create_mlflow_app ¶

def create_mlflow_app(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    name: Optional["capo_sagemaker.types.mlflow_app_name.MlflowAppName"] = None,
    artifact_store_uri: Optional["capo_sagemaker.types.s3_uri.S3Uri"] = None,
    role_arn: Optional["capo_sagemaker.types.role_arn.RoleArn"] = None,
    kms_key_id: Optional["capo_sagemaker.types.kms_key_id.KmsKeyId"] = None,
    model_registration_mode: Optional[
        "capo_sagemaker.types.model_registration_mode.ModelRegistrationMode"
    ] = None,
    weekly_maintenance_window_start: Optional[
        "capo_sagemaker.types.weekly_maintenance_window_start.WeeklyMaintenanceWindowStart"
    ] = None,
    account_default_status: Optional[
        "capo_sagemaker.types.account_default_status.AccountDefaultStatus"
    ] = None,
    default_domain_id_list: Optional[
        "capo_sagemaker.types.default_domain_id_list.DefaultDomainIdList"
    ] = None,
    tags: Optional["capo_sagemaker.types.tag_list.TagList"] = None,
) -> "capo_sagemaker.types.create_mlflow_app_response.CreateMlflowAppResponse":
    """<p>Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store.</p>

    Args:
        name: <p>A string identifying the MLflow app name. This string is not part of the tracking server ARN.</p>
        artifact_store_uri: <p>The S3 URI for a general purpose bucket to use as the MLflow App artifact store.</p>
        role_arn: <p>The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow App uses to access the artifact store in Amazon S3. The role should have the <code>AmazonS3FullAccess</code> permission.</p>
        kms_key_id: <p>The ID of the Amazon Web Services KMS key used to encrypt the data at rest associated with the MLflow App. If you don't specify a value, the MLflow App is not encrypted with a customer-managed key.</p>
        model_registration_mode: <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>AutoModelRegistrationEnabled</code>. To disable automatic model registration, set this value to <code>AutoModelRegistrationDisabled</code>. If not specified, <code>AutomaticModelRegistration</code> defaults to <code>AutoModelRegistrationDisabled</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>
        account_default_status: <p>Indicates whether this MLflow app is the default for the entire account.</p>
        default_domain_id_list: <p>List of SageMaker domain IDs for which this MLflow App is used as the default.</p>
        tags: <p>Tags consisting of key-value pairs used to manage metadata for the MLflow App.</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_app_request.CreateMlflowAppRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.create_mlflow_app_response.CreateMlflowAppResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.create_mlflow_app

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateMlflowApp")
    )
    input_: capo_sagemaker.types.create_mlflow_app_request.CreateMlflowAppRequest = {}
    if name is not None:
        input_["name"] = name
    if artifact_store_uri is not None:
        input_["artifact_store_uri"] = artifact_store_uri
    if role_arn is not None:
        input_["role_arn"] = role_arn
    if kms_key_id is not None:
        input_["kms_key_id"] = kms_key_id
    if model_registration_mode is not None:
        input_["model_registration_mode"] = model_registration_mode
    if weekly_maintenance_window_start is not None:
        input_["weekly_maintenance_window_start"] = weekly_maintenance_window_start
    if account_default_status is not None:
        input_["account_default_status"] = account_default_status
    if default_domain_id_list is not None:
        input_["default_domain_id_list"] = default_domain_id_list
    if tags is not None:
        input_["tags"] = tags

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