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.
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Updated
Jul 8, 2024
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
Codebase for the MLCost application developed for my thesis for the Telecommunications Enginnering bachelor, Universidad Rey Juan Carlos
A Satellite Semantic Segmentation Project using Unet and Attention Unet with Pytorch,
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
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