CO2 Emissions and the 🤗 Hub: Leading the Charge

Summary

Training and deploying machine learning models produces greenhouse gas emissions determined by infrastructure runtime, hardware type, and energy grid carbon intensity. Hugging Face introduced mechanisms to measure, report, and filter models based on their carbon footprint to improve ecological transparency. Using the huggingface_hub library, practitioners can query HfApi.list_models with an emissions_thresholds parameter to filter models by maximum or minimum carbon emissions measured in grams. In addition, installing the codecarbon package enables the Transformers Trainer to automatically attach CodeCarbonCallback and log training emissions to an emissions.csv file. Users can then publish these measured values directly into model card metadata under co2_eq_emissions for public tracking.

Takeaways

1. 1

   The huggingface_hub HfApi.list_models method supports an emissions_thresholds parameter to search models based on minimum or maximum carbon emission gram ranges.

2. 2

   Installing codecarbon allows the Transformers Trainer to automatically load CodeCarbonCallback and track emissions during training.

3. 3

   Emissions data logged during training runs is written to an emissions.csv file and can be published in model cards under the co2_eq_emissions metadata field.