" MicromOne: Save Energy with Your Machine Learning Code: Using CarbonCode for ML and Other Tools

Pagine

Save Energy with Your Machine Learning Code: Using CarbonCode for ML and Other Tools

As machine learning becomes more widely adopted, the environmental impact of training models—especially large ones—has grown significantly. While ML offers incredible potential, it also requires substantial computing resources, leading to high energy consumption and carbon emissions.

Luckily, there are tools and practices that can help you measure, monitor, and reduce the carbon footprint of your ML experiments. One of the most promising tools is CarbonCode for ML.

Let’s dive into what it is, how to use it, and explore some similar tools you can integrate into your workflow to build more sustainable AI.

What is CarbonCode for ML?

CarbonCode for ML is a lightweight Python library that helps you track and reduce the energy consumption and CO₂ emissions of your machine learning training jobs. It gives you insights into how green your code is, so you can make smarter choices about hardware, location, and optimization.

It’s similar in purpose to tools like CodeCarbon and experiment trackers like Weights & Biases with sustainability plugins.

How to Use CarbonCode for ML

You can get started with just a few lines of code. Here's a basic example:

pip install carboncode
from carboncode import CarbonMonitor

monitor = CarbonMonitor(project_name="my_ml_project")

monitor.start()

# Your ML code goes here
train_my_model()

monitor.stop()
monitor.report()

This will give you a summary of:

  • Energy usage (in kWh)

  • Estimated CO₂ emissions (g or kg CO₂)

  • Time and hardware used

You can also log this data for long-term tracking and use it to compare different experiments or model versions.

Why It Matters

Machine learning can be power-hungry. Training a single large transformer model can emit as much carbon as five cars over their lifetime. Monitoring your training jobs helps:

  • Identify hotspots in your workflow

  • Choose more efficient hardware or cloud regions

  • Optimize your code (e.g., reduce batch size, precision tuning, etc.)

  • Build sustainable and responsible AI

Other Tools to Consider

Here are some other tools that align with the same mission:

CodeCarbon

  • Open-source tool from MLCO2

  • Works with most ML frameworks

  • Logs emissions to a dashboard or CSV

  • Can be used with cloud environments

GitHub: https://github.com/mlco2/codecarbon

Carbontracker

  • Developed by the University of Oslo

  • Tracks energy and carbon in real-time

  • Includes GPU/CPU temperature and power info

GitHub: https://github.com/lfwa/carbontracker

EcoML (by Hugging Face)

  • Measures emissions of Transformers during training and inference

  • Offers public leaderboard with carbon impact

Website: https://huggingface.co/efficiency

Pro Tips for Greener ML

  • Use pre-trained models when possible

  • Try early stopping to avoid unnecessary training epochs

  • Choose efficient cloud regions (e.g., ones powered by renewable energy)

  • Prefer batch inference over single predictions

  • Run your code at off-peak hours when grids are cleaner

Final Thoughts

Sustainable AI isn't just a buzzword—it's a necessary shift for the future of machine learning. With tools like CarbonCode for ML, it’s now easier than ever to take responsibility for your carbon impact, without sacrificing performance or innovation.