Source code for airflow.providers.amazon.aws.operators.sagemaker_processing
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from typing import 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 SageMakerProcessingOperator(SageMakerBaseOperator):
"""
Initiate a SageMaker processing job.
This operator returns The ARN of the processing job created in Amazon SageMaker.
:param config: The configuration necessary to start a processing job (templated).
For details of the configuration parameter see :py:meth:`SageMaker.Client.create_processing_job`
:type config: dict
:param aws_conn_id: The AWS connection ID to use.
:type aws_conn_id: str
:param wait_for_completion: If wait is set to True, the time interval, in seconds,
that the operation waits to check the status of the processing job.
:type wait_for_completion: bool
:param print_log: if the operator should print the cloudwatch log during processing
:type print_log: bool
:param check_interval: if wait is set to be true, this is the time interval
in seconds which the operator will check the status of the processing job
:type check_interval: int
:param max_ingestion_time: If wait is set to True, the operation fails if the processing 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
:param action_if_job_exists: Behaviour if the job name already exists. Possible options are "increment"
(default) and "fail".
:type action_if_job_exists: str
"""
@apply_defaults
def __init__(
self,
*,
config: dict,
aws_conn_id: str,
wait_for_completion: bool = True,
print_log: bool = True,
check_interval: int = 30,
max_ingestion_time: Optional[int] = None,
action_if_job_exists: str = "increment", # TODO use typing.Literal for this in Python 3.8
**kwargs,
):
super().__init__(config=config, aws_conn_id=aws_conn_id, **kwargs)
if action_if_job_exists not in ("increment", "fail"):
raise AirflowException(
"Argument action_if_job_exists accepts only 'increment' and 'fail'. "
f"Provided value: '{action_if_job_exists}'."
)
self.action_if_job_exists = action_if_job_exists
self.wait_for_completion = wait_for_completion
self.print_log = print_log
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 = [
['ProcessingResources', 'ClusterConfig', 'InstanceCount'],
['ProcessingResources', 'ClusterConfig', 'VolumeSizeInGB'],
]
if 'StoppingCondition' in self.config:
self.integer_fields += [['StoppingCondition', 'MaxRuntimeInSeconds']]
[docs] def expand_role(self) -> None:
if 'RoleArn' in self.config:
hook = AwsBaseHook(self.aws_conn_id, client_type='iam')
self.config['RoleArn'] = hook.expand_role(self.config['RoleArn'])
[docs] def execute(self, context) -> dict:
self.preprocess_config()
processing_job_name = self.config["ProcessingJobName"]
processing_jobs = self.hook.list_processing_jobs(NameContains=processing_job_name)
# Check if given ProcessingJobName already exists
if processing_job_name in [pj["ProcessingJobName"] for pj in processing_jobs]:
if self.action_if_job_exists == "fail":
raise AirflowException(
f"A SageMaker processing job with name {processing_job_name} already exists."
)
if self.action_if_job_exists == "increment":
self.log.info("Found existing processing job with name '%s'.", processing_job_name)
new_processing_job_name = f"{processing_job_name}-{len(processing_jobs) + 1}"
self.config["ProcessingJobName"] = new_processing_job_name
self.log.info("Incremented processing job name to '%s'.", new_processing_job_name)
self.log.info("Creating SageMaker processing job %s.", self.config["ProcessingJobName"])
response = self.hook.create_processing_job(
self.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 Processing Job creation failed: %s' % response)
return {'Processing': self.hook.describe_processing_job(self.config['ProcessingJobName'])}