In an age where sustainability is a key focus, understanding the carbon footprint of our technology and projects has become more important than ever. The Eco2AI library is a powerful Python tool designed explicitly for tracking CO₂ emissions in relation to your machine learning models. In this article, we’ll guide you through the installation process, basic usage examples, and provide troubleshooting tips to ensure smooth sailing while utilizing this innovative tool.

About Eco2AI

Eco2AI is a Python library that monitors energy consumption of CPU and GPU devices while estimating equivalent carbon emissions based on regional coefficients. By simply integrating a few lines of code, you can keep track of emissions logged in a local file, including key details like project name, start time, and power consumption. This way, you can measure the impact of your computational needs on the environment effectively.

Installation

To install the Eco2AI library, use the following command in your terminal or command prompt:

pip install eco2ai

Usage Examples

Utilizing Eco2AI is as simple as integrating it into your Python code. Below, we illustrate a couple of usage examples to get you started:

Basic Usage

The most straightforward way to use Eco2AI is by following this script:

import eco2ai

tracker = eco2ai.Tracker(project_name='YourProjectName', 
                          experiment_description='Training your model')
tracker.start()

# Your GPU (or CPU) calculations go here

tracker.stop()

Decorator Usage

If you prefer a more automated way, Eco2AI also supports decorators to track emissions without manually starting and stopping the tracker. For example:

from eco2ai import track

@track
def train_func(model, dataset, optimizer, epochs):
    # Your training code here

train_func(your_model, your_dataset, your_optimizer, your_epochs)

Additionally, you can customize the tracker settings as follows:

tracker = eco2ai.Tracker(project_name='YourProjectName', 
                          experiment_description='Training your model', 
                          file_name='emission.csv')
tracker.start()

# Your calculations here

tracker.stop()

Important Notes

It’s crucial to define your country to utilize accurate emission coefficients. If the country cannot be identified, the library defaults to a global average emission coefficient of 436.529 kg/MWh.

Troubleshooting Tips

While Eco2AI is relatively easy to use, you may run into issues or have questions. Here are some common troubleshooting tips:

• If the tracker isn’t logging data, ensure you’ve named your project and provided a description during instantiation.
• Make sure to create a new tracker instance for every separate calculation session. Not doing so may result in overlap in data.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

If you encounter any additional problems, consider reviewing the sber_emission_tracker_guide.ipynb for more examples and notes.

Conclusion

Tracking CO₂ emissions is an essential practice in today’s environmentally conscious landscape. Eco2AI provides a straightforward solution for keeping tabs on your computational impact. As you embark on your journey to sustainable AI practices, remember that proper implementation and awareness of your project settings can lead to meaningful changes.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.