AI Batch Processing with Azure VM Scale Sets.

Question

Pulumi · Accepted Answer

To accomplish AI Batch Processing in Azure utilizing Virtual Machine Scale Sets (VMSS), you will need to set up a VMSS with a specified size and configuration that matches the processing loads of your AI tasks. Azure VMSS allows you to create and manage a group of load balanced VMs. The number of VM instances can automatically increase or decrease in response to demand or a defined schedule, hence providing a perfect scenario for batch processing.

We will be creating a VMSS with an instance of Azure Batch AI, which is a cloud-based service that provides parallel and distributed compute resources with AI workload optimization. Even though Azure Batch AI service is not listed directly in the Pulumi resources, Azure Batch allows you to run large-scale parallel and high-performance computing batch jobs efficiently, which can be integrated with machine learning tasks.

Here is the step-by-step program written in Python. This program will set up VMSS configured for AI Batch processing and explain what each part does:

1. **Define the Azure Resource Group**: A resource group is a container that holds related resources for an Azure solution.
2. **Create a Virtual Machine Scale Set**: We'll create a VMSS with a simple Ubuntu image. For actual batch processing, you would configure the scale set with an image that contains your AI processing software or dependencies.
3. **Link to Azure Batch**: Even though we're not explicitly showing the creation of Azure Batch resources here, you would connect the VMs launched within your VMSS to Azure Batch for job scheduling and auto-scaling based on processing needs.

Let's move on to the program:

```python
import pulumi
import pulumi_azure_native as azure_native
from pulumi_azure_native import compute

# Define an Azure resource group. This is where all our resources will reside.
resource_group = azure_native.resources.ResourceGroup('example-resources')

# Define the VM Scale Set properties. Here we are using a simple UbuntuServer image.
# You would typically use a custom image with your AI software and dependencies configured.
vmss_props = compute.VirtualMachineScaleSetArgs(
    resource_group_name=resource_group.name,
    sku=compute.SkuArgs(
        name="Standard_DS1_v2",  # VM size and type
        capacity=2  # Number of VM instances.
    ),
    overprovision=False,
    upgrade_policy=compute.UpgradePolicyArgs(
        mode=compute.UpgradeMode.MANUAL
    ),
    virtual_machine_profile=compute.VirtualMachineScaleSetVMProfileArgs(
        os_profile=compute.VirtualMachineScaleSetOSProfileArgs(
            computer_name_prefix="example-vmss",
            admin_username="adminuser",
            admin_password="Password1234!",
        ),
        storage_profile=compute.VirtualMachineScaleSetStorageProfileArgs(
            image_reference=compute.ImageReferenceArgs(
                publisher="Canonical",
                offer="UbuntuServer",
                sku="18.04-LTS",
                version="latest"
            ),
        ),
        network_profile=compute.VirtualMachineScaleSetNetworkProfileArgs(
            network_interface_configurations=[
                compute.VirtualMachineScaleSetNetworkConfigurationArgs(
                    name="example-vmss-nic",
                    primary=True,
                    ip_configurations=[
                        compute.VirtualMachineScaleSetIPConfigurationArgs(
                            name="internal",
                            subnet=compute.ApiEntityReferenceArgs(
                                id="/subscriptions/{subscription_id}/resourceGroups/{resource_group}/providers/Microsoft.Network/virtualNetworks/{vnet_name}/subnets/{subnet_name}"
                            ),
                        ),
                    ],
                ),
            ],
        ),
    ),
    location="westus",  # Choose the appropriate Azure region for your VMSS.
)

# Create the Virtual Machine Scale Set with the defined properties.
vm_scale_set = compute.VirtualMachineScaleSet("example-vmss", args=vmss_props)

# Export the ID of the scale set so we can interact with it later if necessary.
pulumi.export('vmss_id', vm_scale_set.id)
```

This Python Pulumi program sets up the VMSS with a lightweight Ubuntu image. For an actual AI Batch processing workload, you would provision VMSS with a custom image suitable for your processing requirements which could include pre-installed AI and machine learning libraries or GPU-enabled virtual machines for computations.

After setting up the VMSS, you would automate your workload by creating and running batch jobs using the connected Azure Batch service.

Please ensure you have the [Pulumi Azure Native Provider](https://www.pulumi.com/docs/reference/pkg/azure-native/) configured before running this program. Additionally, replace the placeholders like `{subscription_id}`, `{resource_group}`, `{vnet_name}`, and `{subnet_name}` with your own values.

This program assumes you have the Pulumi CLI installed and the Azure cloud provider configured. Once you have your Python environment set up, you can run this program using the Pulumi CLI to initiate the deployment of your VMSS for AI Batch processing.