ML Model Versioning with GCP Artifact Registry

Question

Pulumi · Accepted Answer

Managing machine learning (ML) model versions is a critical task for maintaining and deploying models in production systems. Google Cloud Platform (GCP) provides Artifact Registry, a universal package manager that you can use to manage and version your ML models similarly to other artifacts like container images or software packages.

The Artifact Registry service allows you to store, manage, and secure your ML models in a way that supports the ML workflow, which includes versioning and retrieval of different model versions. It supports different repository formats such as Docker images, Maven packages, NPM packages, and it's also flexible enough for the custom format you might use for ML models.

In the following Pulumi Python program, we will create a repository in GCP Artifact Registry to store our ML model versions. We'll also set up IAM policy bindings to control access to the repository:

### Program Explanation:

1. **gcp.artifactregistry.Repository**: This resource is used to create a repository within the Artifact Registry. The `format` property specifies the format of the packages that the repository will store. For ML models, you might choose a Docker or a custom format.

2. **google-native.artifactregistry.v1.RepositoryIamPolicy**: This resource is used to set the IAM policy for the created repository, which can define who has what permissions (like the reader, writer, or admin) over the repository.

Here's the Pulumi Python program that accomplishes this:

```python
import pulumi
import pulumi_gcp as gcp

# Define the configuration for our GCP project and location.
project = 'my-gcp-project'  # Your Google Cloud project ID
location = 'us-central1'    # The location for the artifact registry

# Create an Artifact Registry repository to store ML models.
ml_model_repository = gcp.artifactregistry.Repository("ml-model-repository",
    project=project,
    location=location,
    description="Repository for ML models",
    format="docker"  # Assuming Docker format for the ML models, change as needed.
)

# Define the IAM policy for the repository.
repository_iam_policy = gcp.artifactregistry.RepositoryIamPolicy("ml-model-repository-iam",
    project=project,
    location=location,
    repository=ml_model_repository.name,
    bindings=[
        gcp.artifactregistry.RepositoryIamPolicyBindingArgs(
            role="roles/artifactregistry.reader",
            members=["serviceAccount:service-account-email@my-gcp-project.iam.gserviceaccount.com"],
        )
    ]
)

# Export the repository URL to use in CI/CD pipelines or other processes.
pulumi.export("repository_url", ml_model_repository.url)
```

### Usage Explanation:

- **Repository Creation**: The `gcp.artifactregistry.Repository` resource creates a new repository named "ml-model-repository". The repository is designated to store Docker-formatted ML models. It is important to choose the right format that matches your ML model packaging.

- **IAM Policy Configuration**: After setting up the repository, the `gcp.artifactregistry.RepositoryIamPolicy` resource configures access control using IAM. The example provided gives the read access to a specified service account. You will need to replace `service-account-email@my-gcp-project.iam.gserviceaccount.com` with the correct service account email of your environment.

- **Exporting Repository URL**: The program exports the URL of the repository, which can be used in your ML model deployment pipeline or any scripts for uploading and retrieving model versions.

### Note:
Before running the program, ensure you have authenticated with GCP and set up your Pulumi environment. You'll also want to replace the placeholders with the proper values specific to your GCP project and model packaging needs. The format of the repository (in this example, "docker") will depend on how you package your ML models. If they're Docker images, 'docker' is correct; if they're files or archives, you might need a custom solution or another supported format.

Run this Pulumi program by saving it to a file, for example, `main.py`, and execute it by running `pulumi up` in the terminal. Make sure you've selected the correct stack that corresponds to your environment within Pulumi.