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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