Dynamic DataDisk Attachment for Scalable Azure AI Environments

Question

Pulumi · Accepted Answer

To create a dynamic data disk attachment for scalable Azure AI environments, you'll need to create and configure several Azure resources using Pulumi. Specifically, you will need to create an Azure Virtual Machine Scale Set, which allows you to create and manage a group of load-balanced VMs. Then, you will attach a managed disk to each VM in the set for additional data storage, which is crucial for AI workloads that may require large amounts of data.

Below is a Pulumi program that accomplishes these tasks. It's written in Python and uses the `azure-native` provider for Pulumi, which provides access to all of the Azure services in their native form. The resources used in this program include `VirtualMachineScaleSet`, for creating VM scale sets, and `Disk`, for creating managed disks that will be attached to the VMs in the scale set.

In this example, we'll create:
1. A resource group to contain our resources.
2. A managed disk that will act as our data disk.
3. A virtual machine scale set, which will automatically have the data disk attached to each instance.

Here's a detailed explanation of the program:

```python
import pulumi
import pulumi_azure_native as azure_native

# Creating a new resource group
resource_group = azure_native.resources.ResourceGroup('aiResourceGroup')

# Creating a managed disk that will be used as the data disk.
data_disk = azure_native.compute.Disk(
    'aiDataDisk',
    location=resource_group.location,
    resource_group_name=resource_group.name,
    creation_data=azure_native.compute.CreationDataArgs(
        create_option='Empty'  # Disk will be empty upon creation.
    ),
    disk_size_gb=1024  # Size of the disk in GB.
)

# Creating a Virtual Machine Scale Set with a Managed Disk attached to each of the VMs
vm_scale_set = azure_native.compute.VirtualMachineScaleSet(
    'aiVmScaleSet',
    location=resource_group.location,
    resource_group_name=resource_group.name,
    sku=azure_native.compute.SkuArgs(
        capacity=3,  # Number of VM instances to be provisioned in the VM Scale Set.
        name='Standard_DS1_v2'  # VM size/sku
    ),
    os_profile=azure_native.compute.OSProfileArgs(
        admin_username='adminuser',  # Replace with your admin username
        computer_name_prefix='aicomp',  # Prefix for the computer names
        admin_password='P@ssw0rd1234',  # Replace with your admin password
    ),
    storage_profile=azure_native.compute.StorageProfileArgs(
        image_reference=azure_native.compute.ImageReferenceArgs(
            # Define the image to be used for VM instances.
            publisher='Canonical',
            offer='UbuntuServer',
            sku='18.04-LTS',
            version='latest'
        ),
        data_disks=[
            azure_native.compute.VirtualMachineScaleSetDataDiskArgs(
                lun=0,  # Logical unit number for identifying the disk
                caching='ReadOnly',  # Disk caching mode
                create_option='Attach',  # Attach existing disk
                disk_size_gb=1024,  # Disk size in GB, should match the data disk created.
                managed_disk=azure_native.compute.ManagedDiskParametersArgs(
                    id=data_disk.id  # ID of the managed disk to attach
                )
            )
        ]
    ),
)

# Export the public IP address of the VM scale set
pulumi.export('vmssPublicIP', vm_scale_set.unique_vms_public_ip_address)

```

Let's break down the program:

- We begin by importing the required modules from Pulumi.
- We create a resource group to contain all of our other resources. This helps with organizing and managing resources within Azure.
- We create a managed disk that will be used as the data disk. We specify its size (1024 GB in this example) and the creation option as 'Empty', meaning it will be created without any data.
- We create a virtual machine scale set (VMSS) with our desired SKU, admin credentials, and image reference to set up the VM instances. In the `storage_profile` parameter, we specify the configuration for data disks, including the logical unit number (LUN), caching settings, and the disk size. We use `create_option='Attach'` to specify that we want to attach an existing managed disk, which we reference by the ID of the disk we created earlier.
- Finally, we export the public IP address of the VM scale set, which can be used to connect to your VM instances once they are running.

This program dynamically creates a scalable AI environment that automatically attaches data disks to each VM instance within the scale set. Now, say you run your AI applications, and they need to process large datasets or require high IOPS (input/output operations per second) for complex computations; this setup will enable your environment to scale with the computational needs.