Dashboard setup

Dashboard

The dashboard provides a web-based integrated UI that allows users to access assigned Kubeflow resources and features. Through the dashboard, users can view and manage notebook execution, training job management, AutoML (Katib) experiments, pipeline runs, and model deployment status in one place.

Dashboard menu layout

Dashboard menu layout

Menu	Description
Home	Kubeflow dashboard main screen
Notebooks	Create and manage Jupyter Notebook servers
Tensorboards	Create TensorBoard servers and manage log visualization
Volumes	Manage volumes used by notebooks and training jobs
Katib Experiments	Manage Katib-based AutoML experiments
Model Registry	Manage trained model metadata and versions
KServe Endpoints	Manage KServe-based model serving endpoints
TrainJobs	Manage training jobs based on the Kubeflow Training Operator
Pipelines	Kubeflow Pipelines management menu
└ Pipelines	Manage pipeline definitions and uploads
└ Experiments	Manage pipeline experiments
└ Runs	Manage pipeline run history
└ Recurring Runs	Manage recurring pipeline run schedules
└ Artifacts	Manage input and output artifacts generated during pipeline runs
└ Executions	Track pipeline component execution results
Manage Account	Manage user accounts (such as password changes)
Manage Group Users	Manage group namespace users (visible when Admin permissions are granted)

Check namespace

You can check the namespace at the top of the dashboard.

Check namespace

Kubeflow guides and notices

Kubeflow guide links and notices are available on the right side of the dashboard.

info

For detailed instructions, see How to use each menu and the Kubeflow > Central dashboard official documentation.