mlco2 / codecarbon
Track emissions from Compute and recommend ways to reduce their impact on the environment.
☆1,244Updated this week
Alternatives and similar repositories for codecarbon:
Users that are interested in codecarbon are comparing it to the libraries listed below
- Track and predict the energy consumption and carbon footprint of training deep learning models.☆417Updated 2 months ago
- ML has an impact on the climate. But not all models are born equal. Compute your model's emissions with our calculator and add the result…☆216Updated 2 months ago
- ☆280Updated last year
- Measure energy and carbon consumption of software☆189Updated this week
- 🌱 EcoLogits tracks the energy consumption and environmental footprint of using generative AI models through APIs.☆117Updated last week
- A specification that describes how to calculate a carbon intensity for software applications.☆269Updated 4 months ago
- Référentiel d'évaluation data science responsable et de confiance☆73Updated 4 months ago
- ⚡ Energy consumption metrology agent. Let "scaph" dive and bring back the metrics that will help you make your systems and applications m…☆1,688Updated 2 months ago
- Python package to monitor the power consumption of any algorithm☆46Updated 2 years ago
- 💾 Boavizta.org Data repository☆120Updated last year
- PowerAPI is a Python framework for building software-defined power meters.☆207Updated this week
- Reduce the environmental footprint of your software programs with SonarQube☆171Updated this week
- skops is a Python library helping you share your scikit-learn based models and put them in production☆463Updated this week
- Green Software Practitioner course☆81Updated last month
- An online open-source database of software patterns reviewed and curated by the Green Software Foundation across a wide range of categori…☆90Updated this week
- Impact Framework☆165Updated this week
- A curated list of awesome Green AI resources and tools to assess and reduce the environmental impacts of using and deploying AI.☆63Updated this week
- Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations☆579Updated 2 months ago
- Notebook and sources for calculating the Cloud Carbon Coefficients used in the Cloud Carbon Footprint project.☆39Updated last year
- Croissant is a high-level format for machine learning datasets that brings together four rich layers.☆499Updated last week
- Reduce the environmental footprint of your software applications with this cutting-edge sonarQube plugin☆63Updated last year
- Transform datasets at scale. Optimize datasets for fast AI model training.☆406Updated last week
- Compilation of high-profile real-world examples of failed machine learning projects☆722Updated 7 months ago
- Kickstart your MLOps initiative with a flexible, robust, and productive Python package.☆950Updated last month
- Impact Framework models owned and maintained by the GSF☆25Updated 7 months ago
- Algorithms for explaining machine learning models☆2,436Updated last month
- 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models☆2,768Updated 3 weeks ago
- A collaborative project to collect and report open-data on the energy consumption of servers.☆35Updated 2 months ago
- Software design principles for machine learning applications☆330Updated last month
- The WeightWatcher tool for predicting the accuracy of Deep Neural Networks☆1,516Updated 4 months ago