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.

• About CodeCarbon 💡
• Quickstart 🚀
  • Installation 🔧
  • Start to estimate your impact 📏
    • Monitoring your whole machine
    • In your python code
    • Visualize
• Contributing 🤝
• How To Cite 📝
• Contact 📝

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.

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: