Mini workshop to provide a peak of what’s happening under the hood of models currently at the frontier of the AI revolution, and about how we can track the emissions of our own code.

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

A Satellite Semantic Segmentation Project using Unet and Attention Unet with Pytorch,

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

Diving into the world of Tracking CO2 Emissions from our software or code. Code Carbon is a lightweight open-source Python Library that lets you track the Co2 emissions produced by running code.

The system tracks the emissions of a given recommendation algorithm on a given dataset.

[EARLY ACCESS] vscode extension for codecarbon

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

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