Estimate ecological footprint of AI models

Dev Tools

AIPowerMeter – A library that enables monitoring energy usage of machine learning programs, using RAPL for the CPU and nvidia-smi for the GPU.
CodeCarbon – Estimates the carbon footprint during the training of machine learning models. (CodeCarbon)
EnergyMeter – A Python module combining pyRAPL, NVIDIA-SMI, and eBPF to estimate energy consumption of CPU, memory, GPU, and storage on Linux with only three lines of code.
pyJoules – A Python library that uses hardware measurement tools (Intel RAPL, NVIDIA GPU tools, etc.) to measure device energy consumption. (pyJoules)
EcoLogits – A Python library that tracks the energy consumption and environmental footprint of using generative AI models through APIs. (EcoLogits)
Carbontracker – A tool written in Python to predict the carbon footprint of training of models (Paper: https://doi.org/10.48550/arXiv.2007.03051

Green Algorithms – Provides an online calculator to estimate the carbon footprint of workloads.
ML CO2 Impact – Provides an online calculator to estimate the carbon footprint of AI workloads.
EcoLogits Calculator – Provides an online calculator to estimate the environmental footprint of generative AI models.
ML.ENERGY Leaderboard – Provides a leaderboard for open source LLMs regarding their energy consumption.
Green Coding AI – Measures the energy consumption of the inference of open source LLMs. By: Green Coding Solutions