airflow-dags/reference/providers/amazon/aws/operators/sagemaker_tuning.py

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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


class SageMakerTuningOperator(SageMakerBaseOperator):
    """
    Initiate a SageMaker hyperparameter tuning job.

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

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

        For details of the configuration parameter see
        :py:meth:`SageMaker.Client.create_hyper_parameter_tuning_job`
    :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 tuning 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 tuning job.
    :type check_interval: int
    :param max_ingestion_time: If wait is set to True, the operation fails
        if the tuning 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
    """

    integer_fields = [
        ["HyperParameterTuningJobConfig", "ResourceLimits", "MaxNumberOfTrainingJobs"],
        ["HyperParameterTuningJobConfig", "ResourceLimits", "MaxParallelTrainingJobs"],
        ["TrainingJobDefinition", "ResourceConfig", "InstanceCount"],
        ["TrainingJobDefinition", "ResourceConfig", "VolumeSizeInGB"],
        ["TrainingJobDefinition", "StoppingCondition", "MaxRuntimeInSeconds"],
    ]

    @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

    def expand_role(self) -> None:
        if "TrainingJobDefinition" in self.config:
            config = self.config["TrainingJobDefinition"]
            if "RoleArn" in config:
                hook = AwsBaseHook(self.aws_conn_id, client_type="iam")
                config["RoleArn"] = hook.expand_role(config["RoleArn"])

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

        self.log.info(
            "Creating SageMaker Hyper-Parameter Tuning Job %s",
            self.config["HyperParameterTuningJobName"],
        )

        response = self.hook.create_tuning_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(f"Sagemaker Tuning Job creation failed: {response}")
        else:
            return {
                "Tuning": self.hook.describe_tuning_job(
                    self.config["HyperParameterTuningJobName"]
                )
            }