GSP1232

Overview

In this lab, you will explore a variety of use cases enabled by Gemini 2.0 Flash in the context of education. You will learn about the capabilities of the Gemini model across various reasoning tasks, showcasing its potential for educational applications.

Through a series of tasks, the notebook evaluates Gemini's proficiency in understanding complex prompts, extracting relevant information, and generating accurate and insightful responses, highlighting its potential for enhancing learning and problem-solving in educational settings. The tasks progress from fundamental reasoning with text and numbers to more advanced multi-modal reasoning involving visual data.

Gemini

Gemini is a family of powerful generative AI models developed by Google DeepMind, capable of understanding and generating various forms of content, including text, code, images, audio, and video.

Gemini API in Vertex AI

The Gemini API in Vertex AI provides a unified interface for interacting with Gemini models. This allows developers to easily integrate these powerful AI capabilities into their applications. For the most up-to-date details and specific features of the latest versions, please refer to the official Gemini documentation.

Gemini Models

• Gemini Pro: Designed for complex reasoning, including:
  • Analyzing and summarizing large amounts of information.
  • Sophisticated cross-modal reasoning (across text, code, images, etc.).
  • Effective problem-solving with complex codebases.
• Gemini Flash: Optimized for speed and efficiency, offering:
  • Sub-second response times and high throughput.
  • High quality at a lower cost for a wide range of tasks.
  • Enhanced multimodal capabilities, including improved spatial understanding, new output modalities (text, audio, images), and native tool use (Google Search, code execution, and third-party functions).

Prerequisites

Before starting this lab, you should be familiar with:

• Basic Python programming.
• General API concepts.
• Running Python code in a Jupyter notebook on Vertex AI Workbench.

Objectives

In this lab, you will explore a variety of educational use cases that can benefit from Gemini. You will learn how to:

• Install the Python SDK
• Load Gemini
• Reason at different levels
• Reason on text
• Reason on numbers
• Reason on a single image
• Reason on multiple images
• Reason on a video

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

Note: Use an Incognito (recommended) or private browser window to run this lab. This prevents conflicts between your personal account and the student account, which may cause extra charges incurred to your personal account.

• Time to complete the lab—remember, once you start, you cannot pause a lab.

Note: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that account.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details pane.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details pane.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.

Task 1. Open the notebook in Vertex AI Workbench

1. In the Google Cloud console, on the Navigation menu (), click Vertex AI > Workbench.

2. Find the instance and click on the Open JupyterLab button.

The JupyterLab interface for your Workbench instance opens in a new browser tab.

Note: If you do not see notebooks in JupyterLab, please follow these additional steps to reset the instance:

1. Close the browser tab for JupyterLab, and return to the Workbench home page.

2. Select the checkbox next to the instance name, and click Reset.

3. After the Open JupyterLab button is enabled again, wait one minute, and then click Open JupyterLab.

Task 2. Set up the notebook

1. Open the file.

2. In the Select Kernel dialog, choose Python 3 from the list of available kernels.

3. Run through the Getting Started and the Import libraries sections of the notebook.

   • For Project ID, use , and for Location, use .

Note: You can skip any notebook cells that are noted Colab only. If you experience a 429 response from any of the notebook cell executions, wait 1 minute before running the cell again to proceed.

Click Check my progress to verify the objective. Install the Gen AI SDK for Python

Task 3. Use the Gemini model to perform textual and numerical reasoning tasks

This task will assess Gemini's ability to reason with different types of data, focusing on text and numbers. It will involve presenting the model with various prompts and questions that require logical deduction, inference, and problem-solving skills. The task will explore different levels of reasoning complexity, from basic comprehension to more advanced analytical tasks, to evaluate the model's proficiency in understanding and processing textual and numerical information.

Reasoning at different levels

1. In this task, run through the notebook cells to see how to use the gemini-2.0-flash model to reason at different levels.

Reasoning on text

1. In this task, run through the notebook cells to see how to use the gemini-2.0-flash model to reason on text.

Reasoning on numbers

1. In this task, run through the notebook cells to see how to use the gemini-2.0-flash model to reason on numbers.

Click Check my progress to verify the objective. Use the Gemini model to reason at different levels, text and numbers

Task 4. Use the Gemini model to perform image and video reasoning tasks

This task will evaluate Gemini's capacity for visual reasoning by analyzing its responses to prompts involving single images, sets of images, and video content. The model will be presented with tasks that require understanding visual information, such as object recognition, scene interpretation, and event analysis. By assessing the model's ability to extract meaning and draw inferences from visual inputs, this task aims to showcase Gemini model capabilities in multi-modal reasoning and its potential for applications in visual learning and analysis.

Reasoning on a single image

1. In this task, run through the notebook cells to see how to use the gemini-2.0-flash model to reason on a single image.

Reasoning on multiple images

1. In this task, run through the notebook cells to see how to use the gemini-2.0-flash model to reason on multiple images.

Click Check my progress to verify the objective. Use the Gemini model to reason on single and multiple images

Reasoning on a video

1. In this task, run through the notebook cells to see how to use the gemini-2.0-flash model to reason on a video.

Congratulations!

In this lab, you've how you can use Gemini for education and benefit from text and multimodal models to generate content from text, images, and videos.

Next steps / learn more

Check out the following resources to learn more about Gemini:

• Gemini Overview
• Generative AI on Vertex AI Documentation
• Generative AI on YouTube
• Explore the Vertex AI Cookbook for a curated, searchable gallery of notebooks for Generative AI.
• Explore other notebooks and samples in the Google Cloud Generative AI repository.

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Manual Last April 21, 2025

Lab Last Tested April 18, 2025

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