CreateMlflowTrackingServerRequest ¶

class CreateMlflowTrackingServerRequest(TypedDict, closed=True):
    tracking_server_name: NotRequired[
        "capo_sagemaker.types.tracking_server_name.TrackingServerName"
    ]
    """<p>A unique string identifying the tracking server name. This string is part of the tracking server ARN.</p>"""
    artifact_store_uri: NotRequired["capo_sagemaker.types.s3_uri.S3Uri"]
    """<p>The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.</p>"""
    tracking_server_size: NotRequired[
        "capo_sagemaker.types.tracking_server_size.TrackingServerSize"
    ]
    """<p>The size of the tracking server you want to create. You can choose between <code>"Small"</code>, <code>"Medium"</code>, and <code>"Large"</code>. The default MLflow Tracking Server configuration size is <code>"Small"</code>. You can choose a size depending on the projected use of the tracking server such as the volume of data logged, number of users, and frequency of use. </p> <p>We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users. </p>"""
    mlflow_version: NotRequired["capo_sagemaker.types.mlflow_version.MlflowVersion"]
    """<p>The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow.html#mlflow-create-tracking-server-how-it-works">How it works</a>.</p>"""
    role_arn: NotRequired["capo_sagemaker.types.role_arn.RoleArn"]
    """<p>The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the artifact store in Amazon S3. The role should have <code>AmazonS3FullAccess</code> permissions. For more information on IAM permissions for tracking server creation, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server-iam.html">Set up IAM permissions for MLflow</a>.</p>"""
    automatic_model_registration: NotRequired["capo_sagemaker.types.boolean.Boolean"]
    """<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: NotRequired[
        "capo_sagemaker.types.weekly_maintenance_window_start.WeeklyMaintenanceWindowStart"
    ]
    """<p>The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.</p>"""
    tags: NotRequired["capo_sagemaker.types.tag_list.TagList"]
    """<p>Tags consisting of key-value pairs used to manage metadata for the tracking server.</p>"""
    s3_bucket_owner_account_id: NotRequired["capo_sagemaker.types.account_id.AccountId"]
    """<p>Expected Amazon Web Services account ID that owns the Amazon S3 bucket for artifact storage. Defaults to caller's account ID if not provided.</p>"""
    s3_bucket_owner_verification: NotRequired["capo_sagemaker.types.boolean.Boolean"]
    """<p>Enable Amazon S3 Ownership checks when interacting with Amazon S3 buckets from a SageMaker Managed MLflow Tracking Server. Defaults to <code>True</code> if not provided. </p>"""