samuelrince/awesome-green-ai
📦 Open Source Projectsamuelrince
A curated collection of resources and tools to measure and minimize the environmental impact of AI systems.
The awesome-green-ai repository is an essential resource for the modern AI practitioner concerned with the ecological footprint of large-scale computing. As AI models grow in complexity and energy demand, this list provides a structured pathway to sustainability. It categorizes resources into critical areas such as carbon emission tracking tools, energy-efficient model training techniques, hardware optimization strategies, and policy-oriented research. By aggregating disparate tools—ranging from carbon calculators to specialized libraries for model pruning and quantization—the repository lowers the barrier for teams to integrate sustainability metrics into their CI/CD pipelines. It covers the entire stack, from high-level software design patterns to low-level hardware utilization, ensuring that developers have the necessary knowledge to balance performance with environmental stewardship. Whether you are a researcher looking for the latest papers on efficient training or an engineer seeking tools to monitor cloud energy usage, this curated list provides the foundational knowledge required to build greener AI.
💡Highlights
- ├─Curated green AI resources
- ├─Tools for carbon tracking
- └─Energy-efficient ML methods
🎯For
- ├─AI Researchers
- ├─Sustainability Engineers
- └─Data Scientists