Short CourseBeginner1 Hour 38 Minutes

Carbon Aware Computing for GenAI Developers

Instructors: Nikita Namjoshi

Enroll for Free

Beginner
1 Hour 38 Minutes
8 Video Lessons
5 Code Examples

What you'll learn

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.

About this course

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.

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!

Who should join?

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.

Course Outline

8 Lessons・5 Code Examples

Introduction
Video・3 mins
The Carbon Footprint of Machine Learning
Video・14 mins
Exploring Carbon Intensity on the Grid
Video with code examples・13 mins
Training Models in Low Carbon Regions
Video with code examples・18 mins
Using Real-Time Energy Data for Low-Carbon Training
Video with code examples・20 mins
Understanding your Google Cloud Footprint
Video with code examples・20 mins
Next steps
Video・5 mins
Conclusion
Video・1 mins
Google Cloud Setup
Code examples・1 min

Instructor

Nikita Namjoshi

Developer Advocate at Google Cloud