CfnMlflowTrackingServerProps

class aws_cdk.aws_sagemaker.CfnMlflowTrackingServerProps(*, artifact_store_uri, role_arn, tracking_server_name, automatic_model_registration=None, mlflow_version=None, tags=None, tracking_server_size=None, weekly_maintenance_window_start=None)

Bases: object

Properties for defining a CfnMlflowTrackingServer.

Parameters:

artifact_store_uri (str) – The Amazon S3 URI for MLFlow Tracking Server artifacts.
role_arn (str) – The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on behalf of the customer.
tracking_server_name (str) – The name of the MLFlow Tracking Server.
automatic_model_registration (Union[bool, IResolvable, None]) – A flag to enable Automatic SageMaker Model Registration.
mlflow_version (Optional[str]) – The MLFlow Version used on the MLFlow Tracking Server.
tags (Optional[Sequence[Union[CfnTag, Dict[str, Any]]]]) – An array of key-value pairs to apply to this resource.
tracking_server_size (Optional[str]) – The size of the MLFlow Tracking Server.
weekly_maintenance_window_start (Optional[str]) – The start of the time window for maintenance of the MLFlow Tracking Server in UTC time.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-mlflowtrackingserver.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_sagemaker as sagemaker

cfn_mlflow_tracking_server_props = sagemaker.CfnMlflowTrackingServerProps(
    artifact_store_uri="artifactStoreUri",
    role_arn="roleArn",
    tracking_server_name="trackingServerName",

    # the properties below are optional
    automatic_model_registration=False,
    mlflow_version="mlflowVersion",
    tags=[CfnTag(
        key="key",
        value="value"
    )],
    tracking_server_size="trackingServerSize",
    weekly_maintenance_window_start="weeklyMaintenanceWindowStart"
)