Dear Travis, 

In case you missed it, last week we launched Carbon Aware Computing for GenAI Developers, a short course made in collaboration with Google Cloud and taught by Nikita Namjoshi, Developer Advocate at Google Cloud and Google Fellow on the Permafrost Discovery Gateway.

Training, fine-tuning, and serving generative AI models can be demanding in terms of compute and energy. But these processes don't have to be as carbon-intensive if you choose when and where to run them in the cloud. In this course, you’ll learn how to perform model training and inference jobs with cleaner, low-carbon energy in the cloud.

Explore how to measure the environmental impact of your machine learning jobs and how to optimize their use of clean electricity, and:

• Query real-time electricity grid data: Explore the world map, and based on latitude and longitude coordinates, get the power breakdown of a region (e.g. wind, hydro, coal etc.) and the carbon intensity (CO2 equivalent emissions per kWh of energy consumed).
• Train a model with low-carbon energy: Select a region that has a low average carbon intensity to upload your training job and data. Optimize even further by selecting the lowest carbon intensity region using real-time grid data from ElectricityMaps.
• Retrieve measurements of the carbon footprint for ongoing cloud jobs.
• Use the Google Cloud Carbon Footprint tool, which provides a comprehensive measure of your carbon footprint by estimating greenhouse gas emissions from your usage of Google Cloud.

Throughout the course, you'll work with ElectricityMaps, a free API for querying electricity grid information globally. You'll also use Google Cloud to run a model training job in a cloud data center that is powered by low-carbon energy.

Get started, and learn how to make more carbon-aware decisions as a developer!

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