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.
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:
Setting up your project and environment.
Each page assumes that you have already performed the instructions from the previous pages of the tutorial.
Set up your project and environment
Complete the following steps before using Vertex AI functionality.
-
In the Google Cloud console, go to the project selector page.
-
Select or create a Google Cloud project.
-
Make sure that billing is enabled for your Google Cloud project.
- Open Cloud Shell. 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
- In the Cloud Shell, set the current project to your Google Cloud
project ID and store it in the
projectid
shell variable: 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.gcloud config set project PROJECT_ID && projectid=PROJECT_ID && echo $projectid
-
Enable the IAM, Compute Engine, Notebooks, Cloud Storage, and Vertex AI APIs:
gcloud services enable iam.googleapis.com
compute.googleapis.com notebooks.googleapis.com storage.googleapis.com aiplatform.googleapis.com -
Grant roles to your user account. Run the following command once for each of the following IAM roles:
roles/aiplatform.user, roles/storage.admin
gcloud projects add-iam-policy-binding PROJECT_ID --member="USER_IDENTIFIER" --role=ROLE
- Replace
PROJECT_ID
with your project ID. -
Replace
USER_IDENTIFIER
with the identifier for your user account. For example,user:myemail@example.com
. - Replace
ROLE
with each individual role.
The Vertex AI User ( - Replace
roles/aiplatform.user
) IAM
role provides access to use all resources in Vertex AI. The
Storage Admin
(roles/storage.admin
) lets you store the document's
training dataset in Cloud Storage.
Create a Cloud Storage bucket and copy the sample dataset
Create a Cloud Storage bucket to store the documents that you use to train your AutoML model.
Set the PROJECT_ID variable to the ID of your project.
export PROJECT_ID=PROJECT_ID
Set the BUCKET variable, which you use to create a Cloud Storage bucket.
export BUCKET=${PROJECT_ID}-lcm
Create a Cloud Storage bucket in the
us-central1
region with theBUCKET
variable.gcloud storage buckets create gs://${BUCKET}/ --project=${PROJECT_ID} --location=us-central1
Copy the
happiness.csv
sample training dataset into your bucket.gcloud storage cp gs://cloud-ml-data/NL-classification/happiness.csv gs://${BUCKET}/text/ --recursive
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.