Green Ai Python Packages

codegreen

Know the true energy cost of your code

genai-otel-instrument

GenAI OpenTelemetry Auto-Instrumentation Library A comprehensive wrapper for automatic instrumentation of LLM/GenAI applications Supports all major LLM providers and MCP (Model Context Protocol) tool calls

caml-tc

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.

evomem

Agent Memory Kit System: Capture every correction and interaction to build your own SLM from day one

adaptive-sparse-training

Adaptive Sparse Training (AST): 92.1% ImageNet-100 accuracy with 61% energy savings and zero degradation. Production-ready implementations for energy-efficient deep learning with ResNet50 and modern architectures.

green-ai-profiler

Open-source PyTorch AI model profiler for latency, memory, energy (NVML/RAPL), and layer-wise bottlenecks. Measure green AI / ML efficiency no fabricated joules.

gatorch

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.