GSP076

Overview

This lab will give you hands-on practice with TensorFlow 2.x model training, both locally and on Vertex AI Workbench. After training, you will learn how to deploy your model to Vertex AI for serving (prediction). You'll train your model to predict the income category of a person using the United States Census Income Dataset.

This lab gives you an introductory, end-to-end experience of training and prediction on Vertex AI. The lab will use a census dataset to:

• Create a TensorFlow 2.x training application and validate it locally.
• Run your training job on a single worker instance in the cloud.
• Deploy a model to support prediction.
• Request an online prediction and see the response.

What you will build

The sample builds a classification model for predicting income category based on the United States Census Income Dataset. The two income categories (also known as labels) are:

• >50K — Greater than 50,000 dollars
• <=50K — Less than or equal to 50,000 dollars

The sample defines the model using the Keras Sequential API. The sample defines the data transformations particular to the census dataset, then assigns these (potentially) transformed features to either the DNN or the linear portion of the 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 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.

Task 1. Launch Vertex AI Workbench notebook

To create and launch a Vertex AI Workbench notebook:

1. In the Navigation Menu , click Vertex AI > Workbench.

2. On the Workbench page, click Enable Notebooks API (if it isn't enabled yet), then click New Notebook.

3. In the Customize instance menu, select TensorFlow Enterprise and choose the latest version of TensorFlow Enterprise 2.x (with LTS) > Without GPUs.

4. Name the notebook.

5. Set Region to and Zone to any zone within the designated region.

6. In the Notebook properties, click the pencil icon to edit the instance properties.

7. Click Machine type and then select e2-standard-2 for Machine type.

8. Leave the remaining fields at their default and click Create.

After a few minutes, the Workbench page lists your instance, followed by Open JupyterLab.

9. Click Open JupyterLab to open JupyterLab in a new tab. If you get a message saying beatrix jupyterlab needs to be included in the build, just ignore it.

Note: If Prompted, Click `Build` for Build Recommended pop-up.

Task 2. Clone the example repo within your Workbench instance

To clone the training-data-analyst repository in your JupyterLab instance:

1. In JupyterLab, click the Terminal icon to open a new terminal.

2. At the command-line prompt, type the following command and press ENTER:

git clone https://github.com/GoogleCloudPlatform/training-data-analyst

3. To confirm that you have cloned the repository, in the left panel, double click the training-data-analyst folder to see its contents.

It will take several minutes for the notebook to clone.

Note: If Prompted, Click `Dismiss` for Build Failed pop-up to ignore the message.

Navigate to the example notebook

1. Navigate to training-data-analyst/self-paced-labs/ai-platform-qwikstart and open ai_platform_qwik_start.ipynb.

2. On the notebook toolbar, navigate to Edit > Clear All Outputs and then Run the cells one by one.

When prompted, come back to these instructions to check your progress.

Task 3. Run your training job in the cloud

There are additional steps to read in the notebook. Read these instructions carefully including the comments in the cells with code to ensure you are completing each step correctly.

Test completed tasks - step 3.1

1. Click Check my progress to verify your performed task.

Set up a Cloud Storage bucket.

2. Click Check my progress to verify your performed task.

Upload the data files to your Cloud Storage bucket.

Test completed tasks - step 3.2

Click Check my progress to verify your performed task.

Run a single-instance trainer in the cloud.

Test completed tasks - step 3.3

1. Click Check my progress to verify your performed task.

Create an Vertex AI model.

2. Click Check my progress to verify your performed task.

Create a version v1 of your model.

Task 4. Test your understanding

Below are multiple choice questions to reinforce your understanding of this lab's concepts. Answer them to the best of your abilities.

Congratulations!

In this lab you've learned how to train a TensorFlow model both locally and on Vertex AI, and then how to use your trained model for prediction.

Finish your quest

This self-paced lab is part of the Machine Learning APIs, Baseline: Data, ML, AI, Intro to ML: Language Processing, and Intro to ML: Image Processing 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. See the Google Cloud Skills Boost catalog to see all available quests

Take your next lab

Try out another lab on Machine Learning APIs, like Extract, Analyze, and Translate Text from Images with the Cloud ML APIs or Awwvision: Cloud Vision API from a Kubernetes Cluster.

This lab is also part of a series of labs called Qwik Starts. These labs are designed to give you a little taste of the many features available with Google Cloud. Search for "Qwik Starts" in the lab catalog to find the next lab you'd like to take!

Next steps

• Sign up for the full Coursera Course on Machine Learning.
• Explore a wide range of TensorFlow tutorials.
• You can read more about wide and deep models in the Google Research Blog post named Wide & Deep Learning: Better Together with TensorFlow.
• Get your own version of Tensorflow.

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Manual Last Updated: March 28, 2023

Lab Last Tested: March 28, 2023

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