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About Luca Berton

Luca Berton is an Ansible automation expert, author of "Ansible for VMware by Examples" and "Ansible for Kubernetes by Example" published by Apress, and creator of the Ansible Pilot YouTube channel. He shares practical automation knowledge through tutorials, books, and video courses to help IT professionals and DevOps engineers master infrastructure automation.

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

By Luca Berton · Published 2024-01-01 · Category: installation

How to install and use the redhat.ai Ansible Collection to manage Red Hat AI environments, deploy AI models, and automate MLOps workflows on RHEL AI.

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.

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

Key capabilities: • 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:

Then install:

Using requirements.yml

Key Modules

ilab_init — Initialize InstructLab

Create an initial InstructLab configuration:

ilab_model_download — Download AI Models

Download models from Red Hat's container registry:

Complete Example Playbook

Best Practices Store credentials in Vault — Never hardcode registry credentials Use specific model versions — Pin model releases for reproducibility Test in development — Validate model deployments before production 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.

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