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('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']),
}