Configure secrets with Secret Manager

You can use Secret Manager's Python client with Vertex AI Pipelines to access secrets stored on Secret Manager.

Create a secret using Google Cloud console

Enable the Secret Manager API in Google Cloud console.
Go to the Secret Manager page in the Cloud console.

Go to the Secret Manager page
On the Secret Manager page, click Create Secret.
On the Create secret page, under Name, enter a name for the secret (for example `universe-secret).
To add a secret version when creating the initial secret, in the Secret value field, enter a value for the secret (for example 42).
Choose your region.
Click the Create secret button.

Build and run a pipeline with Python function based components

The following is a sample component that prints out the previously created secret.

Grant the service account that runs the pipeline with secrete manager permission. See the "Configure a service account with granular permissions" section of Configure your Google Cloud project for Vertex AI Pipelines for more information.

Using Kubeflow Pipelines SDK, build a simple pipeline with one task.

 from kfp import compiler
 from kfp import dsl

 # A simple component that prints a secret stored in Secret Manager
 # Be sure to specify "google-cloud-secret-manager" as one of packages_to_install
 @dsl.component(
     packages_to_install=['google-cloud-secret-manager']
 )
 def print_secret_op(project_id: str, secret_id: str, version_id: str) -> str:
     from google.cloud import secretmanager

     secret_client = secretmanager.SecretManagerServiceClient()
     secret_name = f'projects/{project_id}/secrets/{secret_id}/versions/{version_id}'
     response = secret_client.access_secret_version(request={"name": secret_name})
     payload = response.payload.data.decode("UTF-8")
     answer = "The secret is: {}".format(payload)
     print(answer)
     return answer

 # A simple pipeline that contains a single print_secret task
 @dsl.pipeline(
     name='secret-manager-demo-pipeline')
 def secret_manager_demo_pipeline(project_id: str, secret_id: str, version_id: str):
     print_secret_task = print_secret_op(project_id, secret_id, version_id)

 # Compile the pipeline
 compiler.Compiler().compile(pipeline_func=secret_manager_demo_pipeline,
                             package_path='secret_manager_demo_pipeline.yaml')

Run the pipeline using the Vertex AI SDK.
```
 from google.cloud import aiplatform

 parameter_values = {
     "project_id": PROJECT_ID,
     "secret_id": SECRET_ID,
     "version_id": VERSION_ID
 }

 aiplatform.init(
     project=PROJECT_ID,
     location=REGION,
 )

 job = aiplatform.PipelineJob(
     display_name=f'test-secret-manager-pipeline',
     template_path='secret_manager_demo_pipeline.yaml',
     pipeline_root=PIPELINE_ROOT,
     enable_caching=False,
     parameter_values=parameter_values
 )

 job.submit(
     service_account=SERVICE_ACCOUNT
 )
```
Replace the following:
- PROJECT_ID: The Google Cloud project that this pipeline runs in.
- SECRET_ID: The secret id created in previous steps (for example universe-secret).
- VERSION_ID: The version name of the secret.
- REGION: The region that this pipeline runs in.
- PIPELINE_ROOT: Specify a Cloud Storage URI that your pipelines service account can access. The artifacts of your pipeline runs are stored within the pipeline root.
- SERVICE_ACCOUNT: The email address of the service account you created with Secret Manager Accessor permission.

In the output of the job.submit() function, you should be able to click the link that brings you to view the pipeline execution in the Google Cloud console.