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https://github.com/samuelrince/awesome-green-ai
A curated list of awesome Green AI resources and tools to reduce the environmental impacts of using and deploying AI.
https://github.com/samuelrince/awesome-green-ai
List: awesome-green-ai
ai awesome-list climate deep-learning green-ai green-software machine-learning sustainability sustainable-ai
Last synced: 3 months ago
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A curated list of awesome Green AI resources and tools to reduce the environmental impacts of using and deploying AI.
- Host: GitHub
- URL: https://github.com/samuelrince/awesome-green-ai
- Owner: samuelrince
- License: cc0-1.0
- Created: 2022-11-28T19:52:40.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-08T08:28:16.000Z (4 months ago)
- Last Synced: 2024-08-07T09:07:21.018Z (3 months ago)
- Topics: ai, awesome-list, climate, deep-learning, green-ai, green-software, machine-learning, sustainability, sustainable-ai
- Homepage:
- Size: 287 KB
- Stars: 35
- Watchers: 4
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - awesome-green-ai - A curated list of awesome Green AI resources and tools to reduce the environmental impacts of using and deploying AI. (Other Lists / PowerShell Lists)
README
# Awesome Green AI 🤖🌱
*A curated list of awesome Green AI resources and tools to reduce the environmental impacts of using and deploying AI.*
Generated with Stable Diffusion v2
In 2020, Information and Communications Technology (ICT) sector carbon footprint was estimated to be between **2.1-3.9% of total global greenhouse gas emissions**. The ICT sector [continues to grow and now dominates other industries](https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data). It is estimated that the **carbon footprint will double to 6-8% by 2025**. For ICT sector to remain compliant with the Paris Agreement, the industry must reduce by 45% its GHG emissions from 2020 to 2030 and reach net zero by 2050 ([Freitag et al., 2021](https://doi.org/10.1016/j.patter.2021.100340)).
AI is one of the fastest growing sectors, disrupting many other industries ([AI Market Size Report, 2022](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market)). It therefore has an important role to play in reducing carbon footprint. The impacts of ICT, and therefore AI, are **not limited to GHG emissions and electricity consumption**. We need to take into account **all major impacts** (abiotic resource depletion, primary energy consumption, water usage, etc.) using Life Cycle Assessment (LCA) ([Arushanyan et al., 2013](https://doi.org/10.1016/j.compind.2013.10.003)).
**AI sobriety not only means optimizing energy consumption and reducing impacts**, but also includes studies on **[indirect impacts](https://en.wikipedia.org/wiki/Rebound_effect_(conservation)#Direct_and_indirect_effects)** and **[rebound effects](https://en.wikipedia.org/wiki/Jevons_paradox)** that can negate all efforts to reduce the environmental footprint ([Willenbacher et al. 2021](https://doi.org/10.1007/978-3-030-88063-7_5)). It is therefore imperative to consider the use of AI before launching a project in order to avoid indirect impacts and rebound effects later on.
All contributions are welcome. Add links through [pull requests](https://github.com/samuelrince/awesome-green-ai/pulls) or create an [issue](https://github.com/samuelrince/awesome-green-ai/issues) to start a discussion.
## 🛠 Tools
### Code-Based Tools
*Tools to measure and compute environmental impacts of AI.*
- [CodeCarbon](https://github.com/mlco2/codecarbon) – Track emissions from Compute and recommend ways to reduce their impact on the environment.
    
- [carbontracker](https://github.com/lfwa/carbontracker) – Track and predict the energy consumption and carbon footprint of training deep learning models.
 
- [Eco2AI](https://github.com/sb-ai-lab/Eco2AI) – A python library which accumulates statistics about power consumption and CO2 emission during running code.
 
- [Zeus](https://github.com/SymbioticLab/Zeus) – A framework for deep learning energy measurement and optimization.
 
- [Tracarbon](https://github.com/fvaleye/tracarbon) – Tracks your device's energy consumption and calculates your carbon emissions using your location.
  
- [EcoLogits](https://github.com/genai-impact/ecologits) – Estimates the energy consumption and environmental footprint of LLM inference through APIs.
   
- [AIPowerMeter](https://github.com/GreenAI-Uppa/AIPowerMeter) – Easily monitor energy usage of machine learning programs.
 
☠️ No longer maintained:
- [carbonai](https://github.com/Capgemini-Invent-France/CarbonAI) – Python package to monitor the power consumption of any algorithm.
   
