create_retraining_scheduler async ¶

async def create_retraining_scheduler(
    self,
    model_name: "capo_lookoutequipment.types.model_name.ModelName",
    retraining_frequency: "capo_lookoutequipment.types.retraining_frequency.RetrainingFrequency",
    lookback_window: "capo_lookoutequipment.types.lookback_window.LookbackWindow",
    client_token: "capo_lookoutequipment.types.idempotence_token.IdempotenceToken",
    *,
    config_overrides: Optional[AsyncLookoutEquipmentClientConfig] = None,
    retraining_start_date: Optional[
        "capo_lookoutequipment.types.timestamp.Timestamp"
    ] = None,
    promote_mode: Optional[
        "capo_lookoutequipment.types.model_promote_mode.ModelPromoteMode"
    ] = None,
) -> "capo_lookoutequipment.types.create_retraining_scheduler_response.CreateRetrainingSchedulerResponse":
    """<p>Creates a retraining scheduler on the specified model. </p>

    Args:
        model_name: <p>The name of the model to add the retraining scheduler to. </p>
        retraining_start_date: <p>The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.</p>
        retraining_frequency: <p>This parameter uses the <a href="https://en.wikipedia.org/wiki/ISO_8601#Durations">ISO 8601</a> standard to set the frequency at which you want retraining to occur in terms of Years, Months, and/or Days (note: other parameters like Time are not currently supported). The minimum value is 30 days (P30D) and the maximum value is 1 year (P1Y). For example, the following values are valid:</p> <ul> <li> <p>P3M15D – Every 3 months and 15 days</p> </li> <li> <p>P2M – Every 2 months</p> </li> <li> <p>P150D – Every 150 days</p> </li> </ul>
        lookback_window: <p>The number of past days of data that will be used for retraining.</p>
        promote_mode: <p>Indicates how the service will use new models. In <code>MANAGED</code> mode, new models will automatically be used for inference if they have better performance than the current model. In <code>MANUAL</code> mode, the new models will not be used <a href="https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/versioning-model.html#model-activation">until they are manually activated</a>.</p>
        client_token: <p>A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one. </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.conflict_exception.ConflictException: <p> The request could not be completed due to a conflict with the current state of the target 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:
        Creates a retraining scheduler with a specific start date

        >>> await client.create_retraining_scheduler(model_name='sample-model', retraining_start_date='2024-01-01T00:00:00Z', retraining_frequency='P1M', lookback_window='P360D', client_token='sample-client-token')
        Creates a retraining scheduler with manual promote mode

        >>> await client.create_retraining_scheduler(model_name='sample-model', retraining_frequency='P1M', lookback_window='P360D', promote_mode='MANUAL', client_token='sample-client-token')
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_lookoutequipment.types.create_retraining_scheduler_request.CreateRetrainingSchedulerRequest]",
    ) -> AsyncOperationResponse[
        "capo_lookoutequipment.types.create_retraining_scheduler_response.CreateRetrainingSchedulerResponse"
    ]:
        import capo_lookoutequipment._operations.aws_lookout_equipment_frontend_service.create_retraining_scheduler

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateRetrainingScheduler")
    )
    input_: capo_lookoutequipment.types.create_retraining_scheduler_request.CreateRetrainingSchedulerRequest = {
        "model_name": model_name,
        "retraining_frequency": retraining_frequency,
        "lookback_window": lookback_window,
        "client_token": client_token,
    }
    if retraining_start_date is not None:
        input_["retraining_start_date"] = retraining_start_date
    if promote_mode is not None:
        input_["promote_mode"] = promote_mode

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