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from airflow.contrib.hooks.sagemaker_hook import SageMakerHook
from airflow.contrib.sensors.sagemaker_base_sensor import SageMakerBaseSensor
from airflow.utils.decorators import apply_defaults
[docs]class SageMakerTuningSensor(SageMakerBaseSensor):
"""
Asks for the state of the tuning state until it reaches a terminal state.
The sensor will error if the job errors, throwing a AirflowException
containing the failure reason.
:param job_name: job_name of the tuning instance to check the state of
:type job_name: str
"""
[docs] template_fields = ['job_name']
@apply_defaults
def __init__(self,
job_name,
*args,
**kwargs):
super(SageMakerTuningSensor, self).__init__(*args, **kwargs)
self.job_name = job_name
[docs] def non_terminal_states(self):
return SageMakerHook.non_terminal_states
[docs] def failed_states(self):
return SageMakerHook.failed_states
[docs] def get_sagemaker_response(self):
sagemaker = SageMakerHook(aws_conn_id=self.aws_conn_id)
self.log.info('Poking Sagemaker Tuning Job %s', self.job_name)
return sagemaker.describe_tuning_job(self.job_name)
[docs] def get_failed_reason_from_response(self, response):
return response['FailureReason']
[docs] def state_from_response(self, response):
return response['HyperParameterTuningJobStatus']