A Satellite Semantic Segmentation Project using Unet and Attention Unet with Pytorch,
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Mar 20, 2024 - Jupyter Notebook
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codecarbonHere are 39 public repositories matching this topic...Language: All
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VS Code extension for CodeCarbon
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.
A tool to measure and compare the energy consumption of code variants.
java python3 code-smells sustainable-software green-computing carbon-footprint power-consumption-measurement green-software green-coding codecarbon
Energy Consumption of various Machine Learning and Deep Learning Models using codecarbon
Apache Airflow plugin that tracks the energy utilization and carbon footprints of data workflows.
Make an impact with a single API call — plant trees, clean oceans, capture CO₂, and donate to global causes.
python api sdk sustainability climate ocean offset donations carbon sustainable-software plant-trees codecarbon carbon-offset 1clickimpact capture-carbon
Make an impact with a single API call — plant trees, clean oceans, capture CO₂, and donate to global causes.
javascript sdk sustainability climate clean-code carbon sustainable-software plant-trees carbon-offsets codecarbon carbon-offset makeimpact 1clickimpact clean-ocean
Calculateur d'impact environnemental de l'IA générative : carbone (gCO₂e), énergie (kWh), eau, ADPe. 79 publications HAL/arXiv + 87 outils open source (CodeCarbon, EcoLogits, Scaphandre). Méthodologie ACV pour LLM.
hpc artificial-intelligence energy-consumption carbon-footprint co2-emissions water-footprint environmental-impact life-cycle-assessment responsible-ai green-ai green-software codecarbon ecoconception sustainable-ai sustainable-computing llm frugal-ai ai-impact ecologits carbon-awareness
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
Secure AWS workload profiler with Terraform, CodeCarbon, CloudWatch, and lease-aware auto-stop safety.
Codebase for the MLCost application developed for my thesis for the Telecommunications Enginnering bachelor, Universidad Rey Juan Carlos
Public submission hub for PUMA local-LLM benchmark results
benchmarking json-schema open-data project-management benchmarks benchmarking-framework project-management-software project-management-tool codecarbon llms llm-inference llm-tools ollama llm-evaluation
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.
python carbon-emissions carbon-footprint co2-emission energy-transition carbon-intensity power-usage green-software codecarbon co2-footprint green-software-development carbon-emission-calculator
Carbon-aware machine learning benchmarking framework evaluating predictive performance alongside energy consumption and CO₂ emissions.
machine-learning sustainability code data-analytics carbon-emissions model-evaluation green-ai streamlit data-analytics-project codecarbon environmental-ai ml-benchmarking
The system tracks the emissions of a given recommendation algorithm on a given dataset.
sustainability recommendation-system recommender-system emissions sustainable-consumption sustainable-software emissions-co2 recbole codecarbon
End-to-end ML pipeline for predicting house prices using the Ames Housing dataset.
Reproducible ML pipeline for crop recommendation — FastAPI REST API, DVC data versioning, MLflow experiment tracking, Docker Compose deployment, Great Expectations validation, Locust load testing, Prometheus/Grafana monitoring, GitHub Actions CI/CD. Deployed on AWS EC2
docker machine-learning continuous-integration docker-compose grafana prometheus aws-ec2 locust random-forest-classifier dvc mlops mlflow fastapi streamlit great-expectations codecarbon huggingface-spaces dagshub
One of my beloved academic works that is exploring the roots and effects of an unequilibred trade-off between development expenses and gained performance has its basis residing in the current repository, which provides a full implementation of a DL model that can carry out semantic segmentation tasks while staying compact (12.2 M parm; 39 MB).
deep-learning slurm hyperparameter-optimization unet hyperparameter-tuning semantic-segmentation data-augmentation slurm-cluster derandomzation hyperparameter-search efficientnet cityscapes-dataset weights-and-biases super-computer efficientnet-b0 codecarbon efficientnetb3 efficientnetb1 u-net-image-segmentation
An end-to-end machine learning project predicting employee burnout risk (No Risk / At Risk / Burned Out) using 8,500 samples. Covers EDA, feature engineering, SMOTE-Tomek imbalance handling, 5 classifiers with GridSearchCV optimization, and carbon emission tracking via CodeCarbon.
python machine-learning time random-forest classification logistic-regression decision-trees knn-classification xgboost-classifier codecarbon k-fold-cv
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