Deploying Scalable AI Model Servers with ARM Templates

Question

Pulumi · Accepted Answer

To deploy scalable AI model servers using ARM Templates with Pulumi, we will utilize Azure Machine Learning Services and Azure Resource Manager (ARM) templates. We will first define an ARM template that specifies the infrastructure and configuration for deploying a scalable AI model server. Then, we'll use Pulumi to deploy the template as a Resource Group Template Deployment in Azure.

The AI model servers can be deployed using one of the services, such as Azure Machine Learning's online or batch endpoints, depending on the use case. Online endpoints are suitable for real-time inferencing, while batch endpoints are better for asynchronous processing on large datasets.

Here's the Pulumi program written in Python to perform the deployment:

```python
import pulumi
import pulumi_azure_native as azure_native

# Define the resource group where all the resources will be deployed
resource_group = azure_native.resources.ResourceGroup('model-serving-rg')

# Define the ARM template for deploying an AI model server. 
# This template should be prepared beforehand and should include all the necessary resources 
# and configurations for the AI model server.
# For example, you would have your ARM template in JSON format as a string or in a separate file.
template_json = """
{
  "$schema": "https://schema.management.azure.com/schemas/2015-01-01/deploymentTemplate.json#",
  "contentVersion": "1.0.0.0",
  "resources": [
    {
      "type": "Microsoft.MachineLearningServices/workspaces",
      "apiVersion": "2021-01-01",
      "name": "[parameters('workspaceName')]",
      "location": "[parameters('location')]",
      "identity": {
        "type": "SystemAssigned"
      },
      "properties": {
        "description": "[parameters('workspaceDescription')]"
      }
    },
    {
      "type": "Microsoft.MachineLearningServices/workspaces/computes",
      "apiVersion": "2021-01-01",
      "name": "[concat(parameters('workspaceName'), '/', parameters('computeName'))]",
      "location": "[parameters('location')]",
      "dependsOn": [
        "[resourceId('Microsoft.MachineLearningServices/workspaces', parameters('workspaceName'))]"
      ],
      "properties": {
        "vmSize": "[parameters('computeVmSize')]",
        "vmPriority": "Dedicated",
        "scaleSettings": {
          "minNodeCount": 0,
          "maxNodeCount": "[parameters('computeMaxNodeCount')]"
        },
        "computeType": "AmlCompute"
      }
    }
  ],
  "parameters": {
    "workspaceName": {
      "type": "string",
      "defaultValue": "my-ai-model-workspace"
    },
    "location": {
      "type": "string",
      "defaultValue": "East US"
    },
    "workspaceDescription": {
      "type": "string",
      "defaultValue": "AI Model Workspace Description"
    },
    "computeName": {
      "type": "string",
      "defaultValue": "my-ai-compute"
    },
    "computeVmSize": {
      "type": "string",
      "defaultValue": "STANDARD_NC6"
    },
    "computeMaxNodeCount": {
      "type": "int",
      "defaultValue": 4
    }
  }
}
"""

# Deploy the ARM template as a Resource Group Template Deployment
deployment = azure_native.resources.Deployment('ai-model-server-deployment',
    resource_group_name=resource_group.name,
    properties=azure_native.resources.DeploymentPropertiesArgs(
        mode=azure_native.resources.DeploymentMode.INCREMENTAL,
        template=template_json
    )
)

# Export the important information about the deployment
pulumi.export('resource_group', resource_group.name)
pulumi.export('deployment_name', deployment.name)
```

### Explanation:

1. **Resource Group**: We create a resource group named `model-serving-rg` which will contain all the resources related to our AI model servers.

2. **ARM Template**: The `template_json` variable holds our ARM template in JSON format. This template should be designed to include all the Azure resources required for the AI model server. In this example, the template provisions an Azure Machine Learning workspace and a compute cluster with auto-scaling capabilities.

3. **Deployment**: We instantiate the `Deployment` class from the `azure_native.resources` module for deploying our ARM template. We pass the `resource_group_name` from the resource group we defined and set `DeploymentMode.INCREMENTAL` to ensure that existing resources are unchanged if they are already in the desired state.

4. **Exports**: We export the resource group name and the deployment name. These can be used to retrieve the deployment details or be passed as inputs to other programs or stacks.

Remember to replace the placeholder ARM template with your actual ARM template JSON that correctly sets up the desired Azure resources for AI model serving. The keys for the 'parameters' in the ARM template should be adjusted according to your parameter names and default values.