describe_retraining_scheduler `async` ¶

describe_retraining_scheduler(
    model_name: ModelName,
    *,
    config_overrides: Optional[AsyncLookoutEquipmentClientConfig] = None,
) -> DescribeRetrainingSchedulerResponse

Provides a description of the retraining scheduler, including information such as the model name and retraining parameters.

Parameters:

model_name (ModelName) –

The name of the model that the retraining scheduler is attached to.

Raises:

AccessDeniedException –

The request could not be completed because you do not have access to the resource.
InternalServerException –

Processing of the request has failed because of an unknown error, exception or failure.
ResourceNotFoundException –

The resource requested could not be found. Verify the resource ID and retry your request.
ThrottlingException –

The request was denied due to request throttling.
ValidationException –

The input fails to satisfy constraints specified by Amazon Lookout for Equipment or a related Amazon Web Services service that's being utilized.
UnknownServiceError –

The service returned an error code this client does not model.

Examples:

Describes a retraining scheduler

>>> await client.describe_retraining_scheduler(model_name='sample-model')

Source code in docs_src/capo_lookoutequipment/_services/async_lookout_equipment.py

async def describe_retraining_scheduler(
    self,
    model_name: "capo_lookoutequipment.types.model_name.ModelName",
    *,
    config_overrides: Optional[AsyncLookoutEquipmentClientConfig] = None,
) -> "capo_lookoutequipment.types.describe_retraining_scheduler_response.DescribeRetrainingSchedulerResponse":
    """<p>Provides a description of the retraining scheduler, including information such as the model name and retraining parameters. </p>

    Args:
        model_name: <p>The name of the model that the retraining scheduler is attached to. </p>

    Raises:
        capo_lookoutequipment.errors.access_denied_exception.AccessDeniedException: <p>The request could not be completed because you do not have access to the resource. </p>
        capo_lookoutequipment.errors.internal_server_exception.InternalServerException: <p> Processing of the request has failed because of an unknown error, exception or failure. </p>
        capo_lookoutequipment.errors.resource_not_found_exception.ResourceNotFoundException: <p> The resource requested could not be found. Verify the resource ID and retry your request. </p>
        capo_lookoutequipment.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
        capo_lookoutequipment.errors.validation_exception.ValidationException: <p> The input fails to satisfy constraints specified by Amazon Lookout for Equipment or a related Amazon Web Services service that's being utilized. </p>
        capo_lookoutequipment.errors.UnknownServiceError: The service returned an error code this client does not model.

    Examples:
        Describes a retraining scheduler

        >>> await client.describe_retraining_scheduler(model_name='sample-model')
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_lookoutequipment.types.describe_retraining_scheduler_request.DescribeRetrainingSchedulerRequest]",
    ) -> AsyncOperationResponse[
        "capo_lookoutequipment.types.describe_retraining_scheduler_response.DescribeRetrainingSchedulerResponse"
    ]:
        import capo_lookoutequipment._operations.aws_lookout_equipment_frontend_service.describe_retraining_scheduler

        (
            output,
            http_response,
        ) = await capo_lookoutequipment._operations.aws_lookout_equipment_frontend_service.describe_retraining_scheduler.async_describe_retraining_scheduler(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DescribeRetrainingScheduler")
    )
    input_: capo_lookoutequipment.types.describe_retraining_scheduler_request.DescribeRetrainingSchedulerRequest = {
        "model_name": model_name
    }

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output

describe_retraining_scheduler async ¶

describe_retraining_scheduler `async` ¶