Source code for airflow.contrib.operators.sagemaker_transform_operator

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from airflow.contrib.hooks.aws_hook import AwsHook
from airflow.contrib.operators.sagemaker_base_operator import SageMakerBaseOperator
from airflow.utils.decorators import apply_defaults
from airflow.exceptions import AirflowException

[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: string :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, wait_for_completion=True, check_interval=30, max_ingestion_time=None, *args, **kwargs): super(SageMakerTransformOperator, self).__init__(config=config, *args, **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): self.integer_fields = [ ['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): if 'Model' not in self.config: return config = self.config['Model'] if 'ExecutionRoleArn' in config: hook = AwsHook(self.aws_conn_id) config['ExecutionRoleArn'] = hook.expand_role(config['ExecutionRoleArn'])
[docs] def execute(self, context): self.preprocess_config() model_config = self.config.get('Model') transform_config = self.config.get('Transform', self.config) if model_config:'Creating SageMaker Model %s for transform job', model_config['ModelName']) self.hook.create_model(model_config)'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('Sagemaker transform Job creation failed: %s' % response) else: return { 'Model': self.hook.describe_model( transform_config['ModelName'] ), 'Transform': self.hook.describe_transform_job( transform_config['TransformJobName']
) }