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real-world equivalents\n\n\u003e **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**](https://ecologits.ai/). Both tools are complementary.\n\n## Installation\n\n```bash\npip install codecarbon\n```\n\nIf you use Conda:\n\n```bash\nconda activate your_env\npip install codecarbon\n```\n\nMore installation options: [installation docs](https://docs.codecarbon.io/latest/how-to/installation/).\n\n## Quickstart (Python)\n\n```python\nfrom codecarbon import EmissionsTracker\n\ntracker = EmissionsTracker()\ntracker.start()\n\n# Your code here\n\nemissions = tracker.stop()\nprint(f\"Emissions: {emissions} kg CO₂\")\n```\n\n[**Learn more** →](https://docs.codecarbon.io/latest/tutorials/first-tracking/)\n\n## Quickstart (CLI)\n\nTrack a command without changing your code:\n\n```bash\ncodecarbon monitor --no-api -- python train.py\n```\n\nDetect your hardware:\n\n```bash\ncodecarbon detect\n```\n\nFull CLI guide: [CLI tutorial](https://docs.codecarbon.io/latest/tutorials/cli/).\n\n## Configuration\n\nYou can configure CodeCarbon using:\n\n- `~/.codecarbon.config` (global)\n- `./.codecarbon.config` (project-local)\n- `CODECARBON_*` environment variables\n- Python arguments (`EmissionsTracker(...)`)\n\nConfiguration precedence and examples: [configuration guide](https://docs.codecarbon.io/latest/how-to/configuration/).\n\n## How it works\n\nWe 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.\n\n![calculation Summary](docs/images/calculation.png)\n\nWe explain more about this calculation in the [**Methodology**](https://docs.codecarbon.io/latest/explanation/methodology/) section of the documentation.\n\n## Visualize\n\nYou can visualize your experiment emissions on the [dashboard](https://dashboard.codecarbon.io/) or locally with [carbonboard](https://docs.codecarbon.io/latest/how-to/visualize/).\n\n![dashboard](docs/images/dashboard.png)\n\n## Quick links\n\n| Section | Description |\n|---------|-------------|\n| [Quickstart](https://docs.codecarbon.io/latest/tutorials/first-tracking/) | Get started in 5 minutes |\n| [Installation](https://docs.codecarbon.io/latest/how-to/installation/) | Install CodeCarbon |\n| [CLI Tutorial](https://docs.codecarbon.io/latest/tutorials/cli/) | Track emissions from the command line |\n| [Python API Tutorial](https://docs.codecarbon.io/latest/tutorials/python-api/) | Track emissions in Python code |\n| [Comparing Model Efficiency](https://docs.codecarbon.io/latest/tutorials/comparing-model-efficiency/) | Measure carbon efficiency across ML models |\n| [API Reference](https://docs.codecarbon.io/latest/reference/api/) | Full parameter documentation |\n| [Framework examples (scikit-learn)](https://docs.codecarbon.io/latest/how-to/scikit-learn/) | Task-oriented ML framework examples |\n| [Methodology](https://docs.codecarbon.io/latest/explanation/methodology/) | How emissions are calculated |\n| [EcoLogits](https://ecologits.ai/) | Track emissions from GenAI API calls |\n\n## Links\n\n- [Main website](https://codecarbon.io) to learn why we do this.\n- [Dashboard](https://dashboard.codecarbon.io/) to see your emissions.\n- [Documentation](https://docs.codecarbon.io/) to learn how to use the package and our methodology.\n- [EcoLogits](https://ecologits.ai/) to track emissions from GenAI API calls (OpenAI, Anthropic, etc.).\n- [GitHub](https://github.com/mlco2/codecarbon) to look at the source code and contribute.\n- [Discord](https://discord.gg/GS9js2XkJR) to chat with us.\n\n## Contributing\n\nWe are hoping that the open-source community will help us edit the code and make it better!\n\nYou 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.\n\nCheck out our [contribution guidelines](https://github.com/mlco2/codecarbon/blob/master/CONTRIBUTING.md).\n\nFeel free to chat with us on [Discord](https://discord.gg/GS9js2XkJR).\n\n## Citation\n\nIf you find CodeCarbon useful for your research, you can find a citation under a variety of formats on [Zenodo](https://zenodo.org/records/11171501).\n\n\u003cdetails\u003e\n\u003csummary\u003eBibTeX\u003c/summary\u003e\n\n```tex\n@software{benoit_courty_2024_11171501,\n  author       = {Benoit Courty and\n                  Victor Schmidt and\n                  Sasha Luccioni and\n                  Goyal-Kamal and\n                  MarionCoutarel and\n                  Boris Feld and\n                  Jérémy Lecourt and\n                  LiamConnell and\n                  Amine Saboni and\n                  Inimaz and\n                  supatomic and\n                  Mathilde Léval and\n                  Luis Blanche and\n                  Alexis Cruveiller and\n                  ouminasara and\n                  Franklin Zhao and\n                  Aditya Joshi and\n                  Alexis Bogroff and\n                  Hugues de Lavoreille and\n                  Niko Laskaris and\n                  Edoardo Abati and\n                  Douglas Blank and\n                  Ziyao Wang and\n                  Armin Catovic and\n                  Marc Alencon and\n                  Michał Stęchły and\n                  Christian Bauer and\n                  Lucas Otávio N. de Araújo and\n                  JPW and\n                  MinervaBooks},\n  title        = {mlco2/codecarbon: v2.4.1},\n  month        = may,\n  year         = 2024,\n  publisher    = {Zenodo},\n  version      = {v2.4.1},\n  doi          = {10.5281/zenodo.11171501},\n  url          = {https://doi.org/10.5281/zenodo.11171501}\n}\n```\n\n\u003c/details\u003e\n\n## Contact\n\nFeel free to chat with us on [Discord](https://discord.gg/GS9js2XkJR).\n\nCodecarbon was formerly developed by volunteers from [**Mila**](http://mila.quebec) and the [**DataForGoodFR**](https://twitter.com/dataforgood_fr) community alongside donated professional time of engineers at [**Comet.ml**](https://comet.ml) and [**BCG GAMMA**](https://www.bcg.com/en-nl/beyond-consulting/bcg-gamma/default).\n\nNow CodeCarbon is supported by [**Code Carbon**](https://www.helloasso.com/associations/code-carbon), a French non-profit organization whose mission is to accelerate the development and adoption of CodeCarbon.\n\n### Star History\n\n[![Star History Chart](https://api.star-history.com/svg?repos=mlco2/codecarbon,lfwa/carbontracker,sb-ai-lab/Eco2AI,fvaleye/tracarbon,Breakend/experiment-impact-tracker\u0026type=Date)](https://star-history.com/#mlco2/codecarbon\u0026lfwa/carbontracker\u0026sb-ai-lab/Eco2AI\u0026fvaleye/tracarbon\u0026Breakend/experiment-impact-tracker\u0026Date)\n","funding_links":["https://github.com/sponsors/mlco2"],"readme_doi_urls":["https://doi.org/10.5281/zenodo.11171501"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["language-model","climate-change","natural-language-processing","transformer","bert","climate-science","climate-data","electricitymap","flax","speech-recognition"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/453","html_url":"https://ost.ecosyste.ms/projects/453"}