ksm26/Carbon-Aware-Computing-for-GenAI-Developers
Learn to optimize machine learning tasks for environmental sustainability. Discover how to use real-time electricity data and low-carbon energy sources for model training and inference, reducing the carbon footprint of your cloud operations.
This project helps AI developers reduce the environmental impact of their machine learning workflows. It provides methods to query real-time electricity grid data to identify low-carbon energy sources and then direct model training and inference jobs to cloud regions powered by cleaner electricity. The end result is a lower carbon footprint for AI development and operations.
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Use this if you are an AI developer looking to minimize the carbon emissions of your cloud-based machine learning model training and inference.
Not ideal if your primary goal is cost optimization or performance enhancement, as this project focuses specifically on environmental sustainability.
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Jul 15, 2024
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