Track & reduce CO₂ emissions from your local computing
Estimate and track carbon emissions from your computer, quantify and analyze their impact.
- 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")
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.
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.
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
- Main website to learn why we do this.
- Dashboard to see your emissions.
- Documentation to learn how to use the package and our methodology.
- EcoLogits to track emissions from GenAI API calls (OpenAI, Anthropic, etc.).
- mlco2 on GitHub for our other open-source projects.
- GitHub to look at the source code and contribute.
- Discord to chat with us.
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
1 maintainer
Release files for codecarbon 3.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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Built distribution (wheel)
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|---|---|---|---|---|
| codecarbon-3.3.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 859.4 kB
Release files / codecarbon-3.3.1.tar.gz
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| Tags | Source |
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Transparency logRelease files / codecarbon-3.3.1-py3-none-any.whl
| Download URL | codecarbon-3.3.1-py3-none-any.whl |
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| Size | 402.7 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 9, 2026.
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