Containerized Model Serving using Azure Container Registry

Question

Pulumi · Accepted Answer

Containerized model serving is the process of deploying machine learning models as containerized applications. This enables easy scaling, versioning, and management of your machine learning models in production environments. In Azure, you can use Azure Container Registry (ACR) to store and manage your container images, and then use Azure Container Instances (ACI) or Azure Kubernetes Service (AKS) to run these containers.

Here, I will guide you through the creation of an Azure Container Registry using Pulumi, which will be the first step towards serving your containerized model. Once the registry is created, you can push your Docker images containing your model serving application to the registry.

Before proceeding with the code, make sure you have the following prerequisites in place:

1. Pulumi CLI installed and configured with your Azure account.
2. Azure CLI installed and logged in to your Azure account.
3. Python 3.x installed.

The Python program below will create an instance of the Azure Container Registry.

```python
import pulumi
from pulumi_azure_native import containerregistry

# Create an Azure Resource Group
resource_group = containerregistry.ResourceGroup("resource_group", location="WestUS")

# Create an Azure Container Registry
container_registry = containerregistry.Registry(
    "container_registry",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku=containerregistry.SkuArgs(name="Basic"), # Choosing the "Basic" tier for cost-effectiveness
    admin_user_enabled=True, # Enable admin user for simplicity in this example (not recommended for production)
)

# Export the login server and admin credentials of the registry
registry_login_server = pulumi.Output.all(container_registry.name, resource_group.name) \
    .apply(lambda args: f"{args[0]}.azurecr.io")

def get_admin_credentials(args):
    creds = containerregistry.list_registry_credentials(
        resource_group_name=args[1],
        registry_name=args[0]
    )
    return creds.username, creds.passwords[0].value

registry_admin_credentials = pulumi.Output.all(container_registry.name, resource_group.name) \
    .apply(get_admin_credentials)

pulumi.export('registry_login_server', registry_login_server)
pulumi.export('registry_username', registry_admin_credentials.apply(lambda creds: creds[0]))
pulumi.export('registry_password', registry_admin_credentials.apply(lambda creds: creds[1]))
```

This Pulumi program performs the following steps:

1. It imports the required Pulumi packages and the Azure Container Registry package.
2. Creates an Azure Resource Group to contain our registry. The resource group encompasses all the resources for an application.
3. Creates the Azure Container Registry where your Docker images will be stored.
4. Exports the registry's login server, username, and password which you will use to push and pull images from the registry.

Once you have this registry created, you can build a Docker image with your model serving application, push it to the newly created container registry, and then deploy it using Azure Container Instances or Azure Kubernetes Service.

To run the program:

- Save the script to a Python `.py` file.
- Run `pulumi up` from your command line in the same directory where you saved your `.py` file. Pulumi will handle the provisioning of the resources as described in the script. After confirmation, Pulumi applies the changes to your Azure subscription.
- Check the outputs provided by Pulumi after the update for the registry credentials.

Remember to replace "WestUS" with the Azure region where you'd like to deploy the resources if necessary. Also, you can choose a different SKU instead of "Basic" based on your needs. The admin user is enabled for ease of demonstration but consider using a service principal with finer-grained permissions for production workloads.