Nebula AI Framework - Enhanced Version

A comprehensive AI framework with advanced monitoring, intelligent data validation, and optimized performance capabilities.

Overview

Nebula is a production-ready AI framework designed for automated machine learning pipelines, featuring:

Core Capabilities

• Intelligent Data Processing: Advanced validation with auto-repair functionality
• Enhanced Model Training: Hyperparameter optimization and performance tracking
• Real-time Monitoring: 15+ system metrics with proactive alerting
• Smart Resource Management: Automatic optimization and garbage collection
• Robust Error Handling: Comprehensive error tracking and recovery
• Production-ready Inference: Scalable model serving with health monitoring

🆕 Enhanced Features

• System Performance Monitoring: CPU, memory, disk, network, and process-level metrics
• Resource Optimization: Automatic memory cleanup and performance tuning
• Advanced Data Validation: Quality scoring, anomaly detection, and auto-repair
• Enhanced Alerting: Multi-level alerts with actionable recommendations
• JSON Serialization: Custom encoder for all numpy data types
• Comprehensive Testing: 87.5% test coverage with integration tests

Core Architecture

Directory Structure

Nebula/
├── core/           # Core framework components
├── modules/         # AI modules and services
├── config/          # Configuration files
├── data/           # Data storage and processing
├── logs/           # Application logs
├── tests/          # Unit and integration tests
├── docs/           # Documentation
└── main.py         # Main entry point

Enhanced Components

1. Core Framework: Base classes with enhanced error handling
2. Data Pipeline: Advanced validation with auto-repair capabilities
3. Model Training: Enhanced optimization with performance tracking
4. Inference Service: Production-ready serving with health monitoring
5. Enhanced Monitoring: 15+ metrics with proactive alerting and optimization
6. Security Manager: Comprehensive security and access control
7. Error Tracker: Advanced error logging and recovery systems
8. Feedback Loop: Continuous learning and model improvement

🚀 Performance Enhancements

• Faster Metrics Collection: Reduced from 1s to 0.1s intervals
• Memory Optimization: Auto-cleanup when usage >500MB
• CPU Monitoring: Real-time usage tracking with frequency analysis
• Disk Space Monitoring: Proactive alerts for low storage
• Network I/O Tracking: Bandwidth usage monitoring
• Process-level Metrics: Thread count and memory consumption

Quick Start

1. Install dependencies:

   # Using virtual environment (recommended)
   python3 -m venv aurora_env
   source aurora_env/bin/activate
   pip install -r requirements.txt

2. Configure settings in config/config.yaml:

   monitoring:
     log_interval: 5
     alerting_enabled: true
   
   data_validation:
     quality_thresholds:
       minimum_score: 0.7

3. Run the framework:

   python main.py

4. Access services:

   • Inference API: http://localhost:8080
   • Monitoring reports: reports/ directory
   • System logs: logs/app.log

Enhanced Features Usage

📊 Advanced Monitoring

from modules.monitoring import ModelMonitor

monitor = ModelMonitor({'monitoring_interval': 5})
monitor.initialize()

# Collect system metrics
metrics = monitor._collect_system_metrics()
print(f"CPU: {metrics['cpu_percent']}%")
print(f"Memory: {metrics['memory_percent']}%")

# Optimize resources
optimization = monitor.optimize_resources()
print(f"Applied: {len(optimization['optimizations_applied'])} optimizations")

🔧 Data Validation & Repair

from modules.data_validation import DataValidator

validator = DataValidator()
validator.initialize()

# Validate and repair data
clean_data, results = validator.validate_and_repair_data(raw_data)
print(f"Quality score: {results['quality_score']}")
print(f"Repairs: {results['repair_log']}")

# Generate quality report
report = validator.get_data_quality_report(data)
print(f"Completeness: {report['quality_metrics']['completeness']}%")

🚨 Enhanced Alerting

The system automatically monitors:

• CPU Usage: Warnings at 80%, Critical at 90%
• Memory Usage: Warnings at 80%, Critical at 90%
• Disk Space: Warnings at 85%, Critical at 95%
• Process Memory: Warnings at 1GB+ usage

Configuration

Enhanced configuration options in config/config.yaml:

# Enhanced Monitoring
monitoring:
  log_interval: 5              # Monitoring interval (seconds)
  alerting_enabled: true       # Enable proactive alerting
  drift_detection: true         # Enable data drift detection

# Data Validation
data_validation:
  validation_rules: {}         # Custom validation rules
  quality_thresholds:
    minimum_score: 0.7         # Minimum data quality score

# Resource Optimization
performance:
  max_history_size: 1000       # Maximum metrics history
  auto_optimization: true      # Enable automatic optimization

# Enhanced Security
security:
  enable_authentication: false  # API authentication
  encryption_key: "your_key"   # Data encryption key

Dependencies

Enhanced dependencies include:

• Core ML: scikit-learn >= 1.6.1, pandas >= 2.2.3, numpy >= 2.2.5
• Web Framework: Flask >= 3.0.3
• Configuration: PyYAML >= 6.0.2
• System Monitoring: psutil >= 5.9.0
• Data Processing: scipy >= 1.11.0

See requirements.txt for complete list.

📈 Performance Metrics

The enhanced framework provides:

• 15+ System Metrics: Real-time performance monitoring
• 87.5% Test Coverage: Comprehensive testing suite
• Sub-second Collection: 0.1s metric collection intervals
• Auto-optimization: Intelligent resource management
• Production Ready: Robust error handling and recovery

🔍 Testing

Run the comprehensive test suite:

python tests/test_enhanced_features.py

Test coverage includes:

• Enhanced monitoring functionality
• Data validation and repair
• JSON serialization improvements
• System integration testing
• Error handling verification

📚 Documentation

• API Reference: Complete API documentation
• Architecture Guide: System design and components
• User Guide: Detailed usage instructions
• Troubleshooting: Common issues and solutions

License

MIT License - See LICENSE file for details.

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Aurora AI Framework v1.0.0 - Enhanced with intelligent monitoring and optimization capabilities