hops-ui-navigation

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name: hops-ui-navigation description: Use when the user asks where something lives in the Hopsworks UI or how to reach a page (Data Sources, Feature Groups, Feature Views, Model Registry, Deployments, Apps, Agents, Jobs, Jupyter, Project Settings). Knowledge skill: the project-scoped sidebar layout and what sits under each section, so you can point users to the right place.

Hopsworks UI — Navigation Map

A knowledge skill: it describes where things live in the Hopsworks web UI so you can guide a user to the right page. It is not a workflow. For creating or operating resources, use the matching action skill (links at the bottom).

The UI is project-scoped: pick a project, then everything below is a left sidebar inside that project. The sidebar is grouped into section headers (AI/ML, Agents, Compute, Analytics, Configuration) with pages under each.

Source of truth

This map is derived by hand from the frontend, not maintained independently. When routes or sidebar entries change, re-derive it from:

• hopsworks-front/src/layouts/app/navigation/useAppNavigation.tsx — the sidebar tree, order, and section headers.
• hopsworks-front/src/routes/routeNames.ts — the route paths behind each entry.
• hopsworks-front/src/modules/feature-group/hooks/useTabsNavigation.tsx and src/modules/deployments/index.tsx — titles for the dynamic Feature Groups / Deployments entries.

Sidebar layout (top to bottom)

• Home — project landing page.

• Files — HopsFS file browser (datasets, Resources/, Models/, Jupyter/). Opens at the user's own folder.

• Data Sources — storage connectors (JDBC, S3, Snowflake, BigQuery, Redshift, GCS, ADLS, …). Create / edit / preview connectors here.

• Feature Groups — list and inspect feature groups; drill into a group for its schema, data preview, statistics, and provenance.

• AI/ML (section)

  • Feature Views — list feature views; a selected view exposes its Feature List, Training Data (Statistics, Correlations, New Training Data), Feature Logging, Provenance, Tags, API, and Query.
  • Model Registry — registered models and versions; a version shows Metrics, Evaluation, Model Card, Schema, Tags, Code Snippets (VLLM Config for LLM models).
  • Model Deployments — KServe model-serving endpoints; status, logs, predict, metrics.
  • Apps — Python / Streamlit app deployments and their URLs.

• Agents (section)

  • Agent Deployments — served interactive agents (server-only KServe deployments).
  • Agent Jobs — fire-and-forget agent executions; Overview and Executions.

• Compute (section)

  • Environment — Python environments (clone a base, install requirements/wheels).
  • Jobs — jobs and executions (Job Details, Configuration, Schedule, Scheduler, Alerts). Shown as Ingestions for service users.
  • Airflow — Airflow UI (only when airflow_enabled).
  • Jupyter — JupyterLab servers (hidden/disabled when Jupyter is off).

• Analytics (section, only when Trino or Superset is enabled)

  • Queries — Trino SQL query engine (history, runner).
  • Superset — opens the external Superset dashboard in a new tab.

• Configuration (section)

  • Project Settings — General, Integrations, Compute Configuration, Git, Kubernetes Scheduler, Governance Policies, OnlineFS Metrics, IAM Role Chaining, Kafka, Alerts.

Conditional visibility

Some entries depend on edition, role, or service flags, so a user may not see all of them:

• Enterprise-only — Integrations, Compute Configuration, Kubernetes Scheduler, Governance Policies, OnlineFS Metrics, IAM Role Chaining (and Provenance / Tags on a feature view). A non-enterprise user sees these greyed out or hidden.
• Service users see Ingestions instead of Jobs, and have several settings disabled.
• Feature flags — Airflow (airflow_enabled), Jupyter (enable_terminal / enablement), Feature Monitoring, Trino, Superset gate their entries.

Where do I find…?

User asks	Sidebar path
A storage connector / external source	Data Sources
A feature group's schema or data	Feature Groups → (group)
Training data, statistics, correlations	AI/ML → Feature Views → (view) → Training Data
A registered model	AI/ML → Model Registry
A serving endpoint / predict	AI/ML → Model Deployments
A Python app URL	AI/ML → Apps
A served agent / agent job	Agents → Agent Deployments / Agent Jobs
Python env / install a package	Compute → Environment
Run / schedule a job, see executions	Compute → Jobs
Notebooks	Compute → Jupyter
Trino SQL / Superset dashboard	Analytics → Queries / Superset
Git, Kafka, alerts, project config	Configuration → Project Settings

Full docs: https://docs.hopsworks.ai

Related skills and commands

• Discover resources without the UI: hops-data-discovery (hops fg list, hops fv list, hops search ls).
• Operate the resource behind a page: hops-fg, hops-fv, hops-train, hops-online-inference, hops-app, hops-agent-deployment, hops-job, hops-environments, hops-superset, hops-data-sources.
• A quick UI-equivalent snapshot from the CLI: hops context.