Awesome Green AI

Overview

Awesome Green AI is a curated collection of resources, tools, and best practices for building environmentally sustainable AI systems. As AI models grow in size and computational requirements, this repository addresses the critical need to reduce the carbon footprint and energy consumption of machine learning workloads.

Features

• Carbon Footprint Measurement: Tools to measure CO2 emissions from AI training and inference
• Energy-Efficient Models: Techniques for building smaller, more efficient models
• Model Optimization: Quantization, pruning, and distillation methods
• Sustainable ML Practices: Best practices for environmentally conscious AI development
• Green Computing Resources: Hardware and cloud options with renewable energy
• Research Papers: Academic literature on sustainable AI
• Impact Assessment Tools: Calculate and report environmental impact of ML workloads

Key Topics

Energy-Efficient Training

• Mixed precision training
• Gradient checkpointing
• Efficient optimizers
• Transfer learning to reduce training time

Model Compression

• Knowledge distillation
• Model pruning
• Quantization (INT8, INT4)
• Neural architecture search for efficiency

Green Infrastructure

• Carbon-aware scheduling
• Renewable energy-powered data centers
• Edge computing to reduce data transfer
• Serverless and efficient cloud services

Measurement & Reporting

• ML CO2 Impact calculator
• CodeCarbon for tracking emissions
• Green Algorithms for research emissions
• Experiment tracking with environmental metrics

Tools Featured

• CodeCarbon: Track and reduce CO2 emissions from computing
• ML CO2 Impact: Calculate carbon footprint of ML models
• Green Algorithms: Estimate carbon footprint of computational research
• CarbonTracker: Track energy consumption during model training

Pricing

Free and open-source resource.