This package logs the carbon emissions of machine learning models.
Training and running industrial machine learning models requires significant computing resources. The environment impacts are often overlooked if not neglected. To make ML engineers more aware about the carbon footprint of their projects we created this package.
The package seamlessly integrates the CodeCarbon_package into the MLFlow MLOps package. Extending the logging capabilities of the latter with the carbon tracking methodology of the former.
To get started, use the package manager pip to install the mlflow-emissions-package:
pip install mlflow-emissions-package
Make sure that you have an mlflow client running:
mlflow ui
By default, the tracking uri is http://127.0.0.1:5000
To log your emissions you need to provide the tracker with the uri and the name of the experiment. If the experiment does not exist then the tracker will create a new experiment with the given name. Example
from pprint import pprint
import numpy as np
from sklearn.linear_model import LinearRegression
from mlflow_emissions_sdk.experiment_tracking_training import EmissionsTrackerMlflow
# Create a dictionary
tracker_info = {
"tracking_uri" : "http://127.0.0.1:5000",
"experiment_name": "test_name",
"run_name": "run_name",
"flavor" : "sklearn"
}
# Prepare training data
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
# Pick a model
model = LinearRegression()
# Instatiates the tracker
runner = EmissionsTrackerMlflow()
runner.read_params(tracker_info)
# Starts the emissions tracking
runner.start_training_job()
# Training the model
history = model.fit(X, y)
# Ends the tracking
runner.end_training_job()This project is licensed under the MIT License. You are free to use, modify, and distribute this software as per the terms of the license. If you find this project helpful, please consider giving it a star on GitHub.
A special thanks to Emmanuel for his supervision throughout this project.