- [experiment-impact-tracker](https://github.com/Breakend/experiment-impact-tracker) – A simple drop-in method to track energy usage, carbon emissions, and compute utilization of your system.
 
- [GATorch](https://github.com/GreenAITorch/GATorch) – An Energy-Aware PyTorch Extension.
 
- [GPU Meter](https://github.com/autoai-incubator/powermeter) – Power Consumption Meter for NVIDIA GPUs.
 
- [PyJoules](https://github.com/powerapi-ng/pyJoules) – A Python library to capture the energy consumption of code snippets.
 
### Monitoring Tools
*Tools to monitor power consumption and environmental impacts.*
- [Scaphandre](https://github.com/hubblo-org/scaphandre) – A metrology agent dedicated to electrical power consumption metrics.
  
- [PowerJoular](https://github.com/joular/powerjoular) – Monitor power consumption of multiple platforms and processes.
   
- [Boagent](https://github.com/Boavizta/boagent) – Local API and monitoring agent focussed on environmental impacts of the host.
- [vJoule](https://github.com/davidson-consulting/vjoule) – A tool to estimate the energy consumption of your processes.
  
- [jupyter-power-usage](https://github.com/mahendrapaipuri/jupyter-power-usage) – Jupyter extension to display CPU and GPU power usage and carbon emissions.
 
### Optimization Tools
*Tools to optimize energy consumption or environmental impacts.*
- [Zeus](https://github.com/SymbioticLab/Zeus) – A framework for deep learning energy measurement and optimization.
 
- [GEOPM](https://github.com/geopm/geopm) – A framework to enable efficient power management and performance optimizations.
 
