AI Carbon Footprint Calculator

Calculate Your Carbon Footprint

Select GPU: Training Hours: Inference Hours: Select Cloud Provider:

Environmental Impact

Car Equivalent to driving a car for X kilometers

Energy Consumption Equivalent to consuming X kWh of energy

Trees Equivalent number of trees needed to offset that amount of CO2: X trees

Trash Equivalent to burning X kg of trash

Reports Generated Till Now: 0

Developer Note

I am inspired by ML CO2 IMPACT website to calculate the impact of my serious use of GPU ML training. I wanted to calculate that impact, but what was truly missing in that website calculator is the interpretation of the CO2 impact for a normal human being. So, I have added analogies for a normal person to understand the fuel consumption equivalent and trash burning equivalent. Thanks again for visiting, and contact me if you have any suggestions or want to develop this further. The email is at the bottom below.

Thanks, Sincerely,
Mayur B. Ingole
Bangalore, India

Embrace the AI Revolution Responsibly

The AI revolution is a cornerstone of progress and innovation. However, with great computing power comes great responsibility. We must ensure that our advancements in AI do not come at an unacceptable cost to the environment. Here are key practices to help us achieve this balance:

1. Choose Your Cloud Provider Wisely

Not all cloud providers are equal in their commitment to sustainability. Some invest more in clean energy, purchase carbon offsets, and engage in environmentally friendly practices. Review their sustainability commitments carefully and choose a provider that aligns with your values.

2. Select the Most Sustainable Region

Energy sources vary greatly between regions. If possible, choose a data center located in a region that relies more on renewable energy sources. This can significantly reduce the carbon footprint of your computations.

3. Offset Your Carbon Emissions

Many platforms offer straightforward ways to purchase carbon offsets. Consider investing in offset projects to balance out your carbon footprint. You can also advocate for such practices within your organization and personal life, such as offsetting flights or other travel-related emissions.

4. Avoid Excessive Hyperparameter Tuning

Grid search for hyperparameters can lead to significant carbon emissions due to the large number of experiments run. When possible, use more efficient methods such as random search or Bayesian optimization to minimize the computational resources required.

5. Opt for Clean Energy

Wherever possible, choose energy sources that are clean and sustainable. This could mean selecting green energy options provided by your organization or advocating for renewable energy policies within your community.

6. Advocate for Transparency

Push for transparency in reporting the carbon footprint of AI models. Include detailed emissions information in publications, reports, and blog posts. This practice not only raises awareness but also encourages others to adopt more sustainable practices.

7. Optimize Resource Utilization

Ensure that your computing resources are used efficiently. Avoid idle or unnecessary computations and optimize your code to reduce the overall environmental impact. Responsible computing means not just the reduction of emissions but also avoiding wastage of valuable resources.

By following these practices, we can support the advancement of AI while also preserving our environment for future generations. Together, let’s make sure our innovations are as sustainable as they are groundbreaking.

Quantifying the Carbon Emissions of Machine Learning Research Paper

For a deeper understanding of the environmental impact of machine learning, we recommend reading the research paper titled "Quantifying the Carbon Emissions of Machine Learning" by Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandres. The paper provides insights into key factors affecting carbon emissions, including:

The location of the server and its energy grid
The length of the training procedure
The hardware used for training
The paper introduces a Machine Learning Emissions Calculator to help understand these impacts
It also suggests concrete actions for individuals and organizations to reduce their carbon footprint

CodeCarbon: Estimating and Tracking Carbon Emissions

CodeCarbon is a Python package designed to estimate the carbon emissions of your computer programs. By calculating your hardware’s electricity consumption (GPU, CPU, RAM) and applying the carbon intensity of your region, CodeCarbon helps you understand the environmental impact of your computations.

Here’s a quick guide to get started:

Quickstart 🚀

Installation 🔧

Install CodeCarbon using one of the following methods:

From PyPI repository: pip install codecarbon
From Conda repository: conda install -c conda-forge codecarbon

Start to Estimate Your Impact 📏

Initialize your experiment with:

! codecarbon init

Store the experiment ID in a .codecarbon.config file:

[codecarbon]
        log_level = DEBUG
        save_to_api = True
        experiment_id = 2bcbcbb8-850d-4692-af0d-76f6f36d79b2 #the experiment_id you get with init

Monitoring Your Machine 💻

Track emissions independently using:

codecarbon monitor

In Your Python Code 🐍

Track emissions for specific functions with:

from codecarbon import track_emissions
        
        @track_emissions()
        def your_function_to_track():
            # your code

For more options and detailed usage, refer to the CodeCarbon Documentation.