Data extraction for Identifying architectural design decisions for achieving green ML serving
-
Updated
Jan 21, 2024
Data extraction for Identifying architectural design decisions for achieving green ML serving
Replication Package
Energy consumption of ML inference with Runtime Engines
Code and Experimental Package attached to the article "Greener Machine Learning Computing with Intel AI Acceleration"
Replication package for the data analysis of the master thesis "An analysis of modeling and training decisions for greener computer vision systems"
Guidelines to deploy AI models in different cloud providers aligned with green AI goals.
A curated list of Green AI resources.
Mapping the Landscape of AI, ML, and DL in Climate Change Research
A machine learning pipeline taking you from raw data to fully trained machine learning model - from data to model (d2m).
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.
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.
Implementation and evaluation of "FedZero: Leveraging Renewable Excess Energy in Federated Learning"
A curated list of awesome Green AI resources and tools to reduce the environmental impacts of using and deploying AI.
🌱 EcoLogits tracks the energy consumption and environmental footprint of using generative AI models through APIs.
A curated list of trustworthy deep learning papers. Daily updating...
Add a description, image, and links to the green-ai topic page so that developers can more easily learn about it.
To associate your repository with the green-ai topic, visit your repo's landing page and select "manage topics."