Creates a schedule that regularly starts Amazon SageMaker AI Processing Jobs to monitor the data captured for an Amazon SageMaker AI Endpoint.
Parameters:
-
monitoring_schedule_name
(Optional[MonitoringScheduleName], default:
None
)
–
The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
-
monitoring_schedule_config
(Optional[MonitoringScheduleConfig], default:
None
)
–
The configuration object that specifies the monitoring schedule and defines the monitoring job.
-
tags
(Optional[TagList], default:
None
)
–
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
Raises:
-
ResourceInUse
–
Resource being accessed is in use.
-
ResourceLimitExceeded
–
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
UnknownServiceError
–
The service returned an error code this client does not model.
Source code in docs_src/capo_sagemaker/_services/sage_maker.py
| def create_monitoring_schedule(
self,
*,
config_overrides: Optional[SageMakerClientConfig] = None,
monitoring_schedule_name: Optional[
"capo_sagemaker.types.monitoring_schedule_name.MonitoringScheduleName"
] = None,
monitoring_schedule_config: Optional[
"capo_sagemaker.types.monitoring_schedule_config.MonitoringScheduleConfig"
] = None,
tags: Optional["capo_sagemaker.types.tag_list.TagList"] = None,
) -> "capo_sagemaker.types.create_monitoring_schedule_response.CreateMonitoringScheduleResponse":
"""<p>Creates a schedule that regularly starts Amazon SageMaker AI Processing Jobs to monitor the data captured for an Amazon SageMaker AI Endpoint.</p>
Args:
monitoring_schedule_name: <p>The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.</p>
monitoring_schedule_config: <p>The configuration object that specifies the monitoring schedule and defines the monitoring job.</p>
tags: <p>(Optional) An array of key-value pairs. For more information, see <a href=" https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL">Using Cost Allocation Tags</a> in the <i>Amazon Web Services Billing and Cost Management User Guide</i>.</p>
Raises:
capo_sagemaker.errors.resource_in_use.ResourceInUse: <p>Resource being accessed is in use.</p>
capo_sagemaker.errors.resource_limit_exceeded.ResourceLimitExceeded: <p> You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created. </p>
capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
"""
def _handler(
req: "OperationRequest[capo_sagemaker.types.create_monitoring_schedule_request.CreateMonitoringScheduleRequest]",
) -> OperationResponse[
"capo_sagemaker.types.create_monitoring_schedule_response.CreateMonitoringScheduleResponse"
]:
import capo_sagemaker._operations.sage_maker.create_monitoring_schedule
output, http_response = (
capo_sagemaker._operations.sage_maker.create_monitoring_schedule.create_monitoring_schedule(
req.options, req.input
)
)
return OperationResponse(output=output, response=http_response)
interceptors_, options_ = self.operation_options(
config_overrides, OperationLogging("CreateMonitoringSchedule")
)
input_: capo_sagemaker.types.create_monitoring_schedule_request.CreateMonitoringScheduleRequest = {}
if monitoring_schedule_name is not None:
input_["monitoring_schedule_name"] = monitoring_schedule_name
if monitoring_schedule_config is not None:
input_["monitoring_schedule_config"] = monitoring_schedule_config
if tags is not None:
input_["tags"] = tags
response = execute_pipeline(
OperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
response.response.close()
return response.output
|