文章目录
整体架构是怎样的，详细描述该系统运行方式和流程
有哪些组件，起到怎样的作用
简单翻译了官网关于组件的文档
Central Dashboard
起到控制面板的作用
Notebook Servers
其实就是整合 Jupyter notebooks
Integrating well with the rest of the infrastructure with respect to authentication and access control.
很kubeflow的访问控制授权等系统整合的很好
Enabling easier notebook sharing across the organization. Users can create notebook containers or pods directly in the cluster, rather than locally on their workstations. Admins can provide standard notebook images for their organization, and set up role-based access control (RBAC), Secrets and Credentials to manage which teams and individuals can access the notebooks.
用户可以在远程启动notebook，而不是本地。管理员可以给他们的组织提供标准的notebook镜像，并设置基于角色的访问控制（RBAC）
Overall, Kubeflow-hosted notebooks are better integrated with other components while providing extensibility for notebook images.
在提供可扩展的notebook镜像时，以kubeflow为宿主的notebook可以与其他组件很好地整合
Metadata
https://github.com/kubeflow/metadata
The goal of the Metadata project is to help Kubeflow users understand and manage their machine learning (ML) workflows by tracking and managing the metadata that the workflows produce.
元数据项目的目标是帮助kf用户理解和掌控机器学习工作流，通过追踪管理工作流产生的源数据的方式
https://github.com/kubeflow/metadata/blob/master/sdk/python/sample/demo.ipynb
Fairing
Kubeflow Fairing is a Python package that makes it easy to train and deploy ML models on Kubeflow. Kubeflow Fairing can also been extended to train or deploy on other platforms.
Katib
Overview of Katib for hyperparameter tuning and neural architecture search
Katib is a Kubernetes-native project for automated machine learning (AutoML). Katib supports hyperparameter tuning, early stopping and neural architecture search (NAS). Learn more about AutoML at fast.ai, Google Cloud, Microsoft Azure or Amazon SageMaker.
- Bayesian optimization
- Tree of Parzen Estimators
- Random Search
- Covariance Matrix Adaptation Evolution Strategy
- Hyperband
- Efficient Neural Architecture Search
- Differentiable Architecture Search