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๐Ÿ’ป ย  Accessing Utils File and Helper Functions

In each notebook on the top menu:

1: ย  Click on "File"

2: ย  Then, click on "Open"

You will be able to see all the notebook files for the lesson, including any helper functions used in the notebook on the left sidebar. See the following image for the steps above.


๐Ÿ’ป ย  Downloading Notebooks

In each notebook on the top menu:

1: ย  Click on "File"

2: ย  Then, click on "Download as"

3: ย  Then, click on "Notebook (.ipynb)"


๐Ÿ’ป ย  Uploading Your Files

After following the steps shown in the previous section ("File" => "Open"), then click on "Upload" button to upload your files.


๐Ÿ“— ย  See Your Progress

Once you enroll in this courseโ€”or any other short course on the DeepLearning.AI platformโ€”and open it, you can click on 'My Learning' at the top right corner of the desktop view. There, you will be able to see all the short courses you have enrolled in and your progress in each one.

Additionally, your progress in each short course is displayed at the bottom-left corner of the learning page for each course (desktop view).


๐Ÿ“ฑ ย  Features to Use

๐ŸŽž ย  Adjust Video Speed: Click on the gear icon (โš™) on the video and then from the Speed option, choose your desired video speed.

๐Ÿ—ฃ ย  Captions (English and Spanish): Click on the gear icon (โš™) on the video and then from the Captions option, choose to see the captions either in English or Spanish.

๐Ÿ”… ย  Video Quality: If you do not have access to high-speed internet, click on the gear icon (โš™) on the video and then from Quality, choose the quality that works the best for your Internet speed.

๐Ÿ–ฅ ย  Picture in Picture (PiP): This feature allows you to continue watching the video when you switch to another browser tab or window. Click on the small rectangle shape on the video to go to PiP mode.

โˆš ย  Hide and Unhide Lesson Navigation Menu: If you do not have a large screen, you may click on the small hamburger icon beside the title of the course to hide the left-side navigation menu. You can then unhide it by clicking on the same icon again.


๐Ÿง‘ ย  Efficient Learning Tips

The following tips can help you have an efficient learning experience with this short course and other courses.

๐Ÿง‘ ย  Create a Dedicated Study Space: Establish a quiet, organized workspace free from distractions. A dedicated learning environment can significantly improve concentration and overall learning efficiency.

๐Ÿ“… ย  Develop a Consistent Learning Schedule: Consistency is key to learning. Set out specific times in your day for study and make it a routine. Consistent study times help build a habit and improve information retention.

Tip: Set a recurring event and reminder in your calendar, with clear action items, to get regular notifications about your study plans and goals.

โ˜• ย  Take Regular Breaks: Include short breaks in your study sessions. The Pomodoro Technique, which involves studying for 25 minutes followed by a 5-minute break, can be particularly effective.

๐Ÿ’ฌ ย  Engage with the Community: Participate in forums, discussions, and group activities. Engaging with peers can provide additional insights, create a sense of community, and make learning more enjoyable.

โœ ย  Practice Active Learning: Don't just read or run notebooks or watch the material. Engage actively by taking notes, summarizing what you learn, teaching the concept to someone else, or applying the knowledge in your practical projects.


๐Ÿ“š ย  Enroll in Other Short Courses

Keep learning by enrolling in other short courses. We add new short courses regularly. Visit DeepLearning.AI Short Courses page to see our latest courses and begin learning new topics. ๐Ÿ‘‡

๐Ÿ‘‰๐Ÿ‘‰ ๐Ÿ”— DeepLearning.AI โ€“ All Short Courses [+]


๐Ÿ™‚ ย  Let Us Know What You Think

Your feedback helps us know what you liked and didn't like about the course. We read all your feedback and use them to improve this course and future courses. Please submit your feedback by clicking on "Course Feedback" option at the bottom of the lessons list menu (desktop view).

Also, you are more than welcome to join our community ๐Ÿ‘‰๐Ÿ‘‰ ๐Ÿ”— DeepLearning.AI Forum


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Welcome to Carbon Aware Computing for Gen AI Developers. Built in partnership with Google Cloud. I'm delighted their instructor for this course is Nikita Namjoshi, who's a developer advocate at Google Cloud and also a Google fellow on the Permafrost Discovery Gateway, which is an initiative using AI to track Arctic permafrost four. She's working on sustainability for over five years has a lot to share about best practices for reducing CO2 emissions of AI workloads. Thanks, Andrew. I'm excited to work with you and your team on this. Training, fine tuning, and even serving your models, especially generative models can be compute intensive and energy-intensive, but it doesn't have to be carbon intensive. If you know how to customize where and when you run your ML jobs in the cloud. Specifically, you can choose low CO2 energy sources to power your training jobs by running them in data centers to powered by low-carbon energy sources such as wind, solar, hydro, or nuclear. In this short course, you learn how to query the breakdown of energy sources for different regional electricity grids across the world using the Electricity Maps API. And also you'll learn how to train an ML model in the cloud at a data center that is powered by a high amount of carbon free energy. Current estimates are that cloud computing accounts for around 2.5 to 3.7% of our global greenhouse gas emissions. Further, AI workloads are continuing to grow, which is a sign of how our field is booming, but also makes it increasingly important that we do our part to mitigate emissions. In this course, you learned about how carbon is quantified and reported, according to the Greenhouse Gas Protocol corporate standard, and what that means for you as a developer. Nikita will go through these concepts using the Electricity Maps API, as well as Google Cloud's Vertex AI SDK and carbon footprint too. But the concepts you learn apply to multiple clouds, so even if you're just using your local machine. Thanks, Andrew. I think it's a lot easier to understand our environmental impact when we do something like use a single-use plastic fork or water bottle, or when we fly on a plane or maybe put gas in a car, but it's a little harder to grasp the impact from something like computing and machine learning, where we're just writing code. Learning about carbon-aware development has been really eye opening for me. With some simple steps that you'll learn in this course, you can make a significant difference on the carbon impact of your machine learning workloads. Also, be sure to stick around for the end of the course where you will explore a dashboard that helps you pick an optimal Google Cloud region based on how you weigh factors like carbon footprint, cost, and latency. The course will also wrap up with an overview of some exciting research in the field of sustainable cloud computing. Many people have worked to create this course. I'd like to thank from Google Cloud, John Abel, Cynthia Wu, Khulan Davaajav, and Jeff Sternberg. From DeepLearning.AI Eddy Shyu also contributed to this course. In the first lesson, you'll learn about energy and the electric grid as they relate to computing infrastructure and carbon emissions. You'll also explore the carbon intensity of regions all around the world with an interactive map. That sounds great. Let's go on to the next video and get started.
course detail
Next Lesson
Week 1: Carbon Aware Computing for GenAI developers
  • Introduction
    Video
    ใƒป
    3 mins
  • The Carbon Footprint of Machine Learning
    Video
    ใƒป
    14 mins
  • Exploring Carbon Intensity on the Grid
    Video with Code Example
    ใƒป
    13 mins
  • Training Models in Low Carbon Regions
    Video with Code Example
    ใƒป
    18 mins
  • Using Real-Time Energy Data for Low-Carbon Training
    Video with Code Example
    ใƒป
    19 mins
  • Understanding your Google Cloud Footprint
    Video with Code Example
    ใƒป
    20 mins
  • Next steps
    Video
    ใƒป
    5 mins
  • Conclusion
    Video
    ใƒป
    1 min
  • Google Cloud Setup
    Code Example
    ใƒป
    10 mins
  • Course Feedback
  • Community