GSP971

Overview

In this lab, you will use TensorFlow's distribution strategies and the Vertex AI platform to train and deploy a custom TensorFlow image classification model to classify an image classification dataset.

What you'll learn

1. Deploy a training pipeline which uses MirrorStrategy (one of TensorFlow's distribution strategies) on Vertex AI.
2. Deploy an endpoint for the model in the cloud using Vertex AI for online prediction.

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 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 panel.

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 panel.

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 view a menu with a list of Google Cloud products and services, click the Navigation menu at the top-left.

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.

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 {{{project_0.project_id | "PROJECT_ID"}}}

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

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

gcloud auth list

3. Click Authorize.

Output:

ACTIVE: * ACCOUNT: {{{user_0.username | "ACCOUNT"}}} To set the active account, run: $ gcloud config set account `ACCOUNT`

4. (Optional) You can list the project ID with this command:

gcloud config list project

Output:

[core] project = {{{project_0.project_id | "PROJECT_ID"}}} Note: For full documentation of gcloud, in Google Cloud, refer to the gcloud CLI overview guide.

Task 1. Enable Google Cloud services

1. In Cloud Shell, use gcloud to enable the services used in the lab:

gcloud services enable \ compute.googleapis.com \ iam.googleapis.com \ iamcredentials.googleapis.com \ monitoring.googleapis.com \ logging.googleapis.com \ notebooks.googleapis.com \ aiplatform.googleapis.com \ bigquery.googleapis.com \ artifactregistry.googleapis.com \ cloudbuild.googleapis.com \ container.googleapis.com

2. Create a custom service account:

SERVICE_ACCOUNT_ID=vertex-custom-training-sa gcloud iam service-accounts create $SERVICE_ACCOUNT_ID \ --description="A custom service account for Vertex custom training" \ --display-name="Vertex AI Custom Training"

3. Set the Project ID environment variable:

PROJECT_ID=$(gcloud config get-value core/project)

4. Grant your service account the aiplatform.user role:

gcloud projects add-iam-policy-binding $PROJECT_ID \ --member=serviceAccount:$SERVICE_ACCOUNT_ID@$PROJECT_ID.iam.gserviceaccount.com \ --role="roles/aiplatform.user"

This will allow access to running model training, deployment, and explanation jobs with Vertex AI.

Click Check my progress to verify the objective. Enable Google Cloud services

Task 2. Deploy Vertex notebook instance

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).

3. Click on User-Managed Notebooks tab then, click Create New.

4. In the New instance menu, choose TensorFlow Enterprise 2.11 in Environment.

5. Name the notebook.

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

7. Click Advanced Options to edit the instance properties.

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

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

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

10. 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.

Click Check my progress to verify the objective. Deploy Vertex Notebook instance

Task 3. Clone the lab repository

Next, you'll clone the training-data-analyst notebook in your JupyterLab instance.

1. In JupyterLab, click the Terminal icon to open a new terminal.
2. To clone the training-data-analyst Github repository, type in the following command, and press Enter:

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

3. To confirm that you have cloned the repository, double-click the training-data-analyst directory and confirm that you can see its contents.

Click Check my progress to verify the objective. Clone the lab repository

Navigate to the lab notebook

1. In the left panel, navigate to the training-data-analyst/self-paced-labs/vertex-ai/vertex-distributed-tensorflow folder.

2. Click the Qwiklab_Running_Distributed_TensorFlow_using_Vertex_AI.ipynbfile to open it in the right window.

3. Continue the lab in the notebook, and run each cell by clicking the Run icon () at the top of the screen. Alternatively, you can execute the code in a cell with SHIFT + ENTER.

• Read the narrative and make sure you understand what's happening in each cell.

• In order to view the status of training and deployment on Vertex AI, you can follow the instructions in the notebook containing illustrations.

Click Check my progress to verify the objective. Train and deploy a custom image classification model using Vertex AI

Congratulations!

Congratulations! In this lab, you walked through a machine learning experimentation workflow using TensorFlow's distribution strategies and Vertex AI's machine learning services to train and deploy a TensorFlow model to classify images from the CIFAR-10 dataset.

Next steps / Learn more

Read more about Distributed Training with Tensorflow.

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Manual Last Updated October 11, 2023

Lab Last Tested October 11, 2022

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