Docker Mastery Labs

A comprehensive collection of hands-on labs designed to master Docker, Kubernetes, and related DevOps technologies. Each lab is a self-contained mini-project with clear objectives, specific constraints, and validation criteria. Progress from beginner to production expert through practical, real-world scenarios. This readme is generated by Copilot. The 5 first labs too.

Labs Overview

Beginner Level (Labs 1-5)

Build foundational Docker knowledge with practical, hands-on projects.

Lab	Title	Concepts	Status
01	Multi-stage Node.js Build	Multi-stage builds, optimization, layer caching	Not Started
02	Python Environment & Secrets	Environment variables, secrets management, configuration	Not Started
03	PostgreSQL & Volumes	Data persistence, volumes, backup/restore	Not Started
04	Network Isolation	Docker networking, service isolation, DNS	Not Started
05	MERN Stack with Compose	Multi-container orchestration, full-stack apps	Not Started

Rest of the Labs

The rest of the labs have random level attributions. The goal being to cover most of the most trending Devops subjets through labs. Topics being in general, production deployments, CI/CD, and DevOps integration, enterprise orchestration and cloud-native architectures.

Progress Tracking

Track your progress by updating the status column in your fork:

• Not Started
• In Progress
• Completed
• Completed + Bonus Challenges

Repository Structure

docker-mastery-labs/
├── README.md (this file - index of all labs)
├── docs/
│   ├── learning-path.md (created by Copilot. Not functional at ALL)
│   └── resources.md (created by Copilot but pretty solld roadmap)
├── lab-01-multistage-node/
│   ├── README.md (lab instructions)
│   ├── app/ (your work directory)
│   └── solution/ (reference solution)
├── lab-02-python-env-secrets/
│   ├── README.md
│   └── app/
├── lab-03-postgres-volumes/
│   ├── README.md
│   └── data/
├── lab-04-network-isolation/
│   ├── README.md
│   └── services/
└── lab-05-mern-compose/
    ├── README.md
    └── stack/

Automated Lab Generation Pipeline

This repository utilizes an automated pipeline to continuously generate new hands-on labs using AI technology and web scraping.

How Labs Are Created

The lab generation process follows a multi-stage pipeline:

1. Content Discovery

The system scrapes trending DevOps content from multiple authoritative sources:

• Dev.to RSS feeds
• GitHub Trending repositories
• CNCF Blog
• Reddit communities (r/devops, r/kubernetes, r/docker, r/ansible)
• Hacker News (filtered for DevOps topics)
• Medium DevOps tags

2. Topic Selection

From the scraped content, the system:

• Extracts relevant topics and technologies
• Identifies key concepts and learning opportunities
• Selects topics that align with the repository's learning progression
• Ensures diversity across Docker, Kubernetes, Helm, ArgoCD, and Ansible

3. AI-Powered Generation

Using Google Gemini AI, the system generates complete lab structures including:

• Comprehensive objectives and learning goals
• Step-by-step task descriptions
• Validation criteria and verification commands
• Hints and bonus challenges
• Relevant documentation links
• Difficulty assessment (beginner, intermediate, advanced, expert)

4. Lab Creation

The file creator component:

• Generates the lab directory structure
• Creates README.md with complete instructions
• Sets up starter files and solution directories
• Assigns sequential lab numbers
• Organizes labs by difficulty level

GitHub Actions Workflow

The lab generation is automated through GitHub Actions:

Schedule:

• Runs automatically at intervals of 3 or 5 hours (runs at 00:00, 03:00, 08:00, 11:00, 16:00, 19:00 UTC)
• Switches to daily generation once 50 labs are reached
• Can be manually triggered with technology specification

Process:

1. Checks existing lab count
2. Determines if generation should proceed
3. Scrapes current DevOps content
4. Selects topic and technology
5. Generates lab using AI
6. Creates lab files in repository
7. Commits and pushes new lab

Technologies Supported:

• Docker
• Kubernetes
• Helm
• ArgoCD
• Ansible

Manual Lab Generation

Developers can manually generate labs using the Python scripts:

cd scripts
pip install -r requirements.txt
python lab_generator.py --technology docker

Available options:

• --technology <tech> - Force specific technology
• --skip-scrape - Use fallback topics
• --dry-run - Test without creating files
• --test - Run local tests

See scripts/README.md for detailed documentation.

Contributing

Contributions are welcome. If you find issues or have improvements:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Inspired by real-world Docker use cases and community best practices. Lab content is dynamically generated using Google Gemini AI and curated from trending DevOps topics across the web.

Feedback

If you have questions, suggestions, or feedback:

• Open an issue on GitHub
• Share your learning journey
• Help improve the labs for others