create_mlflow_tracking_server ¶

def create_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,
    mlflow_version: Optional[
        "capo_sagemaker.types.mlflow_version.MlflowVersion"
    ] = None,
    role_arn: Optional["capo_sagemaker.types.role_arn.RoleArn"] = 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,
    tags: Optional["capo_sagemaker.types.tag_list.TagList"] = 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.create_mlflow_tracking_server_response.CreateMlflowTrackingServerResponse":
    """<p>Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server.html">Create an MLflow Tracking Server</a>.</p>

    Args:
        tracking_server_name: <p>A unique string identifying the tracking server name. This string is part of the tracking server ARN.</p>
        artifact_store_uri: <p>The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.</p>
        tracking_server_size: <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: <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: <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: <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 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: <p>Tags consisting of key-value pairs used to manage metadata for the tracking server.</p>
        s3_bucket_owner_account_id: <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: <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>

    Raises:
        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.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.create_mlflow_tracking_server_request.CreateMlflowTrackingServerRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.create_mlflow_tracking_server_response.CreateMlflowTrackingServerResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.create_mlflow_tracking_server

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateMlflowTrackingServer")
    )
    input_: capo_sagemaker.types.create_mlflow_tracking_server_request.CreateMlflowTrackingServerRequest = {}
    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 mlflow_version is not None:
        input_["mlflow_version"] = mlflow_version
    if role_arn is not None:
        input_["role_arn"] = role_arn
    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 tags is not None:
        input_["tags"] = tags
    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