SageMaker / Client / update_mlflow_tracking_server

update_mlflow_tracking_server#

SageMaker.Client.update_mlflow_tracking_server(**kwargs)#

Updates properties of an existing MLflow Tracking Server.

See also: AWS API Documentation

Request Syntax

response = client.update_mlflow_tracking_server(
    TrackingServerName='string',
    ArtifactStoreUri='string',
    TrackingServerSize='Small'|'Medium'|'Large',
    AutomaticModelRegistration=True|False,
    WeeklyMaintenanceWindowStart='string'
)
Parameters:

  • TrackingServerName (string) –

    [REQUIRED]

    The name of the MLflow Tracking Server to update.

  • ArtifactStoreUri (string) – The new S3 URI for the general purpose bucket to use as the artifact store for the MLflow Tracking Server.

  • TrackingServerSize (string) – The new size for the MLflow Tracking Server.

  • AutomaticModelRegistration (boolean) – 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 True. To disable automatic model registration, set this value to False. If not specified, AutomaticModelRegistration defaults to False

  • WeeklyMaintenanceWindowStart (string) – 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.

Return type:

dict

Returns:

Response Syntax

{
    'TrackingServerArn': 'string'
}

Response Structure

  • (dict) –

    • TrackingServerArn (string) –

      The ARN of the updated MLflow Tracking Server.

Exceptions

  • SageMaker.Client.exceptions.ResourceNotFound

  • SageMaker.Client.exceptions.ResourceLimitExceeded

  • SageMaker.Client.exceptions.ConflictException