# *************************************************************************************** # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. * # * # Permission is hereby granted, free of charge, to any person obtaining a copy of this * # software and associated documentation files (the "Software"), to deal in the Software * # without restriction, including without limitation the rights to use, copy, modify, * # merge, publish, distribute, sublicense, and/or sell copies of the Software, and to * # permit persons to whom the Software is furnished to do so. * # * # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, * # INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A * # PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT * # HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION * # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE * # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. * # *************************************************************************************** from typing import List, Dict from aws_cdk import ( core ) from custom_constructs.ml_pipeline_construct import MLPipelineConstruct class MachineLearningPipelineStack(core.Stack): def __init__(self, scope: core.Construct, id: str, *, repo_type: str, train_stage_type: str, deploy_stage_type: str = None, environments: List[Dict[str, str]] = None, **kwargs) -> None: super().__init__(scope, id, **kwargs) MLPipelineConstruct(self, id="FDPipeline", repo_type=repo_type, train_stage_type=train_stage_type, deploy_stage_type=deploy_stage_type, envs=environments)