Hello text data: Set up your project and environment

To learn more, run the "Create, train, and deploy an AutoML text classification model" Jupyter notebook in one of the following environments:

Run in Colab | Open in Vertex AI Workbench user-managed notebooks | View on GitHub

This tutorial walks you through the required steps to train and get predictions from your text classification model in the Google Cloud console. If you plan to use the Vertex AI SDK for Python, make sure that the service account initializing the client has the Vertex AI Service Agent (roles/aiplatform.serviceAgent) IAM role.

In this tutorial, you'll train an AutoML model by using a collection of crowd-sourced "happy moments" from the Kaggle open-source dataset HappyDB. The resulting model classifies happy moments into categories reflecting the causes of happiness.

Note: The data is made available through a Creative Commons CCO: Public Domain license.

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To follow step-by-step guidance for this task directly in the Google Cloud console, click Guide me:

Guide me

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For this part of the tutorial, you set up your Google Cloud project to use Vertex AI and a Cloud Storage bucket that contains the documents for training your AutoML model.

This tutorial has several pages:

1. Setting up your project and environment.

2. Creating a text classification dataset .

3. Training an AutoML text classification model.

4. Deploy model to an endpoint and send a prediction.

5. Cleaning up your project.

Each page assumes that you have already performed the instructions from the previous pages of the tutorial.

Set up your project

Complete the following steps before using Vertex AI functionality.

1. In the Google Cloud console, go to the project selector page.

   Go to project selector

2. Select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

3. Make sure that billing is enabled for your Google Cloud project.

4. Open Cloud Shell to execute the commands listed in this tutorial. Cloud Shell is an interactive shell environment for Google Cloud that lets you manage your projects and resources from your web browser.
Go to Cloud Shell
5. In the Cloud Shell, set the current project to your Google Cloud project ID and store it in the projectid shell variable:

     gcloud config list project
     gcloud config set project PROJECT_ID
     projectid=PROJECT_ID
     echo $projectid

   Replace PROJECT_ID with your project ID. You can locate your project ID in the Google Cloud console. For more information, see Find your project ID.

6. Enable the IAM, Compute Engine, Notebooks, Cloud Storage, and Vertex AI APIs:

   gcloud services enable iam.googleapis.com  compute.googleapis.com notebooks.googleapis.com storage.googleapis.com aiplatform.googleapis.com

7. Grant roles to your Google Account. Run the following command once for each of the following IAM roles: roles/aiplatform.user, roles/storage.admin

   gcloud projects add-iam-policy-binding PROJECT_ID --member="user:EMAIL_ADDRESS" --role=ROLE

   • Replace PROJECT_ID with your project ID.
   • Replace EMAIL_ADDRESS with your email address.
   • Replace ROLE with each individual role.
The Vertex AI User (roles/aiplatform.user) IAM role provides access to use all resources in Vertex AI. Along with that role, you need the Storage Admin (roles/storage.admin) role to store the document's training dataset in Cloud Storage.

Create a Cloud Storage bucket and copy the sample dataset

Create a Cloud Storage bucket to store the documents that you use to train your AutoML model.

1. Open Cloud Shell.

2. Set the PROJECT_ID variable to the ID of your project.

   export PROJECT_ID=PROJECT_ID

3. Set the BUCKET variable, which you use to create a Cloud Storage bucket.

   export BUCKET=${PROJECT_ID}-lcm

4. Create a Cloud Storage bucket in the us-central1 region with the BUCKET variable.

   gsutil mb -p ${PROJECT_ID} -l us-central1 gs://${BUCKET}/

5. Copy the happiness.csv sample training dataset into your bucket.

   gsutil -m cp -R gs://cloud-ml-data/NL-classification/happiness.csv gs://${BUCKET}/text/

What's next

Follow the next page of this tutorial to use the Vertex AI console to create a text classification dataset and import the documents you copied to your Cloud Storage bucket.