CarbonTracker has released its latest v1.2.0 version, packed with new features and improvements for carbon monitoring enthusiasts
Developed by the University of Copenhagen (Department of Computer Science), CarbonTracker is a tool designed to monitor and forecast the energy consumption and carbon footprint associated with training deep learning models. This tool is part of the framework of the SustainML project, dedicated to reducing CO2 emissions and advancing sustainable practices in AI.
Developed by Pedram Bakh, this release introduces many enhancements to make tracking and managing your carbon footprint easier and more informative than before.
Highlights of the Release:
🌿New CarbonTracker CLI Tool: CarbonTracker now comes equipped with a Command Line Interface (CLI) tool, streamlining the process of monitoring and managing the carbon emissions of your Python scripts.
🌿 Transition from CO2Signal API to the ElectricityMaps API.
🌿 OS X Support for Apple Silicon Chips.
🌿 Enhanced Feedback with Verbose Setting.
🌿 Decimal Precision Update.
🌿 Enhanced Carbon Intensity Estimation Notifications.
🌿 Performance Optimization.
🌿 And additional Updates.
Want to know more? Visit the CarbonTracker GitHub repository.
Want to know how? Take a look at this example:
All the SustainML participants: DFKI, Inria, IBM, University of Copenhagen, UpMem, Technische Universität Kaiserslautern and eProsima.
MORE INFORMATION ABOUT SUSTAINML:
For any questions, please get in touch with
This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070408.