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Track & reduce CO₂ emissions from your local computing

Estimate and track carbon emissions from your computer, quantify and analyze their impact.

DOI OpenSSF Scorecard codecov Discord

  • A lightweight, easy to use Python library – Simple API to track emissions
  • Open source, free & community driven – Built by and for the community
  • Effective visual outputs – Put emissions in context with real-world equivalents

Tracking GenAI API calls? CodeCarbon measures emissions from local computing (your hardware). To track emissions from remote GenAI API calls (OpenAI, Anthropic, Mistral, etc.), use EcoLogits. Both tools are complementary.

Join the community! Have questions, want to share your work, or contribute? Join us on Discord – we're here to help and excited to hear from you!

Installation

pip install codecarbon

If you use Conda:

conda activate your_env
pip install codecarbon

More installation options: installation docs.

Quickstart (Python)

from codecarbon import EmissionsTracker

tracker = EmissionsTracker()
tracker.start()

# Your code here

emissions = tracker.stop()
print(f"Emissions: {emissions} kg CO₂")

Learn more →

Quickstart (CLI)

Track a command without changing your code:

codecarbon monitor --no-api -- python train.py

Detect your hardware:

codecarbon detect

Full CLI guide: CLI tutorial.

Configuration

You can configure CodeCarbon using:

  • ~/.codecarbon.config (global)
  • ./.codecarbon.config (project-local)
  • CODECARBON_* environment variables
  • Python arguments (EmissionsTracker(...))

Configuration precedence and examples: configuration guide.

How it works

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.

CodeCarbon focuses on the main compute components it can measure or estimate directly: CPU, GPU, and RAM. It does not separately model disk I/O, network transfers, displays, cooling, or other peripherals because those sources are usually much smaller for local code-level experiments and are not exposed through the same low-overhead measurement interfaces.

calculation Summary

We explain more about this calculation in the Methodology section of the documentation.

Visualize

You can visualize your experiment emissions on the dashboard or locally with carbonboard.

dashboard

Quick links

Section Description
Quickstart Get started in 5 minutes
Installation Install CodeCarbon
CLI Tutorial Track emissions from the command line
Python API Tutorial Track emissions in Python code
Comparing Model Efficiency Measure carbon efficiency across ML models
API Reference Full parameter documentation
Framework examples (scikit-learn) Task-oriented ML framework examples
Methodology How emissions are calculated
When to use CodeCarbon vs EcoLogits Choose the right tool
EcoLogits Track emissions from GenAI API calls
Discord Community Chat with us and the community

Links

Contributing

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.

Check out our contribution guidelines.

Feel free to chat with us on Discord.

Citation

If you find CodeCarbon useful for your research, you can find a citation under a variety of formats on Zenodo.

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}
}

Contact

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.

Sponsors

Clever Cloud     Data For Good     GitHub     Mozilla

Project links

Data verified by PyPI on Sep 9, 2026
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Owner

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1 maintainer

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Credits

Author: Mila, DataForGood, BCG GAMMA, Comet.ml, Haverford College

GitHub Statistics

Data verified by PyPI on Sep 9, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.10

Provides Extra

carbonboard viz-legacy

Classifiers

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