Estimate and track carbon emissions from your computer, quantify and analyze their impact.
CodeCarbon websites:
- Main website to learn why we do this.
- Dashboard to see your emissions, read the API doc before.
- Documentation to learn how to use the package and our methodology.
- GitHub to look at the source code and contribute.
- Discord to chat with us.
CodeCarbon started with a quite simple question:
What is the carbon emission impact of my computer program? 🤷
We found some global data like "computing currently represents roughly 0.5% of the world’s energy consumption" but nothing on our individual/organisation level impact.
At CodeCarbon, we believe, along with Niels Bohr, that "Nothing exists until it is measured". So we found a way to estimate how much CO2 we produce while running our code.
How?
We created a Python package that estimates your hardware electricity power consumption (GPU + CPU + RAM) and we apply to it the carbon intensity of the region where the computing is done.
We explain more about this calculation in the Methodology section of the documentation.
Our hope is that this package will be used widely for estimating the carbon footprint of computing, and for establishing best practices with regards to the disclosure and reduction of this footprint.
So ready to "change the world one run at a time"? Let's start with a very quick set up.
From PyPI repository
pip install codecarbon
Using Conda environments If you're using Conda, you can install CodeCarbon with pip in your Conda environment:
conda activate your_env pip install codecarbon
To see more installation options please refer to the documentation: Installation
from codecarbon import track_emissions @track_emissions() def your_function_to_track(): # your code
After running your code, you will find an emissions.csv that you can visualize with carbonboard --filepath="examples/emissions.csv".
To use the online dashboard you need to create an account on CodeCarbon Dashboard. Once you have an account, you can create an experiment_id to track your emissions.
To get an experiment_id enter:
! codecarbon login
You can now store it in a .codecarbon.config at the root of your project
[codecarbon] log_level = DEBUG save_to_api = True experiment_id = 2bcbcbb8-850d-4692-af0d-76f6f36d79b2 #the experiment_id you get with init
Now you have 2 main options:
In your command prompt use:
codecarbon monitor
The package will track your emissions independently from your code.
In your command prompt use:
codecarbon detect
The package will detect and print your hardware information (RAM, CPU, GPU).
from codecarbon import track_emissions @track_emissions() def your_function_to_track(): # your code
The package will track the emissions generated by the execution of your function.
There is other ways to use codecarbon package, please refer to the documentation to learn more about it: Usage
You can now visualize your experiment emissions on the dashboard. dashboard
Hope you enjoy your first steps monitoring your carbon computing impact! Thanks to the incredible codecarbon community 💪🏼 a lot more options are available using codecarbon including:
- offline mode
- cloud mode
- comet integration...
Please explore the Documentation to learn about it If ever what your are looking for is not yet implemented, let us know through the issues and even better become one of our 🦸🏼♀️🦸🏼♂️ contributors! more info 👇🏼
We are hoping that the open-source community will help us edit the code and make it better!
You are welcome to open issues, even suggest solutions and better still contribute the fix/improvement! We can guide you if you're not sure where to start but want to help us out 🥇
In order to contribute a change to our code base, please submit a pull request (PR) via GitHub and someone from our team will go over it and accept it.
Check out our contribution guidelines
Feel free to chat with us on Discord.
If you find CodeCarbon useful for your research, you can find a citation under a variety of formats on Zenodo.
Here is a sample for BibTeX:
@software{benoit_courty_2024_11171501,
author = {Benoit Courty and
Victor Schmidt and
Sasha Luccioni and
Goyal-Kamal and
MarionCoutarel and
Boris Feld and
Jérémy Lecourt and
LiamConnell and
Amine Saboni and
Inimaz and
supatomic and
Mathilde Léval and
Luis Blanche and
Alexis Cruveiller and
ouminasara and
Franklin Zhao and
Aditya Joshi and
Alexis Bogroff and
Hugues de Lavoreille and
Niko Laskaris and
Edoardo Abati and
Douglas Blank and
Ziyao Wang and
Armin Catovic and
Marc Alencon and
Michał Stęchły and
Christian Bauer and
Lucas Otávio N. de Araújo and
JPW and
MinervaBooks},
title = {mlco2/codecarbon: v2.4.1},
month = may,
year = 2024,
publisher = {Zenodo},
version = {v2.4.1},
doi = {10.5281/zenodo.11171501},
url = {https://doi.org/10.5281/zenodo.11171501}
}Feel free to chat with us on Discord.
Codecarbon was formerly developed by volunteers from Mila and the DataForGoodFR community alongside donated professional time of engineers at Comet.ml and BCG GAMMA.
Now CodeCarbon is supported by Code Carbon, a French non-profit organization whose mission is to accelerate the development and adoption of CodeCarbon.
Comparison of the number of stars accumulated by the different Python CO2 emissions projects: Star History Chart