Carbon-Aware AI Job Scheduler

Sustainable job scheduling for smarter AI workloads.

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Inspiration

As AI models become more computationally demanding, the environmental impact of training and running these models has gained increasing attention. The carbon footprint associated with AI workloads is a pressing issue, and we wanted to create a tool that helps mitigate this impact without sacrificing performance. Our goal was to design an application that seamlessly integrates sustainability into AI workflows, allowing users to be conscious of the carbon emissions of their tasks. By making the connection between AI and sustainability clearer, we hope to promote greener practices within the field.

What We Learned

This hackathon provided an invaluable opportunity to explore cutting-edge technologies and environmental computing:

  • AI Integration: We gained hands-on experience with multiple AI services:

    • Groq API for efficient LLM inference
    • Perplexity AI for generating environmental insights
    • Custom ML models for carbon intensity predictions

  • Real-time Data Processing:

    • WattTime API integration for live carbon intensity data
    • WebSocket implementations for real-time updates
    • Data aggregation and analysis techniques

  • Modern Web Development:

    • Material UI theming and component customization
    • React hooks and context for state management
    • Recharts for dynamic data visualization

  • Backend Architecture:

    • FastAPI for high-performance async endpoints
    • SQLAlchemy ORM for database management
    • Pydantic for robust data validation

Most importantly, we learned how intelligent scheduling of computing tasks can significantly reduce carbon emissions while maintaining performance.

How We Built It

Frontend Architecture

  • React.js with Material UI for a responsive interface
  • Framer Motion for smooth animations
  • Custom theme provider with dark/light mode support
  • Recharts for interactive data visualization
  • Date-fns for time manipulation

Backend Systems

  • FastAPI framework for high-performance API endpoints
  • SQLAlchemy for database operations
  • Multiple AI service integrations:
    • Groq API for LLM-powered scheduling decisions
    • Perplexity AI for environmental impact analysis
    • WattTime API for real-time carbon intensity data

Core Features

  • Intelligent job queue system with priority scheduling
  • Real-time carbon intensity monitoring
  • Dynamic environmental impact calculations
  • Interactive analytics dashboard
  • Automated scheduling recommendations

Challenges Faced

  1. API Integration Complexity:

    • Managing multiple AI service integrations (Groq, Perplexity, WattTime)
    • Implementing fallback mechanisms for API failures
    • Optimizing API call efficiency

  2. Real-time Data Management:

    • Implementing WebSocket connections for live updates
    • Balancing data freshness with performance
    • Handling concurrent API requests

  3. UI/UX Design:

    • Creating an intuitive interface for complex data
    • Implementing smooth theme transitions
    • Optimizing chart performance with large datasets

  4. Technical Integration:

    • Coordinating multiple AI services for optimal scheduling
    • Managing environment variables securely
    • Implementing proper error handling

What's next for the project

We have exciting plans to enhance the project:

Technical Improvements

  • Implement machine learning models for predictive scheduling
  • Add support for more AI service providers
  • Enhance the analytics system with more detailed metrics
  • Develop a mobile application

Feature Enhancements

  • Advanced scheduling algorithms considering:
    • Peak energy usage times
    • Regional energy sources
    • Grid carbon intensity predictions
    • Resource availability patterns

User Experience

  • Email/Slack notifications for job status updates
  • Customizable dashboards and reports
  • Team collaboration features
  • API documentation and SDK

Community Building

  • Open-source the core components
  • Create a knowledge base for sustainable computing
  • Develop plugins for popular development tools
  • Build a community around green computing practices

Our goal is to make sustainable computing accessible and efficient for everyone while continuing to leverage cutting-edge AI technologies for environmental benefit.

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