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A simple guide to quickly set up a minimal stack on GCP.

{% hint style="warning" %} This is an older version of the ZenML documentation. To read and view the latest version please visit this up-to-date URL. {% endhint %}

Set up a minimal GCP stack

{% hint style="warning" %} The GCP integration currently only works for Python versions <3.11. The ZenML team is aware of this dependency clash/issue and is working on a fix. For now, please use Python <3.11 together with the GCP integration. {% endhint %}

This page aims to quickly set up a minimal production stack on GCP. With just a few simple steps you will set up a service account with specifically-scoped permissions that ZenML can use to authenticate with the relevant GCP resources.

{% hint style="warning" %} While this guide focuses on Google Cloud, we are seeking contributors to create a similar guide for other cloud providers. If you are interested, please create a pull request over on GitHub. {% endhint %}

1) Choose a GCP project

In the Google Cloud console, on the project selector page, select or create a Google Cloud project. Make sure a billing account is attached to this project to allow the use of some APIs.

This is how you would do it from the CLI if this is preferred.

gcloud projects create <PROJECT_ID> --billing-project=<BILLING_PROJECT>

{% hint style="info" %} If you don't plan to keep the resources that you create in this procedure, create a new project. After you finish these steps, you can delete the project, thereby removing all resources associated with the project. {% endhint %}

2) Enable GCloud APIs

The following APIs will need to be enabled within your chosen GCP project.

  • Cloud Functions API # For the vertex orchestrator
  • Cloud Run Admin API # For the vertex orchestrator
  • Cloud Build API # For the container registry
  • Artifact Registry API # For the container registry
  • Cloud Logging API # Generally needed

3) Create a dedicated service account

The service account should have these following roles.

  • AI Platform Service Agent
  • Storage Object Admin

These roles give permissions for full CRUD on storage objects and full permissions for compute within VertexAI.

4) Create a JSON Key for your service account

This json file will allow the service account to assume the identity of this service account. You will need the filepath of the downloaded file in the next step.

export JSON_KEY_FILE_PATH=<JSON_KEY_FILE_PATH>

5) Create a Service Connector within ZenML

The service connector will allow ZenML and other ZenML components to authenticate themselves with GCP.

{% tabs %} {% tab title="CLI" %}

zenml integration install gcp \
&& zenml service-connector register gcp_connector \
--type gcp \
--auth-method service-account \
--service_account_json=@${JSON_KEY_FILE_PATH} \
--project_id=<GCP_PROJECT_ID>

{% endtab %}

{% tab title="Dashboard" %}

Choose the GCP Connector

Paste the entire contents of the key.json here

Make sure GCR, GCS and Generic GCP Resources are all selected here.

{% endtab %} {% endtabs %}

6) Create Stack Components

Artifact Store

Before you run anything within the ZenML CLI, head on over to GCP and create a GCS bucket, in case you don't already have one that you can use. Once this is done, you can create the ZenML stack component as follows:

{% tabs %} {% tab title="CLI" %}

export ARTIFACT_STORE_NAME=gcp_artifact_store

# Register the GCS artifact-store and reference the target GCS bucket
zenml artifact-store register ${ARTIFACT_STORE_NAME} --flavor gcp \
    --path=gs://<YOUR_BUCKET_NAME>

# Connect the GCS artifact-store to the target bucket via a GCP Service Connector
zenml artifact-store connect ${ARTIFACT_STORE_NAME} -i

{% hint style="info" %} Head on over to our docs to learn more about artifact stores and how to configure them. {% endhint %} {% endtab %}

{% tab title="Dashboard" %}

Choose the GCP Artifact Store.

Choose the name of your Artifact Store and the Connector you just created. This will allow you to pick the bucket of your choice.

{% endtab %} {% endtabs %}

Orchestrator

This guide will use Vertex AI as the orchestrator to run the pipelines. As a serverless service Vertex is a great choice for quick prototyping of your MLOps stack. The orchestrator can be switched out at any point in the future for a more use-case- and budget-appropriate solution.

{% tabs %} {% tab title="CLI" %}

export ORCHESTRATOR_NAME=gcp_vertex_orchestrator

# Register the GCS artifact-store and reference the target GCS bucket
zenml orchestrator register ${ORCHESTRATOR_NAME} --flavor=vertex 
  --project=<PROJECT_NAME> --location=europe-west2

# Connect the GCS orchestrator to the target gcp project via a GCP Service Connector
zenml orchestrator connect ${ORCHESTRATOR_NAME} -i

{% hint style="info" %} Head on over to our docs to learn more about orchestrators and how to configure them. {% endhint %} {% endtab %}

{% tab title="Dashboard" %}

Select the Vertex Orchestrator

Name it, Select the Connector and set an appropriate location. All other fields are optional.

{% endtab %} {% endtabs %}

Container Registry

{% tabs %} {% tab title="CLI" %}

export CONTAINER_REGISTRY_NAME=gcp_container_registry

zenml container-registry register ${CONTAINER_REGISTRY_NAME} --flavor=gcp --uri=<GCR-URI>

# Connect the GCS orchestrator to the target gcp project via a GCP Service Connector
zenml container-registry connect ${CONTAINER_REGISTRY_NAME} -i

{% hint style="info" %} Head on over to our docs to learn more about container registries and how to configure them. {% endhint %} {% endtab %}

{% tab title="Dashboard" %}

Choose the GCP Container Registry.

Name it and select the connector.

{% endtab %} {% endtabs %}

7) Create Stack

{% tabs %} {% tab title="CLI" %}

export STACK_NAME=gcp_stack

zenml stack register ${STACK_NAME} -o ${ORCHESTRATOR_NAME} \
    -a ${ARTIFACT_STORE_NAME} -c ${CONTAINER_REGISTRY_NAME} --set

{% hint style="info" %} In case you want to also add any other stack components to this stack, feel free to do so. {% endhint %} {% endtab %}

{% tab title="Dashboard" %}

Combine the three stack components and you have your minimal GCP stack. Feel free to add any other component of your choice as well.

{% endtab %} {% endtabs %}

And you're already done!

Just like that, you now have a fully working GCP stack ready to go. Feel free to take it for a spin by running a pipeline on it.

Cleanup

If you do not want to use any of the created resources in the future, simply delete the project you created.

gcloud project delete <PROJECT_ID_OR_NUMBER>
```<!-- For scarf -->
<figure><img alt="ZenML Scarf" referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" /></figure>