GSP1023

Overview

Tensorflow Extended (TFX) is Google's end-to-end platform for training and deploying TensorFlow models into production. TFX pipelines orchestrate ordered runs of a sequence of components for scalable, high-performance machine learning tasks in a directed graph. It includes pre-built and customizable components for data ingestion and validation, model training and evaluation, as well as model validation and deployment. TFX is the best solution for taking TensorFlow models from prototyping to production with support on-prem environments and in the cloud such as on Google Cloud Vertex AI Pipelines.

Vertex AI Pipelines helps you to automate, monitor, and govern your ML systems by orchestrating your ML workflow in a serverless manner, and storing your workflow's artifacts using Vertex ML Metadata.

In this lab you will learn how to deploy and run a TFX pipeline on Google Cloud that automates the development and deployment of a TensorFlow 2.15 classification model which predicts the species of penguins.

Objectives

• Create a TFX Pipeline using TFX APIs
• Define a pipeline runner that uses Vertex AI Pipelines together with the Kubeflow V2 dag runner
• Deploy and monitor a TFX pipeline on Vertex AI Pipelines

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. Copy the notebook from a Cloud Storage bucket

1. Click the Terminal icon to open a terminal window.

Your terminal window will open in a new tab. You can now run commands in the terminal to interact with your Workbench instance.

2. Copy and run the following code in the terminal to copy the notebook file .

gcloud storage cp -r gs://{{{project_0.project_id|project_id}}}-labconfig-bucket/* .

3. Confirm that you have copied the notebook file. Double-click on the notebook and ensure that you can see its contents.

Note: If pop-up for File Changed appears click overwrite

4. Replace your Project_ID and Region in the notebook with the lab's Project ID and Region. For GOOGLE_CLOUD_PROJECT, use , and for the GOOGLE_CLOUD_REGION, use .

Click Check my progress to verify the objective. Copy the notebook from a Cloud Storage

Task 3. Run your training job in the cloud

Note: The Vertex AI Pipeline job will take about 40 minutes to complete. To view your job, navigate to AI > Pipelines and select the region from the drop down.

Click Check my progress to verify the objective. Build and deploy a TFX pipeline to Vertex AI Pipelines

Note: You may need to wait a few minutes after job completion for progress to be tracked accordingly.

Congratulations!

You have learned how to build and deploy a TFX pipeline to Vertex AI Pipelines and triggered a pipeline run.

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Manual Last Updated July 02, 2025

Lab Last Tested July 02, 2025

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