AI Workload Isolation and Network Segmentation in the Cloud.

Question

Pulumi · Accepted Answer

To achieve AI workload isolation and network segmentation in the cloud, we'll use cloud resources that allow us to create isolated networks, which is essentially a way to segment the network within the cloud environment. These isolated networks can contain our AI workloads that we want to partition off from other workloads for security, compliance, or performance reasons.

Two Azure-native Pulumi resources that can help us achieve this isolation are `azure-native.avs.WorkloadNetworkSegment` and `azure-native.network.VirtualNetworkPeering`.

- **WorkloadNetworkSegment**: This resource is used within the Azure VMware Solution to create segments that can connect and isolate workloads. This can be particularly useful when we want to segment AI workloads within a VMware-based environment in Azure.

- **VirtualNetworkPeering**: This resource allows different virtual networks in Azure to be connected and, at the same time, remain isolated from each other. We can use it to enforce network segregation and ensure that workloads in one virtual network can't access resources in another unless explicitly permitted.

Here’s a basic structure of a Pulumi Python program that sets up an isolated network segment and connects it to a virtual network using these resources:

```python
import pulumi
import pulumi_azure_native as azure_native

# Initialize the Azure Resource Group
resource_group = azure_native.resources.ResourceGroup('ai-workload-resource-group')

# Create an Azure VMware Solution Workload Network Segment
# This establishes a subset of the network where you can deploy AI workloads.
workload_network_segment = azure_native.avs.WorkloadNetworkSegment('ai-workload-network-segment',
    resource_group_name=resource_group.name,
    private_cloud_name='your-private-cloud',  # Replace with your private cloud name
    segment_id='segment1',  # An arbitrary ID for your network segment
    display_name='AI Workload Segment',
    subnet=azure_native.avs.SubnetArgs(
        gateway_address='192.168.1.1/24',  # Define the gateway for this segment
        dhcp_ranges=["192.168.1.10-192.168.1.20"],  # DHCP range for this segment
    )
)

# Create a new Virtual Network
ai_workload_vnet = azure_native.network.VirtualNetwork('ai-workload-vnet',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    address_space=azure_native.network.AddressSpaceArgs(
        address_prefixes=['10.0.0.0/16'],  # CIDR block for the virtual network
    )
)

# Create a peering connection between the Workload Network Segment and the Virtual Network
# This allows for isolated communication between them.
vnet_peering = azure_native.network.VirtualNetworkPeering('ai-workload-vnet-peering',
    resource_group_name=resource_group.name,
    virtual_network_name=ai_workload_vnet.name,
    remote_virtual_network=azure_native.network.SubResourceArgs(
        id=workload_network_segment.id,
    ),
    allow_virtual_network_access=True,  # Configures the peering to allow access between the networks
)

# Export the IDs of the resources to access them if needed.
pulumi.export('resource_group_id', resource_group.id)
pulumi.export('workload_network_segment_id', workload_network_segment.id)
pulumi.export('ai_workload_vnet_id', ai_workload_vnet.id)
pulumi.export('vnet_peering_id', vnet_peering.id)
```

In this code:

- We initialize a resource group, which is a container that holds related resources for an Azure solution.
- We then create a Workload Network Segment within an existing private cloud on Azure.
- We also create a Virtual Network where we can deploy our AI workloads.
- Finally, we establish peering between the VMware Solution segment and the Virtual Network, allowing for secure communication while maintaining isolation.

To use this code, replace placeholders such as `'your-private-cloud'` with the actual values corresponding to your Azure subscription and environment. Once applied, this program will set up the network isolation for your AI workloads in the cloud.

Remember to log in to the Azure CLI and set your desired subscription, and ensure you have the appropriate permissions to create these resources before running the Pulumi program. After setting up Pulumi for Azure, you can deploy this stack with the `pulumi up` command, which will provision the resources as defined.