else:
            return {
                'Model': self.hook.describe_model(transform_config['ModelName']),
                'Transform': self.hook.describe_transform_job(transform_config['TransformJobName']),
            }