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What is the overall architecture, and describe in detail the operation mode and process of the system
What are the components and what are their functions
Simply translated the official website's documentation on components
Central Dashboard
Act as a control panel
Notebook Servers
In fact, it is to integrate Jupyter notebooks
Integrating well with the rest of the infrastructure with respect to authentication and access control.
It kubeflow authorization of access control systems integrate well
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.
Users can start notebooks remotely instead of locally. Administrators can provide their organizations with standard notebook mirroring and set up role-based access control (RBAC)
Overall, Kubeflow-hosted notebooks are better integrated with other components while providing extensibility for notebook images.
When providing scalable image notebook, notebook to kubeflow as a host can be well integrated with other components
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.
Meta data project goal is to help kf users to understand and control the machine learning workflow, by Ways to track and manage the source data generated by the workflow
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.
Secretary
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