Reducing your Climate Impact when Training ML Models

Learn how to measure a machine learning model's carbon footprint and practice strategies that can help shrink the energy involved in training these models.

Author:

• Mel Hanna, Climate Change AI: mel@climatechange.ai

Originally presented at Climate Change AI Summer School 2023

Access this tutorial

We recommend executing this notebook in a Colab environment to gain access to GPUs and to manage all necessary dependencies.

Estimated time to execute end-to-end: 10 minutes

Contribute to this tutorial

Please refer to these GitHub instructions to open a pull request via the "fork and pull request" workflow.

Pull requests will be reviewed by members of the Climate Change AI Tutorials team for relevance, accuracy, and conciseness.

Climate Change AI Tutorials

Check out the tutorials page on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change.

License

Usage of this tutorial is subject to the MIT License.

Cite

Plain Text

Hanna, M. (2024). Reducing your Climate Impact when Training ML Models [Tutorial]. In Climate Change AI Summer School. Climate Change AI. https://doi.org/10.5281/zenodo.11624916

BibTeX

@misc{hanna2024reducing,
  title={Reducing your Climate Impact when Training ML Models},
  author={Hanna, Melanie},
  year={2024},
  organization={Climate Change AI},
  type={Tutorial},
  doi={https://doi.org/10.5281/zenodo.11624916},
  booktitle={Climate Change AI Summer School},
  howpublished={\url{https://github.com/climatechange-ai-tutorials/tracking-ml-emissions}}
}