Red Hat OpenShift AI - GitOps Repository

This repository provides a GitOps-based approach to deploying and managing Red Hat OpenShift AI and its dependencies using Kustomize. It serves as a standardized, repeatable, and automated way to configure the complete OpenShift AI stack.

Table of Contents

• Red Hat OpenShift AI - GitOps Repository
  • Table of Contents
  • Overview
    • How the Structure Works
    • Dependencies
      • Adding New Dependencies
  • Quick Start
    • Prerequisites
    • Basic Installation
  • Installation Methods
    • Install All Dependencies
    • Install Specific Dependencies
    • Install a Subset of Dependencies
  • Usage Guidelines
    • For Administrators
  • Release Strategy

Overview

This repository addresses OpenShift AI dependencies that are treated as administrator-owned resources. It provides a template for deploying these prerequisites in a standardized way, simplifying the administrator's workflow by providing a single source of truth for the entire OpenShift AI stack.

This repository works with both GitOps tools (ArgoCD, Flux, etc.) and oc or kubectl CLI.

How the Structure Works

The repository is designed to be applied in layers, providing flexibility in deployment:

1. Granular Installation: Each dependency or component has its own kustomization.yaml and can be applied independently.
2. Grouped Installation: Top-level folders contain kustomization.yaml files that include all items within them.
3. Composition: Each component is self-contained and includes its required dependencies.

Dependencies

Operator	Purpose	Namespace	Required By	Operators Required
Cert-Manager	Certificate management and TLS provisioning	cert-manager-operator	Model Serving (Kueue, Ray)
Kueue	Job queue for distributed workloads	openshift-kueue-operator	Model Serving (Ray), Trainer	Cert-Manager
Cluster Observability Operator	Cluster observability and monitoring	openshift-cluster-observability-operator	Monitoring
OpenTelemetry Product	OpenTelemetry product	openshift-opentelemetry-operator	Monitoring
Leader Worker Set	Deploy a LWS in OpenShift for distributed inference workflows	openshift-lws-operator	Model Server	Cert-Manager

Adding New Dependencies

To add a new dependency, follow the Contributing guide.

Quick Start

Prerequisites

• OpenShift cluster (version 4.19 or later)
• kubectl or oc CLI installed
• Cluster admin permissions
• Kustomize v5 or later (optional - kubectl has built-in Kustomize support)

Basic Installation

# 1. Clone the repository
git clone https://github.com/davidebianchi/rhoai-gitops.git
cd rhoai-gitops

# 2. Check out the branch matching your OpenShift AI version
git checkout rhoai-3.0

# 3. Install all dependencies
kubectl apply -k dependencies

# 4. Wait for operators to be ready

Installation Methods

Install All Dependencies

# Install all dependencies
kubectl apply -k dependencies

Install Specific Dependencies

kubectl apply -k dependencies/operators/cert-manager
kubectl apply -k dependencies/operators/kueue-operator

Install a Subset of Dependencies

You can modify dependencies/operators/kustomization.yaml to comment out dependencies you don't need. For example, if the Kueue operator is not needed, comment it out like this:

apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization

components:
  - ../../components/operators/cert-manager
  # - ../../components/operators/kueue-operator

If you need the Kueue operator later, uncomment it and apply the changes:

kubectl apply -k dependencies/operators

Usage Guidelines

For Administrators

1. Fork or Clone this repository as a starting point for your organization
2. Select the Branch matching your target OpenShift AI version (e.g., rhoai-3.0)
3. Customize the configurations for your specific environment
4. Test thoroughly in a non-production environment
5. Maintain your fork with updates and customizations
6. Apply using your GitOps tool (ArgoCD, Flux, etc.) or kubectl

Release Strategy

• No Formal Releases: This repository does not have official releases. Users are expected to clone or fork the repository and use it as a basis for their own configurations.
• Branch per OpenShift AI Version: Each version of OpenShift AI has a dedicated branch (e.g., rhoai-3.0, rhoai-3.1) to ensure compatibility.
• Version Selection: Always select the branch that corresponds to your target OpenShift AI version.