Getting Started with the redhat.ai Ansible Collection for AI Model Management

Introduction

The redhat.ai Ansible Collection provides automation tools for managing Red Hat AI environments, streamlining MLOps workflows, and deploying AI models efficiently on Red Hat Enterprise Linux AI (RHEL AI). This article covers installation, configuration, and practical usage.

See also: Getting Started with Ansible Lightspeed Intelligent Assistant in AAP 2.6

What is the redhat.ai Collection?

The redhat.ai collection is a Red Hat Certified Collection available through Automation Hub. It includes:

• 4 Modules for AI model management
• 1 Role for environment setup
• 2 Plugins for integration

• Initialize InstructLab configurations
• Download AI models from registries
• Manage model serving and inference
• Automate RHEL AI deployments

Requirements

• Ansible 2.16.0 or newer
• Python 3.10 or newer
• Access to Red Hat Automation Hub (for certified content)
• Red Hat Enterprise Linux AI (RHEL AI) target systems

Installation

From Automation Hub

First, configure your ansible.cfg:

[galaxy]
server_list = automation_hub

[galaxy_server.automation_hub]
url=https://cloud.redhat.com/api/automation-hub/
auth_url=https://sso.redhat.com/auth/realms/redhat-external/protocol/openid-connect/token
token=<your-automation-hub-token>

Then install:

ansible-galaxy collection install redhat.ai

Using requirements.yml

---
collections:
  - name: redhat.ai

ansible-galaxy collection install -r requirements.yml

Key Modules

ilab_init — Initialize InstructLab

Create an initial InstructLab configuration:

- name: Create initial ilab config
  redhat.ai.ilab_init:
  register: init_result

- name: Display init result
  ansible.builtin.debug:
    var: init_result

ilab_model_download — Download AI Models

Download models from Red Hat's container registry:

- name: Download Granite model
  redhat.ai.ilab_model_download:
    name: granite-8b-starter-v1
    release: latest
    registry_url: docker://registry.redhat.io
    registry_namespace: rhelai1
    registry_username: "{{ registry_user }}"
    registry_password: "{{ registry_pass }}"

See also: Deploying RHEL AI Infrastructure with the infra.ai Ansible Collection

Complete Example Playbook

---
- name: Deploy AI Model on RHEL AI
  hosts: ai_servers
  become: true
  tasks:
    - name: Initialize InstructLab
      redhat.ai.ilab_init:
      register: init_result

    - name: Download the Granite model
      redhat.ai.ilab_model_download:
        name: granite-8b-starter-v1
        release: latest
        registry_url: docker://registry.redhat.io
        registry_namespace: rhelai1
        registry_username: "{{ vault_registry_user }}"
        registry_password: "{{ vault_registry_pass }}"

    - name: Verify model is available
      ansible.builtin.command:
        cmd: ilab model list
      register: model_list

    - name: Display available models
      ansible.builtin.debug:
        var: model_list.stdout_lines

Best Practices

1. Store credentials in Vault — Never hardcode registry credentials
2. Use specific model versions — Pin model releases for reproducibility
3. Test in development — Validate model deployments before production
4. Monitor resources — AI model serving requires significant compute resources

Conclusion

The redhat.ai collection brings the power of Ansible automation to AI/ML workflows. By automating model deployment and management, teams can accelerate their AI initiatives while maintaining consistency and governance.

For more Ansible tutorials and guides, explore the complete article collection on Ansible Pilot.

See also: New Collections and Integrations in Ansible Automation Platform 2.6

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