Red Hat OpenShift AI (RHOAI) 3.2 Model Catalog - Operations Guide

From Choice Overload to Confident Deployment

The Problem: Users can find models anywhere. They cannot safely deploy them everywhere.
The Solution: The Model Catalog provides a curated, validated “showroom” with a streamlined deployment wizard and enterprise-ready artifacts (ModelCars).

This repository contains a complete Course-in-a-Box that teaches platform engineers and administrators how to operate the Model Catalog in Red Hat OpenShift AI 3.3: architecture, deployment workflow, source configuration, and known constraints.

📚 Full course documentation: The complete Antora course (architecture, labs, troubleshooting) is in this repository—see Option 1: View the Full Course below to build and view it. Use the course when you need more detail than this runbook.

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📚 Option 1: View the Full Course (Antora)

Using Docker (Recommended)

docker run -u $(id -u) -v $PWD:/antora:Z --rm -t antora/antora antora-playbook.yml
# Open the generated site:
# open build/site/index.html

Using Local NPM

npm install
npx antora antora-playbook.yml
# Open build/site/index.html

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⚡ Option 2: The Fast Track (Admin Runbook)

If you already know the concepts and just need the operational commands, use this runbook.

Prerequisites

• Cluster: OpenShift AI 3.3 installed and accessible
• Access: Permissions to view/edit resources in rhoai-model-registries (or cluster-admin)
• CLI: oc installed and authenticated (oc login)
• Repository (optional): Clone this repo to apply config as code: git clone https://github.com/RedHatQuickCourses/rhoai3-catalog.git && cd rhoai3-catalog

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Step 1: Confirm the Catalog is Visible in the Dashboard

The Model Catalog is enabled by default, but it can be hidden via OdhDashboardConfig.

oc get odhdashboardconfig -n redhat-ods-applications

To enable the catalog menu:

oc patch odhdashboardconfig odh-dashboard-config -n redhat-ods-applications --type merge -p '{
  "spec": { "dashboardConfig": { "disableModelCatalog": false } }
}'

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Step 2: Inspect and Manage Catalog Sources

The catalog reads sources from:

• Namespace: rhoai-model-registries
• ConfigMap: model-catalog-sources

oc get configmap model-catalog-sources -n rhoai-model-registries -o yaml

Managing sources as code: The ConfigMap YAML is not in this repo—you create or obtain it in one of these ways:

• Export from the cluster (if the ConfigMap already exists):
  oc get configmap model-catalog-sources -n rhoai-model-registries -o yaml > model-catalog-sources.yaml
  Edit the file, then apply: oc apply -f model-catalog-sources.yaml
• Use a template for a new setup: The Antora course in this repo and the rhoai3-registry repo (deploy/catalog/catalog-source.yaml) show the required ConfigMap structure and example entries. Create your own file (e.g. model-catalog-sources.yaml) from that, then:

oc apply -f model-catalog-sources.yaml

Force a Reload (Recommended After Changes)

oc delete pod -l component=model-catalog -n rhoai-model-registries
oc get pods -l component=model-catalog -n rhoai-model-registries -w

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Step 3: Deploy from the Catalog (Wizard Guardrails)

When deploying from AI hub → Catalog in RHOAI 3.3:

• Prefer ModelCars: OCI artifacts hosted on registry.redhat.io start faster than raw downloads and match enterprise supply chain patterns.
• Shorten names: keep deployment names under ~30–40 characters to avoid Kubernetes 63-character limits.
• New project storage option: if “Existing cluster storage” is missing, create at least one Data Connection in that project first.

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Troubleshooting

Known Issues (RHOAI 3.2)

1) “Request access to model catalog” after upgrade (2.24 → 3.2)

Fix by forcing a clean refresh:

oc delete configmap model-catalog-sources -n rhoai-model-registries
oc delete deployment model-catalog -n rhoai-model-registries
oc get pods -n rhoai-model-registries -l component=model-catalog -w

2) Silent failure due to name length > 63 characters

Workaround: shorten the name in the wizard (examples: llama-3-8b-int8, mistral-small-24b-awq).

3) Missing storage options in new projects

Workaround: create a Data Connection in the target project, then retry the wizard.

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Repository Structure

/
├── modules/                     # Antora course content (AsciiDoc)
│   ├── ROOT/pages/index.adoc     # Home (includes chapter content)
│   └── chapter1/pages/           # Course pages (intro, architecture, lab, troubleshooting)
│
├── antora.yml                    # Component descriptor
├── antora-playbook.yml           # Antora build playbook
└── README.md                     # This file

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Next Steps

• Treat catalog sources as code (GitOps) and standardize naming conventions.
• Align catalog deployments with Hardware Profiles to prevent “wrong GPU” deployments.
• Prefer ModelCars when available to reduce cold-start time and improve reliability.

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Additional Resources

• Full course: Antora course in this repository (see Option 1: View the Full Course above).
• OpenShift AI: Red Hat OpenShift AI Documentation.