GSP066

Overview

The Awwvision lab uses Kubernetes and Cloud Vision API to demonstrate how to use the Vision API to classify (label) images from Reddit's /r/aww subreddit and display the labelled results in a web app.

Awwvision has three components:

1. A simple Redis instance.
2. A web app that displays the labels and associated images.
3. A worker that handles scraping Reddit for images and classifying them using the Vision API. Cloud Pub/Sub is used to coordinate tasks between multiple worker instances.

Objectives

In this lab, you will learn how to perform the following tasks:

• Create a Kubernetes Engine cluster
• Deploy the sample awwvision app that uses the Vision API
• Visit your new webapp and start its crawler.

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).

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

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.

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

1. Click Activate Cloud Shell at the top of the Google Cloud console.

2. Click through the following windows:

   • Continue through the Cloud Shell information window.
   • Authorize Cloud Shell to use your credentials to make Google Cloud API calls.

When you are connected, you are already authenticated, and the project is set to your Project_ID, . The output contains a line that declares the Project_ID for this session:

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

3. (Optional) You can list the active account name with this command:

4. Click Authorize.

Output:

5. (Optional) You can list the project ID with this command:

Output:

Task 1. Create a Kubernetes Engine cluster

In this lab you will use gcloud, Google Cloud's command-line tool, to set up a Kubernetes Engine cluster. You can specify as many nodes as you want, but you need at least one. The cloud platform scope is used to allow access to the Pub/Sub and Vision APIs.

1. In Cloud Shell, run the following to create a cluster in the zone:

2. Then start up the cluster by running:

Test completed task

Click Check my progress to verify your performed task. If you have successfully created a Kubernetes cluster, you will see an assessment score.

3. Run the following to use the container's credentials:

4. Verify that everything is working using the kubectl command-line tool:

Task 2. Create a virtual environment

Python virtual environments are used to isolate package installation from the system.

1. Install the virtualenv environment:

2. Build the virtual environment:

3. Activate the virtual environment:

Task 3. Get the sample

• Add sample data to your project by running:

Task 4. Deploy the sample

1. In Cloud Shell, change to the python/awwvision directory in the cloned cloud-vision repo:

2. Once in the awwvision directory, run make all to build and deploy everything:

As part of the process, Docker images will be built and uploaded to the Google Container Registry private container registry.

In addition, yaml files will be generated from templates, filled in with information specific to your project, and used to deploy the redis, webapp, and worker Kubernetes resources for the lab.

Task 5. Check the Kubernetes resources on the cluster

After you've deployed, check that the Kubernetes resources are up and running.

1. First, list the pods by running:

You should see something like the following, though your pod names will be different. Make sure all of your pods have a Running before executing the next command.

2. Next, list the deployments by running:

You can see the number of replicas specified for each, and the images used:

3. Once deployed, get the external IP address of the webapp service by running:

It may take a few minutes for the assigned external IP to be listed in the output. You should see something like the following, though your IPs will be different:

Test completed task

Click Check my progress to verify your performed task. If you have successfully deployed the sample app, you will see an assessment score.

Task 6. Visit your new webapp and start its crawler

1. Copy and paste the external IP of the awwvision-webapp service into a new browser to open the webapp, then click Start the Crawler button.

2. Next, click go back and you should start to see images from the /r/aww subreddit classified by the labels provided by the Vision API. You will see some of the images classified multiple times, when multiple labels are detected for them. (You can reload in a bit, in case you brought up the page before the crawler was finished).

Your results will look something like this:

Task 7. Test your understanding

Below is a multiple choice question to reinforce your understanding of this lab's concepts. Answer it to the best of your abilities.

Congratulations!

You used Kubernetes and Cloud Vision API to classify images from Reddit's /r/aww subreddit and displayed the results in a web app.

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Manual Last Updated February 14, 2024

Lab Last Tested October 09, 2023

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