Google Cloud Storage (GCS)

How to store artifacts using GCP Cloud Storage

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The GCS Artifact Store is an Artifact Store flavor provided with the GCP ZenML integration that uses the Google Cloud Storage managed object storage service to store ZenML artifacts in a GCP Cloud Storage bucket.

When would you want to use it?

Running ZenML pipelines with the local Artifact Store is usually sufficient if you just want to evaluate ZenML or get started quickly without incurring the trouble and the cost of employing cloud storage services in your stack. However, the local Artifact Store becomes insufficient or unsuitable if you have more elaborate needs for your project:

  • if you want to share your pipeline run results with other team members or stakeholders inside or outside your organization

  • if you have other components in your stack that are running remotely (e.g. a Kubeflow or Kubernetes Orchestrator running in public cloud).

  • if you outgrow what your local machine can offer in terms of storage space and need to use some form of private or public storage service that is shared with others

  • if you are running pipelines at scale and need an Artifact Store that can handle the demands of production grade MLOps

In all these cases, you need an Artifact Store that is backed by a form of public cloud or self-hosted shared object storage service.

You should use the GCS Artifact Store when you decide to keep your ZenML artifacts in a shared object storage and if you have access to the Google Cloud Storage managed service. You should consider one of the other Artifact Store flavors if you don't have access to the GCP Cloud Storage service.

How do you deploy it?

The GCS Artifact Store flavor is provided by the GCP ZenML integration, you need to install it on your local machine to be able to register a GCS Artifact Store and add it to your stack:

zenml integration install gcp -y

The only configuration parameter mandatory for registering a GCS Artifact Store is the root path URI, which needs to point to a GCS bucket and takes the form gs://bucket-name. Please read the Google Cloud Storage documentation on how to configure a GCS bucket.

With the URI to your GCS bucket known, registering an GCS Artifact Store can be done as follows:

# Register the GCS artifact store
zenml artifact-store register gs_store -f gcp --path=gs://bucket-name

# Register and set a stack with the new artifact store
zenml stack register custom_stack -a gs_store ... --set

Depending on your use-case, however, you may also need to provide additional configuration parameters pertaining to authentication to match your deployment scenario.

Configuring a GCS Artifact Store in can be a complex and error-prone process, especially if you plan on using it alongside other stack components running in the Google cloud. You might consider referring to the ZenML Cloud Guide for a more holistic approach to configuring full GCP-based stacks for ZenML.

Authentication Methods

Integrating and using a GCS Artifact Store in your pipelines is not possible without employing some form of authentication. ZenML currently provides two options for configuring GCP credentials, the recommended one being to use a Secrets Manager in your stack to store the sensitive information in a secure location.

This method uses the implicit GCP authentication available in the environment where the ZenML code is running. On your local machine, this is the quickest way to configure a GCS Artifact Store. You don't need to supply credentials explicitly when you register the GCS Artifact Store, as it leverages the local credentials and configuration that the Google Cloud CLI stores on your local machine. However, you will need to install and set up the Google Cloud CLI on your machine as a prerequisite, as covered in the Google Cloud documentation, before you register the GCS Artifact Store.

The implicit authentication method needs to be coordinated with other stack components that are highly dependent on the Artifact Store and need to interact with it directly to function. If these components are not running on your machine, they do not have access to the local Google Cloud CLI configuration and will encounter authentication failures while trying to access the GCS Artifact Store:

  • Orchestrators need to access the Artifact Store to manage pipeline artifacts

  • Step Operators need to access the Artifact Store to manage step level artifacts

  • Model Deployers need to access the Artifact Store to load served models

These remote stack components can still use the implicit authentication method: if they are also running within Google Kubernetes Engine, ZenML will try to load credentials from the Google compute metadata service. In order to take advantage of this feature, you must have configured a Service Account with the proper permissions or enabled Workload Identity when you launched your GKE cluster. These mechanisms allow Google workloads like GKE pods to access other Google services without requiring explicit credentials.

If you have remote stack components that are not running in GKE, or if you are unsure how to configure them to use Service Accounts or Workload Identity, you should use one of the other authentication methods.

For more, up-to-date information on the GCS Artifact Store implementation and its configuration, you can have a look at the API docs.

How do you use it?

Aside from the fact that the artifacts are stored in GCP Cloud Storage, using the GCS Artifact Store is no different from using any other flavor of Artifact Store.

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