update_mlflow_app ¶

def update_mlflow_app(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    arn: Optional["capo_sagemaker.types.mlflow_app_arn.MlflowAppArn"] = None,
    name: Optional["capo_sagemaker.types.mlflow_app_name.MlflowAppName"] = None,
    artifact_store_uri: Optional["capo_sagemaker.types.s3_uri.S3Uri"] = None,
    model_registration_mode: Optional[
        "capo_sagemaker.types.model_registration_mode.ModelRegistrationMode"
    ] = None,
    weekly_maintenance_window_start: Optional[
        "capo_sagemaker.types.weekly_maintenance_window_start.WeeklyMaintenanceWindowStart"
    ] = None,
    default_domain_id_list: Optional[
        "capo_sagemaker.types.default_domain_id_list.DefaultDomainIdList"
    ] = None,
    account_default_status: Optional[
        "capo_sagemaker.types.account_default_status.AccountDefaultStatus"
    ] = None,
) -> "capo_sagemaker.types.update_mlflow_app_response.UpdateMlflowAppResponse":
    """<p>Updates an MLflow App.</p>

    Args:
        arn: <p>The ARN of the MLflow App to update.</p>
        name: <p>The name of the MLflow App to update.</p>
        artifact_store_uri: <p>The new S3 URI for the general purpose bucket to use as the artifact store for the MLflow App.</p>
        model_registration_mode: <p>Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to <code>AutoModelRegistrationEnabled</code>. To disable automatic model registration, set this value to <code>AutoModelRegistrationDisabled</code>. If not specified, <code>AutomaticModelRegistration</code> defaults to <code>AutoModelRegistrationEnabled</code> </p>
        weekly_maintenance_window_start: <p>The new weekly maintenance window start day and time to update. The maintenance window day and time should be in Coordinated Universal Time (UTC) 24-hour standard time. For example: TUE:03:30.</p>
        default_domain_id_list: <p>List of SageMaker Domain IDs for which this MLflow App is the default.</p>
        account_default_status: <p>Indicates whether this this MLflow App is the default for the account.</p>

    Raises:
        capo_sagemaker.errors.conflict_exception.ConflictException: <p>There was a conflict when you attempted to modify a SageMaker entity such as an <code>Experiment</code> or <code>Artifact</code>.</p>
        capo_sagemaker.errors.resource_not_found.ResourceNotFound: <p>Resource being access is not found.</p>
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.update_mlflow_app_request.UpdateMlflowAppRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.update_mlflow_app_response.UpdateMlflowAppResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.update_mlflow_app

        output, http_response = (
            capo_sagemaker._operations.sage_maker.update_mlflow_app.update_mlflow_app(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("UpdateMlflowApp")
    )
    input_: capo_sagemaker.types.update_mlflow_app_request.UpdateMlflowAppRequest = {}
    if arn is not None:
        input_["arn"] = arn
    if name is not None:
        input_["name"] = name
    if artifact_store_uri is not None:
        input_["artifact_store_uri"] = artifact_store_uri
    if model_registration_mode is not None:
        input_["model_registration_mode"] = model_registration_mode
    if weekly_maintenance_window_start is not None:
        input_["weekly_maintenance_window_start"] = weekly_maintenance_window_start
    if default_domain_id_list is not None:
        input_["default_domain_id_list"] = default_domain_id_list
    if account_default_status is not None:
        input_["account_default_status"] = account_default_status

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output