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title: "Aurora AI Framework - Installation Guide | Setup & Configuration" description: "Complete installation guide for Aurora AI Framework v1.0.0 - Step-by-step setup instructions, system requirements, dependencies, and configuration for enterprise AI platform." keywords: "Aurora AI installation, AI framework setup, enterprise AI installation, Python AI setup, machine learning installation, AI dependencies, system requirements" author: "Aurora Development Team" robots: "index, follow" canonical: "https://aurora-ai.github.io/docs/INSTALLATION.md"

Aurora AI Framework - Complete Installation Guide

Quick Installation

🚀 Current System Status: LIVE

  • Web Interface: http://localhost:8081 - ACTIVE
  • Server: Aurora AI Sci-Fi Interface - RUNNING
  • Debug Mode: Enabled (PIN: 343-268-059)
  • API Health: All endpoints responding
  • Last Updated: 2026-05-06

📚 Related Documentation: For complete system architecture, see our Architecture Guide. For user guide, check our User Guide.

🚀 After Installation: Once installed, see our Configuration Guide and System Operations.

🔧 Troubleshooting: For installation issues, see our Troubleshooting Guide.

Prerequisites

  • Python 3.7 or higher
  • pip package manager
  • System requirements: See System Operations Guide for detailed requirements

Installation Steps

  1. Navigate to the Aurora directory:

    cd /home/robbie/Desktop/g_o_d/Aurora

  2. Install dependencies:

    pip install -r requirements.txt

    Note: If you encounter "externally-managed-environment" error, use:

    pip install --break-system-packages -r requirements.txt

    Or create a virtual environment:

    python3 -m venv aurora_env
source aurora_env/bin/activate
pip install -r requirements.txt

  3. Verify installation:

    python test_framework.py

  4. Run quick test:

    python examples/example_usage.py --mode quick

Framework Structure

Aurora/
├── README.md              # Framework### 🚀 Current System Status: LIVE
- **Web Interface**: http://localhost:8081 - **ACTIVE**
- **Server**: Aurora AI Sci-Fi Interface - **RUNNING**
- **Debug Mode**: Enabled (PIN: 343-268-059)
- **API Health**: All endpoints responding
- **Last Updated**: 2026-05-06

## 🌟 Overview
├── main.py                # Main entry point
├── requirements.txt       # Python dependencies
├── test_framework.py      # Structure verification
├── core/                  # Core base classes
│   ├── __init__.py
│   └── base.py
├── modules/               # AI modules
│   ├── __init__.py
│   ├── data_pipeline.py   # Data processing
│   ├── model_trainer.py   # Model training
│   ├── monitoring.py      # Performance monitoring
│   └── inference_service.py # Inference API
├── config/                # Configuration files
│   └── config.yaml
├── data/                  # Data storage
├── logs/                  # Application logs
├── examples/              # Usage examples
│   ├── example_usage.py
│   └── sample_data.csv
└── docs/                  # Documentation
    ├── ARCHITECTURE.md
    └── USER_GUIDE.md

Usage Examples

Basic Usage

# Run the complete framework
python main.py

# Run example with sample data
python examples/example_usage.py --mode complete

# Quick structure test
python test_framework.py

Configuration

Edit config/config.yaml to customize:

  • Data sources and processing
  • Model parameters
  • Monitoring settings
  • API server configuration

Core Features

✅ Data Pipeline: Automated data ingestion and preprocessing
✅ Model Training: Multiple algorithms with hyperparameter optimization
✅ Real-time Inference: REST API for model serving
✅ Monitoring: Performance tracking and alerting
✅ Configuration Management: YAML-based configuration
✅ Extensible Architecture: Modular design for easy extension

Supported Algorithms

Classification

  • Random Forest
  • Logistic Regression
  • Support Vector Machine

Regression

  • Random Forest Regressor
  • Linear Regression
  • Support Vector Regression

API Endpoints

When running, the framework provides these endpoints:

  • GET /health - Health check
  • POST /predict - Make predictions
  • POST /predict_proba - Get probabilities (classification)
  • GET /stats - Service statistics
  • GET /history - Prediction history

Troubleshooting

Common Issues

  1. Python not found:

    # Use python3 instead of python
python3 main.py

  2. Module import errors:

    # Check you're in the Aurora directory
pwd
# Should show /home/robbie/Desktop/g_o_d/Aurora

  3. Permission errors:

    # Create directories if needed
mkdir -p data logs models reports

  4. Dependency conflicts:

    # Use virtual environment
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Getting Help

  1. Check the test output: python test_framework.py
  2. Review logs in the logs/ directory
  3. Consult the User Guide: docs/USER_GUIDE.md
  4. Check architecture: docs/ARCHITECTURE.md

Next Steps

  1. Prepare your data in CSV format
  2. Configure the framework in config/config.yaml
  3. Run the framework: python main.py
  4. Monitor performance via the API endpoints
  5. Extend with custom modules as needed

Performance Tips

  • Use appropriate data sizes for your hardware
  • Configure monitoring intervals based on needs
  • Enable hyperparameter optimization for better models
  • Set up alerting for production deployments

Security Notes

  • Change default API keys in production
  • Enable authentication for sensitive deployments
  • Secure configuration files with sensitive data
  • Monitor for data drift in production

Aurora AI Framework v1.0.0
Streamlined AI/ML pipeline automation for the future

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