Training ML models has an environmental cost that most practitioners do not measure. A model trained during peak grid hours, when coal and gas plants are meeting high demand - can emit significantly more CO2 than the same model trained during off-peak hours when renewables dominate the grid. The carbon intensity of electricity varies by a factor of 2–5x throughout the day, but most training pipelines ignore this entirely.
Carbon-Aware Model Training Pipeline is a PyTorch-based training pipeline that monitors real-time electricity carbon intensity, delays training until a low-carbon window is available, reduces GPU memory footprint through gradient accumulation, and tracks CO2 emissions throughout the training process using CodeCarbon - with a comparison report that quantifies the carbon savings against a baseline run.
Features
Carbon-Aware Scheduling - real-time carbon intensity monitoring with smart training delays until low-carbon windows are detected.
Gradient Accumulation - reduces GPU memory footprint while maintaining effective batch size.
