Awwvision: Cloud Vision API from a Kubernetes Cluster

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Awwvision: Cloud Vision API from a Kubernetes Cluster

45 minutes 5 Credits


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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.

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 will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that 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 or private browser window to run this lab. This prevents any 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: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your 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 pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

    • The Open Google 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 Console. 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 from the Lab Details panel and paste it into the Sign in dialog. Click Next.

  4. Copy the Password from the Lab Details panel and paste it into the Welcome dialog. Click Next.

    Important: You must use the credentials from the left panel. Do not use your Google Cloud Skills Boost credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.
  5. 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 Cloud Console opens in this tab.

Note: You can view the menu with a list of Google Cloud Products and Services by clicking the Navigation menu at the top-left. Navigation menu icon

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 Activate Cloud Shell icon at the top of the Google Cloud console.

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:

Your Cloud Platform project in this session is set to YOUR_PROJECT_ID

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

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

gcloud auth list
  1. Click Authorize.

  2. Your output should now look like this:


ACTIVE: * ACCOUNT: To set the active account, run: $ gcloud config set account `ACCOUNT`
  1. (Optional) You can list the project ID with this command:

gcloud config list project


[core] project = <project_ID>

Example output:

[core] project = qwiklabs-gcp-44776a13dea667a6 Note: For full documentation of gcloud, in Google Cloud, refer to the gcloud CLI overview guide.

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 us-central1-a zone:

gcloud config set compute/zone us-central1-a
  1. Then start up the cluster by running:

gcloud container clusters create awwvision \ --num-nodes 2 \ --scopes cloud-platform

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.

Create a Kubernetes Engine cluster
  1. Run the following to use the container's credentials:

gcloud container clusters get-credentials awwvision
  1. Verify that everything is working using the kubectl command-line tool:

kubectl cluster-info

Task 2. Create a virtual environment

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

  1. Install the virtualenv environment:

sudo apt-get install -y virtualenv
  1. Build the virtual environment:

python3 -m venv venv
  1. Activate the virtual environment.

source venv/bin/activate

Task 3. Get the sample

  • Add sample data to your project by running:

gsutil -m cp -r gs://spls/gsp066/cloud-vision .

Task 4. Deploy the sample

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

cd cloud-vision/python/awwvision
  1. Once in the awwvision directory, run make all to build and deploy everything:

make all

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:

kubectl get pods

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.

NAME READY STATUS RESTARTS AGE awwvision-webapp-vwmr1 1/1 Running 0 1m awwvision-worker-oz6xn 1/1 Running 0 1m awwvision-worker-qc0b0 1/1 Running 0 1m awwvision-worker-xpe53 1/1 Running 0 1m redis-master-rpap8 1/1 Running 0 2m
  1. Next, list the deployments by running:

kubectl get deployments -o wide

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

NAME READY UP-TO-DATE AVAILABLE AGE CONTAINERS IMAGES SELECTOR awwvision-webapp 1/1 1 1 1m awwvision-webapp app=awwvision awwvision-worker 3/3 3 3 1m awwvision-worker app=awwvision redis-master 1/1 1 1 1m redis-master redis app=redis
  1. Once deployed, get the external IP address of the webapp service by running:

kubectl get svc awwvision-webapp

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:

NAME TYPE CLUSTER_IP EXTERNAL_IP PORT(S) AGE awwvision-webapp LoadBalancer 80:31925/TCP 13m

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.

Deploy the sample

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:

Awwvision web page displaying several photo tiles

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.


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

Finish your quest

This self-paced lab is part of the Machine Learning APIs, Advanced ML: ML Infrastructure quests. A quest is a series of related labs that form a learning path. Completing a quest earns you a badge to recognize your achievement. You can make your badge or badges public and link to them in your online resume or social media account. Enroll in any quest that contains this lab and get immediate completion credit. Refer to the Google Cloud Skills Boost catalog for all available quests.

Take your next lab

Try out another lab on Machine Learning APIs, like Distributed Load Testing using Kubernetes.

Next steps / Learn more

To learn more, sign up for the full Coursera Course on Machine Learning

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Manual Last Updated January 31, 2023

Lab Last Tested January 31, 2023

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