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.

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Provides hands-on integration with ElectricityMaps API for querying global grid carbon intensity and Google Cloud's Carbon Footprint tool for measuring emissions across ML workloads. Teaches dynamic job routing strategies—selecting low-carbon data center regions for training and inference based on real-time electricity mix data rather than static geography. Covers end-to-end carbon accounting for cloud ML operations including training, inference, and storage, enabling developers to shift compute to cleaner energy windows.

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Jul 15, 2024

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