GSP223

Overview

AutoML helps developers with limited ML expertise train high quality image recognition models. Once you upload images to the AutoML UI, you can generate predictions against a pre-trained model via an easy to use REST API. In this lab, you upload images to Cloud Storage and use them to generate predictions from a pre-trained AutoML model.

Objectives

In this lab, you perform the following tasks:

• Upload a labeled dataset to Cloud Storage and connect it to AutoML with a CSV label file.
• Generate predictions against a pre-trained model.

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. Set up the AutoML environment

For the AutoML environment, confirm that Cloud AutoML API is enabled, open the Vertex AI Dashboard, then create a storage bucket to store training images.

Confirm that Cloud AutoML API is enabled

Cloud AutoML API lets you train high-quality custom machine learning models with minimum effort and machine learning expertise.

To confirm Cloud AutoML API is enabled:

1. In the Google Cloud console, in the Navigation menu (), select APIs & Services > Library.

2. In the Search for APIs & services field, type Cloud AutoML, then click Cloud AutoML API in the search results.

3. Confirm that the Cloud AutoML API is in the Enable state.

Open the Vertex AI Dashboard

The Vertex AI Dashboard provides a centralized interface to access services to train and then use an image classification model.

To open the Vertex AI Dashboard:

• In the Navigation menu (), click Vertex AI.

Create a storage bucket

The storage bucket holds your training images.

• In Cloud Shell, run the following command to create a storage bucket named -vcm:

Click Check my progress to verify the objective.

Task 2. Upload training images to Cloud Storage

To train a model to classify cloud images, you need labeled training data so the model can develop an understanding of the image features associated with different types of clouds. In this example your model learns to classify three different types of clouds: cirrus, cumulus, and cumulonimbus.

To put the training images in your Cloud Storage bucket:

1. In Cloud Shell, run the following command to create an environment variable with the name of your bucket:

The training images are publicly available in a Cloud Storage bucket.

2. Use the gsutil command line utility for Cloud Storage to copy the training images into your bucket:

3. To view the images you just copied into your bucket, in the Navigation menu, click Cloud Storage > Buckets, then click on your bucket name. You should see 3 folders of photos for each of the 3 different cloud types to be classified.

If you click on the individual image files in each folder you can see the photos you'll use to train the model for each type of cloud.

Task 3. Create a dataset

Now that your training data is in Cloud Storage, you need a way for AutoML to access it. Typically, you'd create a CSV file where each row contains a URL to a training image and the associated label for that image.

For this lab, the CSV file has been created for you; you just need to update it with your bucket name.

1. Run the following command to copy the CSV file to your Cloud Shell instance:

2. Update the CSV file with the files in your project:

3. Upload this file to your Cloud Storage bucket:

4. Once that command completes, click the Refresh button at the top of the Storage browser. Confirm that you see the data.csv file in your bucket.

5. In the Navigation menu, click Vertex AI > Datasets.

6. At the top of the console, click Create.

7. Set Dataset Name to clouds.

8. Select Single-label classification.

9. Click Create.

10. Choose Select import files from Cloud Storage and then click Browse > -vcm > data.csv. Click Select.

11. Click Continue.

It will take 2 - 5 minutes for your images to import.

Click Check my progress to verify the objective.

Task 4. Inspect images

After the import completes, the Browse tab opens and shows all the images in your dataset.

Try filtering by different labels in the left menu (i.e. click cumulus) to review the training images:

If any images are labeled incorrectly you can click on the image to switch the label:

Given that model training requires significant time, this lab provides a pre-trained model to generate predictions. In the following section, you use this model to generate predictions on the images you uploaded to Cloud Storage.

Task 5. Generate predictions

There are a few ways to generate predictions. In this lab, you upload images and see how your model does classifying these two images (the first is a cirrus cloud, the second is a cumulonimbus).

1. In Cloud Shell, enter the following command to download the two images to your local machine:

2. View the example file CLOUD1-JSON:

Example output:

3. Copy the endpoint value to an environment variable:

4. Enter the following command to request a prediction:

Expected Output:

Task 6. Pop Quiz

Test your understanding of AutoML by completing the short quiz on the topics covered in this lab. Use the knowledge you have gained in the lab to generate predictions.

Check if the model can predict the type of cloud in the image:

1. Set CLOUD1-JSON as the input file:

2. Enter the following command to request a prediction:

Check if the model can predict the type of cloud in the image:

3. Set CLOUD2-JSON as the input file.

4. Enter the following command to request a prediction:

Congratulations!

Congratulations! In this lab, you learned how to use AutoML to classify images of clouds. You first uploaded training images to Cloud Storage and created a CSV for AutoML to find these images. Then you generated predictions on new cloud images using a pre-trained model.

Next steps / Learn more

• Watch Introducing Cloud AutoML
• Learn more about how AutoML works by listening to the Google Cloud Podcast episode
• Read the announcement blog post
• Learn how to perform each step with the API, see Introduction to Vertex

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Manual Last Updated December 3, 2024

Lab Last Tested December 3, 2024

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