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Carbon Aware Computing for GenAI Developers

Nikita Namjoshi

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.

Learn how to perform model training and inference jobs with cleaner, low-carbon energy in the cloud!

1. 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).

2. 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.

3. Retrieve measurements of the carbon footprint for ongoing cloud jobs.

4. 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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What's inside

Syllabus

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what should give you pause

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Suitable for GenAI developers seeking to reduce the environmental impact of their machine learning workflows

Provides practical guidance on optimizing energy consumption and carbon footprint

Empowers developers with real-time electricity grid data for informed decision-making

Utilizes the Google Cloud Carbon Footprint tool for comprehensive carbon footprint estimation

Course advised by Nikita Namjoshi, a recognized Google Cloud developer advocate

Instructs on querying real-time electricity grid data and selecting regions with low carbon intensity

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Reviews summary

Carbon-aware ai for cloud developers

This course, 'Carbon Aware Computing for GenAI Developers,' offers a timely and practical deep dive into managing the environmental impact of artificial intelligence. It focuses on how to measure and optimize the carbon footprint of machine learning workloads, especially model training and inference jobs in the cloud. Learners can expect to work with ElectricityMaps for real-time grid data and leverage Google Cloud's Carbon Footprint tool to make more sustainable development choices. The content is likely structured to provide hands-on experience with these tools, guided by an expert instructor from Google Cloud, making it particularly valuable for developers aiming to build greener AI.

Benefits from a knowledgeable instructor from Google Cloud.

"The instructor's background and expertise made the material very credible."

"I expect the course to be well-structured and clear, typical of DeepLearning.AI courses."

"Learning from an active developer advocate is a great advantage."

Focuses on real-world tools and cloud-based implementations.

"The hands-on work with ElectricityMaps and Google Cloud tools was incredibly useful."

"I appreciated the practical demonstrations of running carbon-aware jobs."

"Learning to apply these concepts directly in a cloud environment is a major plus."

Addresses the critical need for sustainable AI development.

"I found the course extremely relevant for current AI development."

"Understanding the environmental impact of GenAI is becoming crucial, and this course delivers."

"This course provides much-needed insights into green computing practices for ML."

Best suited for GenAI developers with cloud experience.

"This course seems designed for those already familiar with GenAI and cloud concepts."

"As an experienced developer, I found the pace and content well-suited to my background."

"Learners new to cloud computing might need to brush up on basics beforehand."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Carbon Aware Computing for GenAI Developers with these activities:

Review prerequisites

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As a refresher, review the basics of machine learning and cloud computing, including basic concepts, algorithms, and tools.

Browse courses on Machine Learning

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Review key machine learning concepts (e.g., supervised learning, unsupervised learning, model evaluation).
Review key cloud computing concepts (e.g., cloud architecture, virtual machines, data storage).

Explore Google Cloud Carbon Footprint tool

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Follow a tutorial or documentation to get familiar with the features and functionality of the Google Cloud Carbon Footprint tool.

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Complete the tutorial: https://cloud.google.com/carbonfootprint/docs/quickstart

Practice querying real-time electricity grid data

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Reinforce your understanding of how to query real-time electricity grid data using the ElectricityMaps API.

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Visit the ElectricityMaps website: https://electricitymaps.com/
Explore the map and query real-time electricity grid data for different regions.

Four other activities

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Blog post: Best practices for low-carbon ML training

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Synthesize your knowledge by creating a blog post or article that shares best practices for reducing the carbon footprint of machine learning training jobs.

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Research and gather information on low-carbon ML training.
Identify and outline key best practices.
Write and edit the blog post, ensuring clarity and conciseness.

Mentor a junior developer

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Deepen your understanding by sharing your knowledge and mentoring a junior developer interested in carbon-aware computing.

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Identify a junior developer who is interested in learning about carbon-aware computing.
Set up regular meetings to provide guidance and support.
Share resources and materials to help the developer learn and grow.

Participate in a carbon-aware ML hackathon

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Challenge yourself to apply your skills and knowledge in a practical setting by participating in a hackathon focused on carbon-aware ML.

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Find an appropriate hackathon or competition.
Form a team and develop a project proposal.
Implement and refine your solution.

Contribute to an open-source project on carbon-aware computing

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Contribute to the open-source community by collaborating on a project that promotes carbon-aware computing.

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Identify a suitable open-source project focused on carbon-aware computing.
Review the project documentation and identify areas where you can contribute.
Submit code contributions or documentation improvements.

Career center

Learners who complete Carbon Aware Computing for GenAI Developers will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Carbon Aware Computing for GenAI Developers.

Climate Change 2021 – The Physical Science Basis

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This report is the most comprehensive and up-to-date assessment of the science of climate change.

Carbon Aware Computing for GenAI Developers

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What's inside

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Reviews summary

Carbon-aware ai for cloud developers

Activities

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