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

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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 SageMakerTrainingOperator(SageMakerBaseOperator):
    """
    Initiate a SageMaker training job.

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

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

        For details of the configuration parameter see :py:meth:`SageMaker.Client.create_training_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 training job.
    :type wait_for_completion: bool
    :param print_log: if the operator should print the cloudwatch log during training
    :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 training job
    :type check_interval: int
    :param max_ingestion_time: If wait is set to True, the operation fails if the training 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
    """

[docs]    integer_fields = [
        ['ResourceConfig', 'InstanceCount'],
        ['ResourceConfig', 'VolumeSizeInGB'],
        ['StoppingCondition', 'MaxRuntimeInSeconds'],
    ]

    @apply_defaults
    def __init__(
        self,
        *,
        config: dict,
        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, **kwargs)

        self.wait_for_completion = wait_for_completion
        self.print_log = print_log
        self.check_interval = check_interval
        self.max_ingestion_time = max_ingestion_time

        if action_if_job_exists in ("increment", "fail"):
            self.action_if_job_exists = action_if_job_exists
        else:
            raise AirflowException(
                "Argument action_if_job_exists accepts only 'increment' and 'fail'. "
                f"Provided value: '{action_if_job_exists}'."
            )

[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()

        training_job_name = self.config["TrainingJobName"]
        training_jobs = self.hook.list_training_jobs(name_contains=training_job_name)

        # Check if given TrainingJobName already exists
        if training_job_name in [tj["TrainingJobName"] for tj in training_jobs]:
            if self.action_if_job_exists == "increment":
                self.log.info("Found existing training job with name '%s'.", training_job_name)
                new_training_job_name = f"{training_job_name}-{len(training_jobs) + 1}"
                self.config["TrainingJobName"] = new_training_job_name
                self.log.info("Incremented training job name to '%s'.", new_training_job_name)
            elif self.action_if_job_exists == "fail":
                raise AirflowException(
                    f"A SageMaker training job with name {training_job_name} already exists."
                )

        self.log.info("Creating SageMaker training job %s.", self.config["TrainingJobName"])
        response = self.hook.create_training_job(
            self.config,
            wait_for_completion=self.wait_for_completion,
            print_log=self.print_log,
            check_interval=self.check_interval,
            max_ingestion_time=self.max_ingestion_time,
        )
        if response['ResponseMetadata']['HTTPStatusCode'] != 200:
            raise AirflowException('Sagemaker Training Job creation failed: %s' % response)
        else:
            return {'Training': self.hook.describe_training_job(self.config['TrainingJobName'])}