| :mod:`airflow.providers.amazon.aws.operators.sagemaker_tuning` |
| ============================================================== |
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| .. py:module:: airflow.providers.amazon.aws.operators.sagemaker_tuning |
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| Module Contents |
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| .. py:class:: SageMakerTuningOperator(*, config: dict, wait_for_completion: bool = True, check_interval: int = 30, max_ingestion_time: Optional[int] = None, **kwargs) |
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| Bases: :class:`airflow.providers.amazon.aws.operators.sagemaker_base.SageMakerBaseOperator` |
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| Initiate a SageMaker hyperparameter tuning job. |
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| This operator returns The ARN of the tuning job created in Amazon SageMaker. |
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| :param config: The configuration necessary to start a tuning job (templated). |
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| 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 |
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| .. attribute:: integer_fields |
| :annotation: = [['HyperParameterTuningJobConfig', 'ResourceLimits', 'MaxNumberOfTrainingJobs'], ['HyperParameterTuningJobConfig', 'ResourceLimits', 'MaxParallelTrainingJobs'], ['TrainingJobDefinition', 'ResourceConfig', 'InstanceCount'], ['TrainingJobDefinition', 'ResourceConfig', 'VolumeSizeInGB'], ['TrainingJobDefinition', 'StoppingCondition', 'MaxRuntimeInSeconds']] |
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| .. method:: expand_role(self) |
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| .. method:: execute(self, context) |
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