LFCS Practice Tool

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        Linux System Administration Practice Tool                
                                                                       
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Interactive Linux System Administration Practice Environment with Docker Containers

A comprehensive CLI-based training tool designed to help you prepare for the Linux Foundation Certified System Administrator (LFCS) exam. Practice real-world Linux administration tasks in isolated Docker containers with automatic validation, progress tracking, and interactive learning modules.

Demo

Watch the tool in action - see how to start a practice session, complete tasks, and get instant validation feedback.

Table of Contents

• Features
• Quick Start
• Installation
• Usage
• Learning Modes
• Documentation
• Requirements
• Contributing
• License

Features

Core Features

• 83+ Practice Scenarios across 5 LFCS exam categories
• Isolated Docker Environments for safe, repeatable practice
• Automatic Validation with detailed feedback on task completion
• Progress Tracking with statistics, streaks, and mastery percentages
• Multi-Distribution Support (Ubuntu, CentOS, Rocky Linux)
• Interactive Learning Mode with 14 structured learning modules
• Live Validation - check your work without exiting the container
• Difficulty Progression from easy to hard scenarios
• Smart Recommendations based on your performance

Advanced Features

• Context Generation - scenarios with randomized values for variety
• Property-Based Testing - comprehensive test coverage
• Error Recovery - intelligent error handling with recovery suggestions
• Comprehensive Logging - detailed logs for debugging
• Configuration Management - flexible YAML and environment variable configuration
• Achievement System - track milestones and accomplishments

Quick Start

Choose your installation method based on your needs:

Method 1: Simple Installation (Recommended for Users)

# 1. Install from PyPI
pip install lfcs

# 2. Start practicing (Docker images build automatically on first use)
lfcs start

Note: Docker images are built automatically when you first use the tool. The first run will take 5-20 minutes to build images, but subsequent runs will be instant.

Method 2: Full Repository Installation (For Contributors)

# 1. Clone repository
git clone https://github.com/loyality7/lfcs-practice-tool.git
cd lfcs-practice-tool

# 2. Install in editable mode
pip install -e ".[dev,ai]"

# 3. Build Docker images (optional - will auto-build if skipped)
cd docker/base_images
./build_all.sh
cd ../..

# 4. Start practicing
lfcs start

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Installation

Prerequisites

Before installing, ensure you have:

• Python 3.9+ - Check with python3 --version
• Docker 20.10+ - Check with docker --version
• 4GB RAM minimum (8GB recommended)
• 10GB disk space for Docker images
• Internet connection for downloading packages and images

Installation Method 1: Simple Pip Install (Recommended)

Best for: Users who just want to practice LFCS scenarios

What you get: Full LFCS practice tool with all 83+ scenarios, 14 learning modules, and automatic Docker image building

Steps:

1. Install the package:

   pip install lfcs

2. Start practicing:

   lfcs start

That's it! Docker images will be built automatically on first use.

Important Notes:

• ✅ Automatic setup - Docker images build automatically when first needed
• ⏱️ First run takes 5-20 minutes - Building images (one-time setup)
• 🚀 Subsequent runs are instant - Images are reused
• 📦 Everything included - Scenarios, learning modules, and Dockerfiles in pip package

Installation Method 2: Repository Install (For Contributors)

Best for: Developers who want to contribute or modify the code

What you get: Full source code with editable installation

Steps:

1. Clone repository:

   git clone https://github.com/loyality7/lfcs-practice-tool.git
   cd lfcs-practice-tool

2. Install in editable mode:

   # Install with development dependencies
   pip install -e ".[dev,ai]"

3. Build Docker images:

   cd docker/base_images
   ./build_all.sh
   cd ../..

4. Verify installation:

   lfcs --version
   lfcs start

Optional: Enable AI Features

To use AI-powered hints and validation:

# Set your API key (choose one)
export ANTHROPIC_API_KEY="your-api-key-here"
# OR
export OPENAI_API_KEY="your-api-key-here"

# Start with AI mode
lfcs start --ai

Usage

Basic Commands

# Start interactive practice session
lfcs start

# Start with filters
lfcs start --category networking --difficulty easy

# List available scenarios
lfcs list

# View your statistics
lfcs stats

# Interactive learning mode
lfcs learn

# Reset progress
lfcs reset --confirm

Practice Workflow

1. Select Scenario: Choose category, difficulty, and specific scenario
2. Read Task: Review the task description and hints
3. Work in Container: Complete the task in the Docker shell
4. Validate: Type exit to validate your work
5. Review Results: See detailed feedback and score
6. Track Progress: View statistics and recommendations

Interactive Learning Mode

# Start learning mode
lfcs learn

# List all modules
lfcs learn --list

# Start specific module
lfcs learn --module 01_beginner/01_linux_basics

# Continue from last lesson
lfcs learn --continue

Learning Modes

1. Practice Mode (Scenario-Based)

Practice specific LFCS exam tasks with automatic validation:

• Essential Commands: Text processing, pipes, archives
• Operations & Deployment: Services, packages, systemd
• Networking: Interface configuration, routing, firewall
• Storage: Filesystems, LVM, mounting, permissions
• Users & Groups: User management, permissions, sudo

2. Interactive Learning Mode

Structured learning path from basics to LFCS level:

• Beginner: Linux basics, file navigation, basic commands
• Intermediate: Text processing, process management, packages
• Advanced: Networking, storage management, user administration
• Expert: Security hardening, automation, troubleshooting

3. Local Practice Mode

Practice on your local system without Docker (use with caution):

lfcs start --local

WARNING: Local mode modifies your actual system. Use only if you understand the risks.

