cancel_trained_model `async` ¶

cancel_trained_model(
    membership_identifier: UUID,
    trained_model_arn: TrainedModelArn,
    *,
    config_overrides: Optional[AsyncCleanRoomsMLClientConfig] = None,
    version_identifier: Optional[UUID] = None,
) -> None

Submits a request to cancel the trained model job.

Parameters:

membership_identifier (UUID) –

The membership ID of the trained model job that you want to cancel.
trained_model_arn (TrainedModelArn) –

The Amazon Resource Name (ARN) of the trained model job that you want to cancel.
version_identifier (Optional[UUID], default: None ) –

The version identifier of the trained model to cancel. This parameter allows you to specify which version of the trained model you want to cancel when multiple versions exist.

If versionIdentifier is not specified, the base model will be cancelled.

Raises:

AccessDeniedException –

You do not have sufficient access to perform this action.
ConflictException –

You can't complete this action because another resource depends on this resource.
ResourceNotFoundException –

The resource you are requesting does not exist.
ThrottlingException –

The request was denied due to request throttling.
ValidationException –

The request parameters for this request are incorrect.
UnknownServiceError –

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

Source code in docs_src/capo_cleanroomsml/_services/async_clean_rooms_ml.py

async def cancel_trained_model(
    self,
    membership_identifier: "capo_cleanroomsml.types.uuid.UUID",
    trained_model_arn: "capo_cleanroomsml.types.trained_model_arn.TrainedModelArn",
    *,
    config_overrides: Optional[AsyncCleanRoomsMLClientConfig] = None,
    version_identifier: Optional["capo_cleanroomsml.types.uuid.UUID"] = None,
) -> None:
    """<p>Submits a request to cancel the trained model job.</p>

    Args:
        membership_identifier: <p>The membership ID of the trained model job that you want to cancel.</p>
        trained_model_arn: <p>The Amazon Resource Name (ARN) of the trained model job that you want to cancel.</p>
        version_identifier: <p>The version identifier of the trained model to cancel. This parameter allows you to specify which version of the trained model you want to cancel when multiple versions exist.</p> <p>If <code>versionIdentifier</code> is not specified, the base model will be cancelled.</p>

    Raises:
        capo_cleanroomsml.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_cleanroomsml.errors.conflict_exception.ConflictException: <p>You can't complete this action because another resource depends on this resource.</p>
        capo_cleanroomsml.errors.resource_not_found_exception.ResourceNotFoundException: <p>The resource you are requesting does not exist.</p>
        capo_cleanroomsml.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
        capo_cleanroomsml.errors.validation_exception.ValidationException: <p>The request parameters for this request are incorrect.</p>
        capo_cleanroomsml.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_cleanroomsml.types.cancel_trained_model_request.CancelTrainedModelRequest]",
    ) -> AsyncOperationResponse[None]:
        import capo_cleanroomsml._operations.aws_stark_control_service.cancel_trained_model

        (
            output,
            http_response,
        ) = await capo_cleanroomsml._operations.aws_stark_control_service.cancel_trained_model.async_cancel_trained_model(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CancelTrainedModel")
    )
    input_: capo_cleanroomsml.types.cancel_trained_model_request.CancelTrainedModelRequest = {
        "membership_identifier": membership_identifier,
        "trained_model_arn": trained_model_arn,
    }
    if version_identifier is not None:
        input_["version_identifier"] = version_identifier

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

cancel_trained_model async ¶

cancel_trained_model `async` ¶