RHEL AI Community: Resources, Support, and Getting Involved

📘 Book Reference: This article is based on Chapter 9: Community and Support of Practical RHEL AI, providing a comprehensive guide to navigating the RHEL AI ecosystem.

Introduction

Success with RHEL AI extends beyond technical implementation. Chapter 9 of Practical RHEL AI covers the vibrant ecosystem surrounding the platform, from official Red Hat support to community-driven innovation through InstructLab.

Official Red Hat Resources

Documentation Portal

The primary source for RHEL AI documentation:

Support Channels

Enterprise support options for RHEL AI:

flowchart TB
    subgraph Support["Red Hat Support Tiers"]
        Premium["Premium<br/>24x7 support, 1-hour critical response"]
        Standard["Standard<br/>Business hours, 4-hour response"]
        Self["Self-Support<br/>Documentation and knowledge base"]
    end

Opening Support Cases

# Using Red Hat Support Tool
redhat-support-tool addcase \
  --product "Red Hat Enterprise Linux AI" \
  --version "1.0" \
  --summary "Issue with InstructLab training" \
  --description "Training fails at synthetic data generation step"

InstructLab Community

What is InstructLab?

InstructLab is the open-source project underlying RHEL AI’s model fine-tuning capabilities. Contributing to InstructLab benefits the entire community.

Getting Started with Contributions

# Clone the InstructLab repository
git clone https://github.com/instructlab/instructlab.git
cd instructlab

# Set up development environment
python -m venv venv
source venv/bin/activate
pip install -e ".[dev]"

# Run tests
pytest tests/

Taxonomy Contributions

The taxonomy repository powers InstructLab’s skill definitions:

# Example contribution: taxonomy/knowledge/technology/cloud/qna.yaml
created_by: your-github-username
version: 1
seed_examples:
  - context: |
      Information about cloud-native AI deployment patterns
    question: "What are best practices for deploying AI on Kubernetes?"
    answer: |
      Best practices include:
      1. Use GPU node pools with appropriate taints
      2. Implement resource quotas for training jobs
      3. Use persistent volumes for model artifacts
      4. Configure horizontal pod autoscaling for inference

Contribution Workflow

1. Fork Repository
       │
       ▼
2. Create Branch (feature/your-feature)
       │
       ▼
3. Add/Modify Taxonomy Files
       │
       ▼
4. Run Local Validation
       │
       ▼
5. Submit Pull Request
       │
       ▼
6. Community Review
       │
       ▼
7. Merge & Celebrate 🎉

Community Forums and Discussion

Official Channels

Best Practices for Getting Help

## Good Question Template

**Environment:**
- RHEL Version: 9.3
- RHEL AI Version: 1.0
- GPU: NVIDIA A100 80GB
- Python: 3.11

**What I'm trying to do:**
Fine-tune Granite 3B on custom taxonomy

**What I've tried:**
1. Followed documentation at [link]
2. Ran `ilab generate` with config...

**Error message:**

[paste relevant error]

**Expected behavior:**
Synthetic data generation should complete

Training and Certification

Learning Paths

Red Hat offers structured learning for RHEL AI:

1. DO007 - Ansible Basics for AI Automation
2. AI100 - Introduction to RHEL AI
3. AI200 - Advanced InstructLab Techniques
4. AI300 - Production AI Deployment

Self-Paced Resources

learning_resources:
  - name: "RHEL AI Quick Start"
    type: "Tutorial"
    duration: "2 hours"
    url: "developers.redhat.com/rhel-ai-quickstart"
  
  - name: "InstructLab Workshop"
    type: "Hands-on Lab"
    duration: "4 hours"
    url: "github.com/instructlab/workshops"
  
  - name: "Practical RHEL AI Book"
    type: "Comprehensive Guide"
    duration: "Self-paced"
    url: "lucaberton.com/books"

Open Source Ecosystem

Related Projects

RHEL AI builds on several open-source foundations:

Upstream First Philosophy

Red Hat’s approach ensures community contributions flow upstream:

flowchart TB
    Community["Community<br/>(Upstream)"] -->|Contributions| Community
    Community -->|Downstream| Fedora["Fedora AI"]
    Fedora -->|Enterprise| RHEL["RHEL AI"]

Enterprise Integration

Partner Ecosystem

RHEL AI integrates with leading enterprise platforms:

integrations:
  cloud_providers:
    - AWS (EC2 P4d, P5)
    - Azure (NC/ND Series)
    - GCP (A2/A3 Instances)
    - IBM Cloud
  
  orchestration:
    - OpenShift AI
    - Kubernetes
    - Ansible Automation Platform
  
  observability:
    - Datadog
    - Grafana Cloud
    - Splunk

ISV Certifications

Hardware and software certifications for RHEL AI:

Contributing Back

Ways to Contribute

1. Code Contributions - Fix bugs, add features
2. Documentation - Improve guides and tutorials
3. Taxonomy - Add domain-specific skills
4. Testing - Report bugs, validate fixes
5. Community Support - Help others in forums

Recognition Programs

Active contributors may be recognized through:

• GitHub contributor badges
• Community spotlight features
• Red Hat Summit speaker opportunities
• Early access to new features

Getting Started Checklist

□ Register at access.redhat.com
□ Join InstructLab Discord
□ Fork InstructLab repository
□ Complete AI100 training module
□ Run your first fine-tuning job
□ Submit first taxonomy contribution
□ Read Practical RHEL AI book

Related Book Content

This article covers material from:

• Chapter 9: Community and Support - All resources and channels
• Chapter 1: Introduction - RHEL AI ecosystem overview
• Chapter 5: Custom Applications - Practical contribution examples

📚 Join the RHEL AI Community

Ready to accelerate your RHEL AI journey?

Practical RHEL AI is your complete guide:

• ✅ Step-by-step tutorials for all skill levels
• ✅ Production-ready code examples
• ✅ Troubleshooting guides for common issues
• ✅ Best practices from Red Hat experts
• ✅ Community contribution guidelines