Documentation

• User Guide - Comprehensive usage guide
• Developer Guide - Adding scenarios and contributing
• Architecture - System design and components
• Troubleshooting - Common issues and solutions
• Contributing - Contribution guidelines

Requirements

System Requirements

• OS: Linux, macOS, or Windows with WSL2
• CPU: 2+ cores recommended
• RAM: 4GB minimum, 8GB recommended
• Disk: 10GB free space for Docker images
• Network: Internet connection for Docker image downloads

Software Requirements

• Python 3.9 or higher
• Docker 20.10 or higher
• Git (for cloning repository)

Optional Requirements

• Anthropic or OpenAI API key (for AI features)
• 16GB RAM (for running multiple containers)

Project Structure

lfcs-practice-tool/
├── src/                    # Source code
│   ├── cli/               # Command-line interface
│   ├── core/              # Core engine and scenario loader
│   ├── docker_manager/    # Docker container management
│   ├── validation/        # Validation strategies
│   ├── learn/             # Interactive learning system
│   ├── utils/             # Utilities and helpers
│   └── main.py            # Main entry point
├── scenarios/             # Practice scenarios (83+ YAML files)
│   ├── essential_commands/
│   ├── networking/
│   ├── operations_deployment/
│   ├── storage/
│   └── users_groups/
├── learn_modules/         # Learning modules (14 YAML files)
│   ├── 01_beginner/
│   ├── 02_intermediate/
│   ├── 03_advanced/
│   └── 04_expert/
├── docker/                # Docker configuration
│   ├── base_images/       # Dockerfiles for Ubuntu, CentOS, Rocky
│   └── validation_scripts/ # Custom validation scripts
├── database/              # SQLite database for progress
├── config/                # Configuration files
├── tests/                 # Test suite
└── docs/                  # Documentation

Configuration

Configuration Files

• config/config.yaml - Main configuration
• config/ai_config.yaml - AI provider settings
• .env - Environment variables (create from .env.example)

Environment Variables

# Docker configuration
export DEFAULT_IMAGE="ubuntu"
export CONTAINER_NETWORK="bridge"
export DOCKER_PRIVILEGED="true"

# Database and logs
export DB_PATH="database/progress.db"
export LOGS_PATH="logs"
export LOG_LEVEL="INFO"

# AI configuration (optional)
export AI_ENABLED="true"
export ANTHROPIC_API_KEY="your-key"

# Local mode (practice without Docker)
export LOCAL_MODE="false"

Statistics and Progress

The tool tracks comprehensive statistics:

• Overall Performance: Total attempts, pass rate, average score
• Category Breakdown: Performance by category and difficulty
• Mastery Levels: Percentage mastery for each difficulty level
• Streaks: Current and best passing streaks
• Achievements: Unlocked milestones
• Recommendations: Personalized suggestions for improvement

View your stats anytime:

lfcs stats                    # Overall statistics
lfcs stats --category storage # Category-specific stats

Troubleshooting

Docker Issues

# Docker not running
sudo systemctl start docker

# Permission denied
sudo usermod -aG docker $USER
newgrp docker

# Image not found
cd docker/base_images && ./build_all.sh

Common Issues

• Container fails to start: Check Docker daemon status and available resources
• Validation fails unexpectedly: Check container logs with docker logs <container-id>
• Database locked: Close other instances of the tool
• Permission errors: Ensure write permissions for database and logs directories

See TROUBLESHOOTING.md for detailed solutions.

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Quick Contribution Guide

1. Fork the repository
2. Create a feature branch
3. Add scenarios or fix bugs
4. Write tests
5. Submit a pull request

Adding Scenarios

# Create a new scenario YAML file
cp scenarios/storage/easy/create_directory_01.yaml \
   scenarios/storage/easy/my_scenario_01.yaml

# Edit the scenario
# Test it
lfcs start --category storage --difficulty easy

# Submit PR

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=src --cov-report=html

# Run specific test
pytest tests/unit/test_scenario_loader.py

# Run property-based tests
pytest tests/unit/ -k "property"

License

MIT License - see LICENSE file for details.

Acknowledgments

• Linux Foundation for the LFCS certification program
• Docker for containerization technology
• All contributors and scenario authors

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [email protected]

Roadmap

• Web-based UI
• Multi-user support
• Cloud deployment scenarios
• Kubernetes practice scenarios
• Video tutorials integration
• Mobile app for progress tracking

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Made with ❤️ for the Linux community

Version: 1.1.0 | Author: C Sarath Babu | License: MIT