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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Instructor: Nikita Namjoshi
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.
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.
Analyze the carbon footprint of sample Google Cloud usage data, including machine learning training, inference, storage, and other API activities.
Learn how to perform model training and inference jobs with cleaner, low-carbon energy in the cloud!
Learn from Nikita Namjoshi, developer advocate at Google Cloud and Google Fellow on the Permafrost Discovery Gateway, and explore how to measure the environmental impact of your machine learning jobs, and also how to optimize their use of clean electricity.
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!
Familiarity with Python will help with the coding parts of the lesson, although the concepts covered can help anyone gain an understanding of the environmental impact of machine learning workflows.
Introduction
The Carbon Footprint of Machine Learning
Exploring Carbon Intensity on the Grid
Training Models in Low Carbon Regions
Using Real-Time Energy Data for Low-Carbon Training
Understanding your Google Cloud Footprint
Next steps
Conclusion
Google Cloud Setup
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