update_mlflow_tracking_server ¶

def update_mlflow_tracking_server(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    tracking_server_name: Optional[
        "capo_sagemaker.types.tracking_server_name.TrackingServerName"
    ] = None,
    artifact_store_uri: Optional["capo_sagemaker.types.s3_uri.S3Uri"] = None,
    tracking_server_size: Optional[
        "capo_sagemaker.types.tracking_server_size.TrackingServerSize"
    ] = None,
    automatic_model_registration: Optional[
        "capo_sagemaker.types.boolean.Boolean"
    ] = None,
    weekly_maintenance_window_start: Optional[
        "capo_sagemaker.types.weekly_maintenance_window_start.WeeklyMaintenanceWindowStart"
    ] = None,
    s3_bucket_owner_account_id: Optional[
        "capo_sagemaker.types.account_id.AccountId"
    ] = None,
    s3_bucket_owner_verification: Optional[
        "capo_sagemaker.types.boolean.Boolean"
    ] = None,
) -> "capo_sagemaker.types.update_mlflow_tracking_server_response.UpdateMlflowTrackingServerResponse":
    """<p>Updates properties of an existing MLflow Tracking Server.</p>

    Args:
        tracking_server_name: <p>The name of the MLflow Tracking Server 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 Tracking Server.</p>
        tracking_server_size: <p>The new size for the MLflow Tracking Server.</p>
        automatic_model_registration: <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>True</code>. To disable automatic model registration, set this value to <code>False</code>. If not specified, <code>AutomaticModelRegistration</code> defaults to <code>False</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>
        s3_bucket_owner_account_id: <p>The new expected Amazon Web Services account ID that owns the Amazon S3 bucket for artifact storage.</p>
        s3_bucket_owner_verification: <p>Whether to enable or disable Amazon S3 Bucket Owenrship Verifaction whenever the MLflow Tracking Server interacts with Amazon Amazon S3.</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_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.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_tracking_server_request.UpdateMlflowTrackingServerRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.update_mlflow_tracking_server_response.UpdateMlflowTrackingServerResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.update_mlflow_tracking_server

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("UpdateMlflowTrackingServer")
    )
    input_: capo_sagemaker.types.update_mlflow_tracking_server_request.UpdateMlflowTrackingServerRequest = {}
    if tracking_server_name is not None:
        input_["tracking_server_name"] = tracking_server_name
    if artifact_store_uri is not None:
        input_["artifact_store_uri"] = artifact_store_uri
    if tracking_server_size is not None:
        input_["tracking_server_size"] = tracking_server_size
    if automatic_model_registration is not None:
        input_["automatic_model_registration"] = automatic_model_registration
    if weekly_maintenance_window_start is not None:
        input_["weekly_maintenance_window_start"] = weekly_maintenance_window_start
    if s3_bucket_owner_account_id is not None:
        input_["s3_bucket_owner_account_id"] = s3_bucket_owner_account_id
    if s3_bucket_owner_verification is not None:
        input_["s3_bucket_owner_verification"] = s3_bucket_owner_verification

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