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.
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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!
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.
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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Syllabus
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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
Show steps
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Review key machine learning concepts (e.g., supervised learning, unsupervised learning, model evaluation).
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Review key cloud computing concepts (e.g., cloud architecture, virtual machines, data storage).
Explore Google Cloud Carbon Footprint tool
Show steps
Follow a tutorial or documentation to get familiar with the features and functionality of the Google Cloud Carbon Footprint tool.
Show steps
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Complete the tutorial: https://cloud.google.com/carbonfootprint/docs/quickstart
Practice querying real-time electricity grid data
Show steps
Reinforce your understanding of how to query real-time electricity grid data using the ElectricityMaps API.
Show steps
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Visit the ElectricityMaps website: https://electricitymaps.com/
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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.
Show steps
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Research and gather information on low-carbon ML training.
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Identify and outline key best practices.
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Write and edit the blog post, ensuring clarity and conciseness.
Mentor a junior developer
Show steps
Deepen your understanding by sharing your knowledge and mentoring a junior developer interested in carbon-aware computing.
Show steps
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Identify a junior developer who is interested in learning about carbon-aware computing.
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Set up regular meetings to provide guidance and support.
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Share resources and materials to help the developer learn and grow.
Participate in a carbon-aware ML hackathon
Show steps
Challenge yourself to apply your skills and knowledge in a practical setting by participating in a hackathon focused on carbon-aware ML.
Show steps
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Find an appropriate hackathon or competition.
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Form a team and develop a project proposal.
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Implement and refine your solution.
Contribute to an open-source project on carbon-aware computing
Show steps
Contribute to the open-source community by collaborating on a project that promotes carbon-aware computing.
Show steps
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Identify a suitable open-source project focused on carbon-aware computing.
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Review the project documentation and identify areas where you can contribute.
-
Submit code contributions or documentation improvements.
Review prerequisites
Show steps
As a refresher, review the basics of machine learning and cloud computing, including basic concepts, algorithms, and tools.
Browse courses on
Machine Learning
Show steps
- 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
Show steps
Follow a tutorial or documentation to get familiar with the features and functionality of the Google Cloud Carbon Footprint tool.
Show steps
- Complete the tutorial: https://cloud.google.com/carbonfootprint/docs/quickstart
Practice querying real-time electricity grid data
Show steps
Reinforce your understanding of how to query real-time electricity grid data using the ElectricityMaps API.
Show steps
- Visit the ElectricityMaps website: https://electricitymaps.com/
- Explore the map and query real-time electricity grid data for different regions.
Blog post: Best practices for low-carbon ML training
Show steps
Synthesize your knowledge by creating a blog post or article that shares best practices for reducing the carbon footprint of machine learning training jobs.
Show steps
- 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
Show steps
Deepen your understanding by sharing your knowledge and mentoring a junior developer interested in carbon-aware computing.
Show steps
- 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
Show steps
Challenge yourself to apply your skills and knowledge in a practical setting by participating in a hackathon focused on carbon-aware ML.
Show steps
- 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
Show steps
Contribute to the open-source community by collaborating on a project that promotes carbon-aware computing.
Show steps
- 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.
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