Can I use Kubeflow with SageMaker?
如何將 Kubeflow 與 SageMaker 搭配使用？
Yes. Amazon SageMaker Components for Kubeflow Pipelines are open-source plugins that allow you to use Kubeflow Pipelines to define your ML workflows and use SageMaker for the data labeling, training, and inference steps. Kubeflow Pipelines is an add-on to Kubeflow that lets you build and deploy portable and scalable complete ML pipelines. However, when using Kubeflow Pipelines, ML ops teams need to manage a Kubernetes cluster with CPU and GPU instances and keep its utilization high at all times to reduce operational costs. Maximizing the utilization of a cluster across data science teams is challenging and adds additional operational overhead to the ML ops teams. As an alternative to an ML-optimized Kubernetes cluster, with SageMaker Components for Kubeflow Pipelines you can take advantage of powerful SageMaker features such as data labeling, fully managed large-scale hyperparameter tuning and distributed training jobs, one-click secure and scalable model deployment, and cost-effective training through Amazon Elastic Compute Cloud (Amazon EC2) Spot Instances without needing to configure and manage Kubernetes clusters specifically to run the ML jobs.
是。適用於 Kubeflow Pipeline 的 Amazon SageMaker 元件是開放原始碼外掛程式，可讓您使用 Kubeflow Pipelines 定義 ML 工作流程，以及透過 SageMaker 進行資料標記、訓練和推論步驟。Kubeflow Pipelines 是 Kubeflow 的附加元件，可用來建置和部署可攜式和可擴展的完整 ML 管道。不過，使用 Kubeflow Pipeline 時，ML 營運團隊需要管理含有 CPU 和 GPU 執行個體的 Kubernetes 叢集，並始終維持高使用率以降低營運成本。在資料科學團隊中最大限度地使用叢集是一項挑戰，並會增加 ML 營運團隊的額外營運開銷。作為 ML 優化 Kubernetes 叢集的替代方案，您可以透過適用於 Kubeflow Pipeline 的 SageMaker 元件，利用 SageMaker 的強大功能，例如資料標記、全受管大規模超參數調整和分散式訓練任務、一鍵式安全和可擴展的模型部署，以及使用 Amazon Elastic Compute Cloud (Amazon EC2) Spot 執行個體進行經濟實惠的訓練，無需特別設定和管理 Kubernetes 叢集來執行 ML 任務。