Dear learner,
Today we’re launching Carbon Aware Computing for GenAI Developers, a new 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.
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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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Retrieve real-time data on global energy mixes and carbon intensity from the ElectricityMaps API. Identify power grids that produce electricity from low-carbon sources, such as hydro, nuclear, wind, and solar power.
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Run a machine learning training job using low-carbon electricity by re-directing training tasks to cloud server locations selected based on their average and real-time carbon intensity measurements.
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Analyze the carbon footprint of sample Google Cloud usage data, including machine learning training, inference, storage, and other API activities.
| Lesson | Video | Code |
|---|---|---|
| Introduction | video | |
| The Carbon Footprint of Machine Learning | video | |
| Exploring Carbon Intensity on the Grid | video | code |
| Training Models in Low Carbon Regions | video | code |
| Using Real-Time Energy Data for Low-Carbon Training | video | code |
| Understanding your Google Cloud Footprint | video | code |
| Next steps | video | |
| Conclusion | video | |
| Google Cloud Setup | code |