Make an impact with a single API call — plant trees, clean oceans, capture CO₂, and donate to global causes.

Energy Consumption of various Machine Learning and Deep Learning Models using codecarbon

This project develops forecasting models for monitoring forest health, focusing on Larch Casebearer damage using Yolov8 models, with a focus on evaluating the environmental impact of the training process

Codebase for the MLCost application developed for my thesis for the Telecommunications Enginnering bachelor, Universidad Rey Juan Carlos

End-to-end ML pipeline for predicting house prices using the Ames Housing dataset.

Tecniche di early stopping sostenibili per modelli di raccomandazione, mirate a ridurre le emissioni di CO2 durante l’addestramento senza compromettere in maniera significativa le performance. Basato su RecBole e CodeCarbon.

A simple and easy-to-use Python code resource consumption monitoring program based on psutil and CodeCarbon.

MakeImpact is a Python SDK that helps you integrate environmental actions into your applications. 🌱 With simple commands, you can easily contribute to sustainability efforts, like planting trees and cleaning oceans. 🐙

This study analyzes the energy efficiency (E(n)) of MergeSort and QuickSort on Apple M2 architecture. While QuickSort is faster for large datasets (N=50,000), it consumes significantly more energy (3.75 J) than MergeSort (2.50 J) due to higher instantaneous power draw. It highlights the need for energy-aware algorithm selection.

Example project built as a tutorial on how to monitor the emissions and energy consumption of a Python application, using AWS CloudWatch to increase the visibility of these statistics using Custom Metrics and Dashboards.

Pytest plugin for tracking carbon emissions