Session 4
Environmental impact
TLDR
- Recap Session 3
- Leseauftrag gemeinsam anschauen
- Datenbegriff klären
Recap Session 3
mündlich
Leseauftrag “Quantifying the Carbon Emissions of Machine Learning”
Overview
The paper discusses the environmental impact of training machine learning (ML) models, emphasizing the factors contributing to carbon emissions. It introduces the Machine Learning Emissions Calculator to estimate emissions and offers recommendations for mitigating ML-related carbon footprints.
Key Factors Affecting Carbon Emissions
- Energy Source & Location
- The carbon footprint of ML training varies based on the energy grid used by the cloud server.
- Regions reliant on fossil fuels have significantly higher emissions compared to those powered by renewables.
- Example: CO₂ emissions range from 20g CO₂eq/kWh (Quebec) to 736.6g CO₂eq/kWh (Iowa, USA).
- The carbon footprint of ML training varies based on the energy grid used by the cloud server.
- Computing Infrastructure & Training Time
- ML models require substantial computation, often involving multiple GPUs for extended periods.
- Hardware efficiency matters: newer GPUs and TPUs are more energy-efficient.
- Using pre-trained models and fine-tuning can reduce emissions compared to training from scratch.
- ML models require substantial computation, often involving multiple GPUs for extended periods.
ML Emissions Calculator
- Developed to estimate CO₂ emissions based on:
- Location of training server
- Type of hardware used
- Training duration
- Location of training server
- Uses publicly available data to ensure transparency and allow improvements over time.
Recommendations for Reducing Carbon Footprint
- Choosing Cloud Providers Wisely
- Google Cloud, Microsoft Azure, and AWS vary in sustainability efforts.
- Selecting carbon-neutral cloud providers or low-emission data centers can significantly reduce impact.
- Google Cloud, Microsoft Azure, and AWS vary in sustainability efforts.
- Selecting Data Center Locations
- Training in regions with renewable energy can lower emissions up to 40 times compared to fossil-fuel-powered locations.
- Reducing Wasted Resources
- Random search for hyperparameter tuning is more efficient than grid search.
- Avoid unnecessary training experiments through better planning and debugging.
- Random search for hyperparameter tuning is more efficient than grid search.
- Using Energy-Efficient Hardware
- TPUs and newer GPUs (e.g., TPU3) have significantly better FLOPS/Watt efficiency compared to CPUs.
Discussion & Challenges
- Global Load Balancing: A shift towards low-carbon data centers may still require fossil-fuel-powered backup servers.
- Transparency Issues: Lack of precise data on energy consumption from cloud providers limits accuracy.
- Inference Emissions: While the focus is on training, deploying models also contributes to emissions.
- Trade-offs: Balancing efficiency and scientific progress, especially in AI applications for climate solutions.
Conclusion
The paper highlights the growing energy demands of ML and provides a practical framework to quantify and mitigate its environmental impact. The authors advocate for transparency, efficiency in model training, and sustainable computing choices.
Was können wir tun?
TBD