Ever think your machine-learning model leaves a carbon footprint? It might seem surprising, but training these powerful tools requires a lot of computational muscle, and that muscle uses energy. In our AI-powered world, considering the environmental impact of our models is no longer optional.

What is a Carbon footprint anyway?

Carbon footprint is the amount of carbon dioxide(CO2) emissions associated with all the activities of a person or other entity (e.g., building, corporation, country, etc.). It includes direct emissions, such as those that result from fossil-fuel combustion in manufacturing, heating, and transportation, as well as emissions required to produce the electricity associated with goods and services consumed.

The carbon footprint concept also often includes the emissions of other greenhouse gases, such as methane, nitrous oxide, or chlorofluorocarbons (CFCs).

How do I measure the Carbon Footprint of my ML model?

Lucky for us there are Python libraries available for measuring the carbon footprint of our ML models:

• Created by Yoshua Bengio, CodeCarbon is a Python library that makes estimating the carbon emissions produced when your code runs easy. This user-friendly tool measures the power usage of your computer's CPU, GPU, and RAM to calculate the code's environmental footprint.
• Another helpful Python library is CarbonTracker. It goes beyond just estimating CO2 emissions. It factors in both the energy used by your hardware and the source of that energy's carbon intensity to give you a more precise picture.

Measuring the Carbon Footprint of your ML model using CarbonTracker

Let’s say we are training a Neural Network we can instantiate an instance of the CarbonTracker class and use it to track our model training process in each epoch to compute the amount of carbon emitted by our program.

Install CarbonTracker

pip install carbontracker

Your model

You will need to import the training components of your machine-learning model into the tracker file. Let's take a look at a dummy neural network in a file called neural_net.py:

def load_dataset(source, split="test"):
    pass

def build_model(data_set):
    N = 2100
    for i in range(N):
        for j in range(N):
            for k in range(N):
                three_sum = i + j + k

if __name__== "__main__": build_model()

The code example below shows how the training components are imported from the neural_net module and called by the tracker.

Code

from carbontracker.tracker import CarbonTracker
from neural_net import load_dataset, build_model

max_epochs = 100

tracker = CarbonTracker(epochs=max_epochs)

dataset = load_dataset("imdb", split="test")

# Training loop.
for epoch in range(max_epochs):
    tracker.epoch_start()
    
    # Your model training.
    model = build_model(dataset)

    tracker.epoch_end()

# Optional: Add a stop in case of early termination 
# before all monitor_epochs has been monitored to
# ensure that actual consumption is reported.
tracker.stop()

As you can see once you setup the CarbonTracker tracker object, the rest is straightforward.

Output

Beyond Measurement: Optimizing Code for a Sustainable Future

Measuring the carbon footprint of machine learning models is crucial, but it's just the beginning. Here are practical steps we can take to optimize code execution and minimize emissions for a greener future:

• By optimizing your code, you can reduce unnecessary computations and the resources your model uses. Think of it like making your model lighter on its feet. Techniques like model compression, pruning, and quantization can help you minimize the computational power needed without sacrificing performance.
• Using energy-efficient hardware, like GPUs with lower power consumption, can significantly reduce the energy your code guzzles during execution. It's like choosing a fuel-efficient car for your machine-learning tasks!
• The size and complexity of your machine learning model directly impact its environmental footprint. Think of it like choosing the right engine for your car. Bigger, more complex models can be more powerful, but they also require more energy to train and run. For a greener approach, consider using models that strike a balance between accuracy and computational complexity. Often, smaller models can achieve similar results while requiring less energy. This translates to lower carbon emissions without sacrificing performance!
• Training machine learning models often requires vast amounts of data. But what if you could achieve similar results with less? Data efficiency techniques can help you do just that. These techniques involve preprocessing your data to reduce its size while maintaining its effectiveness for training.

Green Machine Learning: A Continuous Journey

We've explored several ways to optimize your machine-learning workflow for a lower carbon footprint. But remember, as with other software applications, it’s about continuous improvement. Embrace a culture of continuous optimization, continue to refine your code and regularly assess carbon emissions of your machine learning workflows using tools like CodeCarbon and CarbonTracker.