A curated list of trustworthy deep learning papers. Continually updating...
🌱 EcoLogits tracks the energy consumption and environmental footprint of using generative AI models through APIs.
Track the carbon footprint of your Claude Code sessions, in the terminal and the Claude desktop app
A curated list of awesome Green AI resources and tools to assess and reduce the environmental impacts of using and deploying AI.
Desktop app for DeepSeek Harness with a live energy/carbon console: measure local GPU inference, estimate cloud APIs via published coefficients, alert on waste
[ACM e-Energy'24] Code and data for "FedZero: Leveraging Renewable Excess Energy in Federated Learning"
A carbon-aware ML training scheduling system that optimises machine learning workloads using real-time UK grid carbon intensity data. It simulates ML training, finds optimal low-carbon execution windows, and visualises carbon savings through an interactive multi-page Streamlit analytics dashboard.
Use local Large Language Models (LLMs) while monitoring energy usage.
Custom implementations of backpropagation-free deep learning algorithms, rigorously benchmarked against tuned baselines with hardware-validated energy, time, memory, and CO₂e measurements.
Distributed LLM pretraining during renewable curtailment windows 🌱
A curated list of Green AI resources.
🌱 Estimate the environmental footprint of GenAI with EcoLogits API
A machine learning pipeline taking you from raw data to fully trained machine learning model - from data to model (d2m).
GATorch is a tool seamlessly integrated with PyTorch that enables ML developers to generate an energy consumption report. By attaching your model, the tool automatically tracks the energy consumption of your model's training and generates graphs and plots to gain in-depth insights into the energy consumption of your model.
Know the true energy cost of your code
Research-ready and production-friendly neural network pruning for PyTorch—transparent methods, reproducible baselines, and deployment metrics to compress models for real-world use.
Measure and audit intelligence gained per joule spent. Proof-of-Learning prototype.
A Grafana panel to monitor the carbon footprint of AWS, GCP and Azure instances. Maintains a list of data centres and their energy sources, and computes CO2e emissions by sampling energy consumption of CPU, GPU and DRAM.
Kali-Lite — IA locale et frugale • Ollama • Linux/macOS • GPL-2.0-only
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