---
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](ARCHITECTURE.md). For user guide, check our [User Guide](USER_GUIDE.md).

> **🚀 After Installation**: Once installed, see our [Configuration Guide](CONFIGURATION_GUIDE.md) and [System Operations](SYSTEM_OPERATIONS.md).

> **🔧 Troubleshooting**: For installation issues, see our [Troubleshooting Guide](TROUBLESHOOTING.md).

### Prerequisites
- Python 3.7 or higher
- pip package manager
- System requirements: See [System Operations Guide](SYSTEM_OPERATIONS.md) for detailed requirements

### Installation Steps

1. **Navigate to the Aurora directory**:
   ```bash
   cd /home/robbie/Desktop/g_o_d/Aurora
   ```

2. **Install dependencies**:
   ```bash
   pip install -r requirements.txt
   ```
   
   *Note: If you encounter "externally-managed-environment" error, use:*
   ```bash
   pip install --break-system-packages -r requirements.txt
   ```
   *Or create a virtual environment:*
   ```bash
   python3 -m venv aurora_env
   source aurora_env/bin/activate
   pip install -r requirements.txt
   ```

3. **Verify installation**:
   ```bash
   python test_framework.py
   ```

4. **Run quick test**:
   ```bash
   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
```bash
# 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**:
   ```bash
   # Use python3 instead of python
   python3 main.py
   ```

2. **Module import errors**:
   ```bash
   # Check you're in the Aurora directory
   pwd
   # Should show /home/robbie/Desktop/g_o_d/Aurora
   ```

3. **Permission errors**:
   ```bash
   # Create directories if needed
   mkdir -p data logs models reports
   ```

4. **Dependency conflicts**:
   ```bash
   # 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