### Calculation Tools
*Tools to estimate environmental impacts of algorithms, models and compute resources.*
* [Green Algorithms](http://calculator.green-algorithms.org/) - A tool to easily estimate the carbon footprint of a project.
* [ML CO2 Impact](https://mlco2.github.io/impact/) - Compute model emissions and add the results to your paper with our generated latex template.
* [EcoLogits Calculator](https://huggingface.co/spaces/genai-impact/ecologits-calculator) - Estimate energy consumption and environmental impacts of LLM inference.
* [AI Carbon](https://huggingface.co/spaces/sasha/AI_Carbon) - Estimate your AI model's carbon footprint.
* [MLCarbon](https://github.com/SotaroKaneda/MLCarbon) - End-to-end carbon footprint modeling tool.
* [GenAI Carbon Footprint](https://github.com/greenscale-ai/genai-carbon-footprint) - A tool to estimate energy use (kWh) and carbon emissions (gCO2eq) from LLM usage.
Generic tools:
* [Boaviztapi](https://github.com/Boavizta/boaviztapi/) - Multi-criteria impacts of compute resources taking into account manufacturing and usage.
* [Datavizta](https://datavizta.boavizta.org/serversimpact) - Compute resources data explorer not limited to AI.
* [EcoDiag](https://ecoinfo.cnrs.fr/ecodiag-calcul/) - Compute carbon footprint of IT resources taking into account manufactuing and usage (🇫🇷 only).
### Leaderboards
* [LLM Perf Leaderboad](https://huggingface.co/spaces/optimum/llm-perf-leaderboard) - Benchmarking LLMs on performance and energy.
* [ML.Energy Leaderboard](https://ml.energy/leaderboard/?__theme=light) - Energy consumption of LLMs at inference.
## 📄 Papers
* Energy and Policy Considerations for Deep Learning in NLP - [Strubell et al. (2019)](https://arxiv.org/abs/1906.02243)
* Quantifying the Carbon Emissions of Machine Learning - [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700)
* Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models - [Anthony et al. (2020)](https://arxiv.org/abs/2007.03051)
* Green AI - [Schwartz et al. (2020)](https://cacm.acm.org/magazines/2020/12/248800-green-ai/fulltext)
* Carbon Emissions and Large Neural Network Training - [Patterson, et al. (2021)](https://arxiv.org/abs/2104.10350)
* Green Algorithms: Quantifying the Carbon Footprint of Computation - [Lannelongue et al. (2021)](https://onlinelibrary.wiley.com/doi/10.1002/advs.202100707)
* Aligning artificial intelligence with climate change mitigation - [Kaack et al. (2021)](https://hal.archives-ouvertes.fr/hal-03368037/document)
* A Pratical Guide to Quantifying Carbon Emissions for Machine Learning researchers and practitioners - [Ligozat et al. (2021)](https://hal.archives-ouvertes.fr/hal-03376391/document)
* Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions - [Ligozat et al. (2022)](https://arxiv.org/abs/2110.11822)
* Measuring the Carbon Intensity of AI in Cloud Instances - [Dodge et al. (2022)](https://arxiv.org/abs/2206.05229)
* Estimating the Carbon Footprint of BLOOM a 176B Parameter Language Model - [Luccioni et al. (2022)](https://arxiv.org/abs/2211.02001)
* Bridging Fairness and Environmental Sustainability in Natural Language Processing - [Hessenthaler et al. (2022)](https://arxiv.org/abs/2211.04256)
* Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI - [Budennyy et al. (2022)](https://arxiv.org/abs/2208.00406)
* Environmental assessment of projects involving AI methods - [Lefèvre et al. (2022)](https://hal.science/hal-03922093v1/document)
* Sustainable AI: Environmental Implications, Challenges and Opportunities - [Wu et al. (2022)](https://arxiv.org/abs/2111.00364)
* The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink - [Patterson et al. (2022)](https://arxiv.org/abs/2204.05149)
* Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning - [Henderson et al. (2022)](https://arxiv.org/abs/2002.05651)
* Towards Sustainable Artificial Intelligence: An Overview of Environmental Protection Uses and Issues - [Pachot et al. (2022)](https://arxiv.org/abs/2212.11738)
* Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions - [Delanoë et al. (2023)](https://www.sciencedirect.com/science/article/pii/S030147972300049X)
* Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models - [Li et al. (2023)](https://arxiv.org/abs/2304.03271)
* Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training - [You et al. (2023)](https://www.usenix.org/conference/nsdi23/presentation/you)
* Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training - [Yang et al. (2023)](https://www.climatechange.ai/papers/iclr2023/29)
* Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems - [Li et al. (2023)](https://arxiv.org/abs/2306.13177)
* LLMCarbon: Modeling the End-To-End Carbon Footprint of Large Language Models - [Faiz et al. (2023)](https://arxiv.org/abs/2309.14393)
* Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study - [Castano et al. (2023)](https://ieeexplore.ieee.org/document/10304801)
* Power Hungry Processing: Watts Driving the Cost of AI Deployment? - [Luccioni et al. (2023)](https://arxiv.org/abs/2311.16863)
* Perseus: Removing Energy Bloat from Large Model Training - [Chung et al. (2023)](https://arxiv.org/abs/2312.06902)
* Timeshifting strategies for carbon-efficient long-running large language model training - [Jagannadharao et al. (2023)](https://link.springer.com/article/10.1007/s11334-023-00546-x)
* Estimating the environmental impact of Generative-AI services using an LCA-based methodology - [Berthelot et al. (2024)](https://hal.univ-lorraine.fr/INRIA/hal-04346102v2)
* Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference - [Stojkovic et al. (2024)](https://arxiv.org/abs/2403.20306)
* Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training - [Liu et al. (2024)](https://arxiv.org/abs/2404.01157)
* Engineering Carbon Emission-aware Machine Learning Pipelines - [Humsom et al. (2024)](https://dl.acm.org/doi/10.1145/3644815.3644943)
* A simplified machine learning product carbon footprint evaluation tool - [Lang et al. (2024)](https://www.sciencedirect.com/science/article/pii/S2666789424000254)
* Beyond Efficiency: Scaling AI Sustainably - [Wu et al. (2024)](https://arxiv.org/abs/2406.05303v1)
* MLCA: a tool for Machine Learning Life Cycle Assessment - [Morand et al. (2024)](https://conf.researchr.org/getImage/ict4s-2024/orig/ICT4S2024_paper_73.pdf)
### Survey Papers
* Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools - [Bannour et al.(2021)](https://aclanthology.org/2021.sustainlp-1.2.pdf)
* A Survey on Green Deep Learning - [Xu et al. (2021)](https://arxiv.org/abs/2111.05193)
* A Systematic Review of Green AI - [Verdecchia et al. (2023)](https://arxiv.org/abs/2301.11047)
* Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning - [Luccioni et al. (2023)](https://arxiv.org/abs/2302.08476)
* Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems - [Miao et al. (2023)](https://arxiv.org/abs/2312.15234)