1. Isolating AI Workloads with Azure VNet Subnets


    Isolating workloads within a virtual network (VNet) is a common cloud practice to enhance security and control network traffic. In Azure, a VNet is an isolated network within the Azure cloud that you can configure. Subnets within a VNet are used to further segment the network, for instance, to separate AI workloads from other parts of your system.

    When you set up a VNet in Azure, you can define one or more subnets within it. To isolate AI workloads, you would create a dedicated subnet for them. This ensures that only traffic that you permit can flow into and out of the area where AI operations happen.

    In Pulumi, the azure-native SDK is typically used to create and manage resources within Azure. To create a VNet and a subnet within it for AI workloads isolation, you'd use the VirtualNetwork and Subnet resources from the azure-native.network module.

    Here's a program that illustrates how to create a VNet and a subnet:

    import pulumi import pulumi_azure_native.network as network # Configure the name of the VNet and Subnet which we will create vnet_name = "ai-workload-vnet" subnet_name = "ai-workload-subnet" # Create an Azure Resource Group resource_group = network.ResourceGroup("ai_workload_rg", resource_group_name="ai_workload_rg") # Create a Virtual Network (VNet) for the AI workloads vnet = network.VirtualNetwork("aiWorkloadVNet", resource_group_name=resource_group.name, location="WestUS", # You can choose a different Azure location virtual_network_name=vnet_name, address_space=network.AddressSpaceArgs( address_prefixes=[""], # CIDR block for the VNet )) # Create a subnet within the VNet for the AI workloads subnet = network.Subnet("aiWorkloadSubnet", resource_group_name=resource_group.name, address_prefix="", # CIDR block for the subnet, must be within the VNet's address space virtual_network_name=vnet.name, subnet_name=subnet_name) # Export the VNet and Subnet names pulumi.export("virtual_network_name", vnet.name) pulumi.export("subnet_name", subnet.name)

    In this program:

    • We first create a resource group named ai_workload_rg, which acts as a container that holds related Azure resources for your AI workloads.

    • Then we define a virtual network called ai-workload-vnet with a CIDR block of This network will be isolated within Azure, and we can control exactly what traffic is allowed in and out. The location WestUS is specified for the resources, but you can choose any Azure region that supports VNets.

    • Inside this VNet, we create a subnet named ai-workload-subnet with a smaller CIDR block, dedicated for AI workloads. This subnet should only be used for resources related to the AI workload to maintain isolation. It must be a part of the VNet's address space.

    • Lastly, the names of the VNet and Subnet are exported. This means that after you run pulumi up, you will see the names of these resources in your Pulumi stack's outputs. They can be used to reference these resources in other parts of your Pulumi codebase.

    Using Pulumi, you can define infrastructure as code, version control it, and deploy changes through a CI/CD pipeline. Pulumi also enables you to define policies and templates to standardize and scale your cloud setup.