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Showing content from http://cloud.google.com/vertex-ai/docs/pipelines/configure-project below:

Configure your Google Cloud project for Vertex AI Pipelines

Configure your Google Cloud project for Vertex AI Pipelines

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Before you use Vertex AI Pipelines to orchestrate your machine learning (ML) pipelines, you must set up your Google Cloud project. Some resources, such as the metadata store used by Vertex ML Metadata, are created in your Google Cloud project the first time that you run a pipeline.

Use the following instructions to configure your project for Vertex AI Pipelines.

  1. Create your Google Cloud project and configure it for use with Vertex AI Pipelines.

  2. If you don't specify a service account, Vertex AI Pipelines uses the Compute Engine default service account to run your pipelines. For more information about the Compute Engine default service account, see Using the Compute Engine Default Service Account.

    We recommend that you create a service account to run your pipelines and then grant this account granular permissions to the Google Cloud resources that are needed to run your pipeline.

  3. Vertex AI Pipelines uses Cloud Storage to store the artifacts of your pipeline runs. Create a Cloud Storage bucket and grant your service account access to this bucket.

  4. Vertex AI Pipelines uses Vertex ML Metadata to store the metadata created by your pipeline runs. When you run a pipeline for the first time, if the metadata store of your project doesn't exist, Vertex AI creates your project's metadata store.

    If you want your data encrypted using a customer-managed encryption key (CMEK), you can manually create your metadata store using a CMEK key before you run a pipeline. Otherwise, if there's no existing default metadata store in your project, Vertex AI creates your project's metadata store using the CMEK key used when you run the pipeline for the first time. After the metadata store is created, it uses a CMEK key that's different from the CMEK key used in a pipeline run.

Set up your Google Cloud project

Use the following instructions to create a Google Cloud project and configure it for use with Vertex AI Pipelines.

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  3. Verify that billing is enabled for your Google Cloud project.

  4. Enable the Vertex AI, Compute Engine, and Cloud Storage APIs.

    Enable the APIs

  5. Install the Google Cloud CLI.

  6. If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

  7. To initialize the gcloud CLI, run the following command:

    gcloud init

  8. After initializing the gcloud CLI, update it and install the required components:

    gcloud components update
gcloud components install beta
Configure a service account with granular permissions

When you run a pipeline, you can specify a service account. Your pipeline run acts with the permissions of this service account.

If you don't specify a service account, your pipeline run uses the Compute Engine default service account. For more information about the Compute Engine default service account, see Using the Compute Engine Default Service Account.

Configure a Cloud Storage bucket for pipeline artifacts

Vertex AI Pipelines stores the artifacts of your pipeline runs using Cloud Storage. Use the following instructions to create a Cloud Storage bucket and grant your service account (or the Compute Engine default service account) access to read and write objects in that bucket.

  1. Run the following command to create a Cloud Storage bucket in the region that you want to run your pipelines in.

    gcloud storage buckets create gs://BUCKET_NAME --location=BUCKET_LOCATION --project=PROJECT_ID

    Replace the following values:

    Learn more about creating Cloud Storage buckets.

  2. Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step.

    gcloud storage buckets add-iam-policy-binding gs://BUCKET_NAME \
--member=serviceAccount:SERVICE_ACCOUNT_ID@PROJECT_ID.iam.gserviceaccount.com \
--role=roles/storage.objectUser

    Replace the following values:

    Alternatively, if you prefer to use the Compute Engine default service account to run your pipelines, run the gcloud iam service-accounts list command to locate the project number for that account.

    gcloud iam service-accounts list

    The Compute Engine default service account is named like the following: PROJECT_NUMBER-compute@developer.gserviceaccount.com.

    Run the following command to grant the Compute Engine default service account access to read and write pipeline artifacts in the bucket that you created in the previous step.

    gcloud storage buckets add-iam-policy-binding gs://BUCKET_NAME \
--member=serviceAccount:PROJECT_NUMBER-compute@developer.gserviceaccount.com \
--role=roles/storage.objectUser

    Replace the following values:

    Learn more about controlling access to Cloud Storage buckets.

Use the following instructions to create a CMEK and set up a Vertex ML Metadata metadata store that uses this CMEK.

  1. Use Cloud Key Management Service to configure a customer-managed encryption key.

  2. Use the following REST call to create your project's default metadata store using your CMEK.

    Before using any of the request data, make the following replacements:

    HTTP method and URL:

    POST https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/metadataStores?metadata_store_id=default

    Request JSON body:

    {
  "encryption_spec": {
    "kms_key_name": "projects/PROJECT_ID/locations/LOCATION_ID/keyRings/KEY_RING/cryptoKeys/KEY_NAME"
  },
}

    To send your request, expand one of these options:

    curl (Linux, macOS, or Cloud Shell) Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

    Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory:

    cat > request.json << 'EOF'
{
  "encryption_spec": {
    "kms_key_name": "projects/PROJECT_ID/locations/LOCATION_ID/keyRings/KEY_RING/cryptoKeys/KEY_NAME"
  },
}
EOF

    Then execute the following command to send your REST request:

    curl -X POST \
         -H "Authorization: Bearer $(gcloud auth print-access-token)" \
         -H "Content-Type: application/json; charset=utf-8" \
         -d @request.json \
         "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/metadataStores?metadata_store_id=default"
    PowerShell (Windows) Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

    Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory:

    @'
{
  "encryption_spec": {
    "kms_key_name": "projects/PROJECT_ID/locations/LOCATION_ID/keyRings/KEY_RING/cryptoKeys/KEY_NAME"
  },
}
'@  | Out-File -FilePath request.json -Encoding utf8

    Then execute the following command to send your REST request:

    $cred = gcloud auth print-access-token
    $headers = @{ "Authorization" = "Bearer $cred" }
    Invoke-WebRequest `

        -Method POST `
        -Headers $headers `
        -ContentType: "application/json; charset=utf-8" `
        -InFile request.json `
        -Uri "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/metadataStores?metadata_store_id=default" | Select-Object -Expand Content

    You should receive a JSON response similar to the following:

    {
  "name": "projects/PROJECT_ID/locations/LOCATION_ID/operations/OPERATIONS_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.CreateMetadataStoreOperationMetadata",
    "genericMetadata": {
      "createTime": "2021-05-18T18:47:14.494997Z",
      "updateTime": "2021-05-18T18:47:14.494997Z"
    }
  }
}

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-07 UTC.

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