Consistent Naming for AI Computing Resources in Azure Virtual Networks

Question

Pulumi · Accepted Answer

To maintain consistency in naming AI computing resources within an Azure Virtual Network, it is important to adopt a naming convention that aligns with the purpose of each resource, adheres to any organizational standards, and stays within the naming rules and restrictions of Azure resources.

When provisioning resources with Pulumi, you can enforce a naming convention by defining a consistent naming strategy in your Python code and then using these names while creating each Azure resource.

Below is an example of how you can set up a Pulumi Python program that ensures consistent naming for AI computing resources, such as virtual machines and related network resources, within an Azure Virtual Network.

In this example, I'll demonstrate how you can create a virtual network, a subnet, a network interface, and a virtual machine with AI capabilities using an Azure GPU-enabled VM size, all with consistent names. The resource names will include a prefix indicating their purpose, followed by a unique identifier or suffix where necessary.

```python
import pulumi
import pulumi_azure_native as azure_native

# Define a consistent naming convention for resources
prefix = "ai-compute"

# Create a resource group
resource_group = azure_native.resources.ResourceGroup(f"{prefix}-rg")

# Define the virtual network
vnet = azure_native.network.VirtualNetwork(
    f"{prefix}-vnet",
    # Associate the virtual network with the newly created resource group
    resource_group_name=resource_group.name,
    address_space=azure_native.network.AddressSpaceArgs(
        address_prefixes=["10.0.0.0/16"]
    ),
    location=resource_group.location
)

# Create a subnet within the virtual network
subnet = azure_native.network.Subnet(
    f"{prefix}-subnet",
    # Associate the subnet with the virtual network and resource group
    resource_group_name=resource_group.name,
    virtual_network_name=vnet.name,
    address_prefix="10.0.1.0/24"
)

# Define a network interface for a virtual machine
network_interface = azure_native.network.NetworkInterface(
    f"{prefix}-nic",
    # Associate the network interface with the subnet and resource group
    resource_group_name=resource_group.name,
    location=resource_group.location,
    ip_configurations=[azure_native.network.NetworkInterfaceIPConfigurationArgs(
        name=f"{prefix}-ip-config",
        subnet=azure_native.network.SubnetArgs(
            id=subnet.id
        ),
        private_ip_allocation_method="Dynamic"
    )]
)

# Define a virtual machine for AI workloads
vm = azure_native.compute.VirtualMachine(
    f"{prefix}-vm",
    # Associate the virtual machine with the resource group
    resource_group_name=resource_group.name,
    location=resource_group.location,
    network_profile=azure_native.compute.NetworkProfileArgs(
        network_interfaces=[azure_native.compute.NetworkInterfaceReferenceArgs(
            id=network_interface.id,
            primary=True
        )]
    ),
    hardware_profile=azure_native.compute.HardwareProfileArgs(
        vm_size="Standard_NC6"  # GPU-enabled VM size for AI workloads
    ),
    os_profile=azure_native.compute.OSProfileArgs(
        computer_name=f"{prefix}-vm",
        admin_username="azureuser",
        admin_password="ChangeThisPassword123"  # Replace with a secure password
    ),
    storage_profile=azure_native.compute.StorageProfileArgs(
        image_reference=azure_native.compute.ImageReferenceArgs(
            publisher="Canonical",
            offer="UbuntuServer",
            sku="18.04-LTS",
            version="latest"
        ),
        os_disk=azure_native.compute.OSDiskArgs(
            create_option="FromImage",
            name=f"{prefix}-os-disk"
        )
    )
)

# Export the public IP address of the virtual machine (if you give it a public IP)
# pulumi.export("public_ip", network_interface.ip_configurations.apply(lambda configs: configs[0].public_ip_address))

# The above is a simple example, in a real-world scenario, you would secure your infrastructure further,
# handle sensitive data like passwords more securely, utilize additional resources like managed identities, and so forth.
```

In the code above, we have adopted a basic naming convention by using the `prefix` variable, which acts as a starting point for every resource name. It's vital to customize this according to your specific requirements and Azure's naming rules.

* All resources are tagged with the prefix "ai-compute". This indicates their role within our infrastructure.
* `ResourceGroup` is a container that holds related resources for an Azure solution.
* `VirtualNetwork` and `Subnet` are foundational networking components that allow VMs to communicate with each other.
* `NetworkInterface` represents a network interface for an Azure VM that allows it to communicate with internet, Azure, and on-premises resources.
* `VirtualMachine` is an Azure VM configured here with a GPU size suitable for AI and computes tasks.
* The example shows how to create a virtual machine and associate it with the network interface, subnet, and resource group using our defined naming convention.
* SSL/TLS security, advanced networking settings, identity management, and cost monitoring are some of the additional considerations you would typically need in a production environment.

This example is meant to illustrate how Pulumi can deploy Azure resources with consistent naming; however, in a complete application, you should consider additional factors such as security, scalability, and monitoring.