Track & reduce Co2 emissions from your computing
AI can benefit society in many ways, but given the energy needed to support the computing behind AI, these benefits can come at a high environmental price. Use Code Carbon to track and reduce your CO2 output.
What we are
- A lightweight, easy to use python library
- Open source, free & community driven
- Effective visual outputs
Seamless integration
Only a few lines of code
Computer emits co2. we started measuring how much
A single datacenter can consume large amounts of energy to run computing code. An innovative new tracking tool is designed to measure the climate impact of artificial intelligence. Kana Lottick, Silvia Susai, Sorelle Friedler, and Jonathan Wilson. Energy Usage Reports: Environmental awareness as part of algorithmic accountability. NeurlPS Workshop on Tackling Climate Change with Machine Learning, 2019.
How It Works
1
Download package
2
Embed the code
3
Run and track
4
Visualize results
Dashboard
Visualizing the outputs & insights
Emissions for individual code repositories based on infraestructure and power consumption.
Exemplary equivalents to put emissions in context.
Recommends compute regions with lower carbon intensity for major cloud providers – AWS, Azure, and GCP.
Call to Action
Use, contribute and spread the word!
Use it!
We look forward to developers and researchers using the tool and sharing their feedback
Contribute!
We look forward to developers contributing to CodeCarbon development <3<3<3
Spread the love!
Spread the word about CodeCarbon among your colleagues, peers, conferences, and developer forums
CodeCarbon is maintained by amazing contributors
Here's the team that helped build Code Carbon
Benoît Courty
Data Scientist
Amine Saboni
Deep Learning Engineer
Sasha Luccioni
Researcher
Hugging Face
Iñigo Imaz
Software Developer
CodeCarbon is supported by our sponsors
CodeCarbon was developed by four partner organizations and is made freely available to the community
