arrow_back

TFX on Google Cloud Vertex AI Pipelines

Zaloguj się Dołącz
Quick tip: Review the prerequisites before you run the lab
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.
Sprawdź swoją wiedzę i podziel się nią ze społecznością.
done
Zyskaj dostęp do ponad 700 praktycznych modułów oraz odznak umiejętności i szkoleń

TFX on Google Cloud Vertex AI Pipelines

Moduł 1 godz. 30 godz. universal_currency_alt Punkty: 5 show_chart Średnio zaawansowane
info Ten moduł może zawierać narzędzia AI, które ułatwią Ci naukę.
Sprawdź swoją wiedzę i podziel się nią ze społecznością.
done
Zyskaj dostęp do ponad 700 praktycznych modułów oraz odznak umiejętności i szkoleń

GSP1023

Google Cloud self-paced labs logo

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. Navigation menu icon and Search field

Task 1. Open the notebook in Vertex AI Workbench

  1. In the Google Cloud console, on the Navigation menu (Navigation menu icon), 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.

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.

  1. 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/* .
  1. 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

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

Task 3. Navigate to the lab notebook

  1. From the left-hand menu, select . This will open a new tab.

  2. 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 .

Task 4. 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.

Google Cloud training and certification

...helps you make the most of Google Cloud technologies. Our classes include technical skills and best practices to help you get up to speed quickly and continue your learning journey. We offer fundamental to advanced level training, with on-demand, live, and virtual options to suit your busy schedule. Certifications help you validate and prove your skill and expertise in Google Cloud technologies.

Manual Last Updated February 07, 2025

Lab Last Tested February 07, 2025

Copyright 2025 Google LLC All rights reserved. Google and the Google logo are trademarks of Google LLC. All other company and product names may be trademarks of the respective companies with which they are associated.

Before you begin

  1. Labs create a Google Cloud project and resources for a fixed time
  2. Labs have a time limit and no pause feature. If you end the lab, you'll have to restart from the beginning.
  3. On the top left of your screen, click Start lab to begin

Use private browsing

  1. Copy the provided Username and Password for the lab
  2. Click Open console in private mode

Sign in to the Console

  1. Sign in using your lab credentials. Using other credentials might cause errors or incur charges.
  2. Accept the terms, and skip the recovery resource page
  3. Don't click End lab unless you've finished the lab or want to restart it, as it will clear your work and remove the project

Ta treść jest obecnie niedostępna

Kiedy dostępność się zmieni, wyślemy Ci e-maila z powiadomieniem

Świetnie

Kiedy dostępność się zmieni, skontaktujemy się z Tobą e-mailem

One lab at a time

Confirm to end all existing labs and start this one

Setup your console before you begin

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.