Load Balanced Azure Kubernetes Service for ML Model Deployment

Question

Pulumi · Accepted Answer

To create a load-balanced Azure Kubernetes Service (AKS) for machine learning (ML) model deployment, we will use several Pulumi resources. These resources will allow us to set up an AKS cluster, configure load balancing, and prepare the infrastructure for the deployment of the ML model. We will use:

- `azure-native.containerservice.ManagedCluster`: Represents an AKS cluster on Azure, which is where the ML models will be deployed and run.
- `azure-native.network.LoadBalancer`: Represents a load balancer on Azure that will distribute incoming traffic across multiple replicas of your ML model, ensuring high availability and scalability.
- `azure-native.network.PublicIPAddress`: Represents a public IP address associated with the load balancer, which allows external access to the ML services.
- `azure-native.network.LoadBalancerBackendAddressPool`: A backend pool for the load balancer, where we will direct the traffic intended for our services.
- `azure-native.network.LoadBalancingRule`: The rule that handles how traffic is distributed to the services.
- `azure-native.network.VirtualNetwork`: A virtual network on which the AKS cluster and the load balancer will communicate.

Here's how you could create this infrastructure with Pulumi:

```python
import pulumi
from pulumi_azure_native import containerservice, resources, network

# Create an Azure Resource Group
resource_group = resources.ResourceGroup('resourceGroup')

# Create a Public IP for the Load Balancer
public_ip = network.PublicIPAddress(
    'publicIP',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    public_ip_allocation_method=network.IPAllocationMethod.STATIC,
)

# Create an Azure Virtual Network
vnet = network.VirtualNetwork(
    'vnet',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    address_space=network.AddressSpaceArgs(
        address_prefixes=['10.0.0.0/16'],
    ),
)

# Create a Subnet for AKS nodes
subnet = network.Subnet(
    'subnet',
    resource_group_name=resource_group.name,
    address_prefix='10.0.0.0/24',
    virtual_network_name=vnet.name,
)

# Deploy an AKS cluster
aks_cluster = containerservice.ManagedCluster(
    'aksCluster',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    agent_pool_profiles=[containerservice.ManagedClusterAgentPoolProfileArgs(
        count=3,
        max_pods=110,
        mode=containerservice.AgentPoolMode.SYSTEM,
        os_type=containerservice.OSType.LINUX,
        type=containerservice.AgentPoolType.VIRTUAL_MACHINE_SCALE_SETS,
        vm_size='Standard_DS2_v2',
        vnet_subnet_id=subnet.id,
    )],
    dns_prefix='aks-kubernetes',
)

# Create a Load Balancer
load_balancer = network.LoadBalancer(
    'loadBalancer',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    frontend_ip_configurations=[network.FrontendIPConfigurationArgs(
        name='LoadBalancerFrontEnd',
        public_ip_address=public_ip.id,
    )],
)

# Create a Backend Address Pool for the Load Balancer
backend_address_pool = network.BackendAddressPool(
    'backendAddressPool',
    resource_group_name=resource_group.name,
    load_balancer_name=load_balancer.name,
)

# Create a Load Balancing Rule for the Load Balancer
load_balancing_rule = network.LoadBalancingRule(
    'loadBalancingRule',
    resource_group_name=resource_group.name,
    load_balancer_name=load_balancer.name,
    frontend_ip_configuration=load_balancer.frontend_ip_configurations[0].name,
    backend_address_pool=backend_address_pool.id,
    protocol=network.TransportProtocol.TCP,
    frontend_port=80, # Assuming you are serving your ML model on HTTP port 80
    backend_port=80,
    enable_floating_ip=True,
)

# Export the required information
pulumi.export('resource_group_name', resource_group.name)
pulumi.export('aks_cluster_name', aks_cluster.name)
pulumi.export('public_ip_address', public_ip.ip_address)
```

This program in Python provides a blueprint for the deployment of an AKS cluster with a load balancer for machine learning model deployment. Here's a breakdown of what each part achieves:

- We first create an Azure resource group, which is a logical container for the resources we'll be deploying.
- Then we create a public IP which will be used by the load balancer to handle incoming traffic.
- A virtual network (VNet) and a subnet within that VNet for your AKS nodes to communicate securely are set up.
- We proceed to the deployment of the AKS cluster with a set number of nodes (VM scale sets) and specifications like VM size and OS type.
- A load balancer is created that includes a frontend IP configuration to connect to the public IP.
- We also create a backend address pool for the load balancer, which is where your services, once deployed in AKS, will be listed so that incoming traffic can reach them.
- A load balancing rule is defined to manage how the traffic is distributed to the services.
- Finally, we export some of the crucial details, such as the resource group name, AKS cluster name, and the public IP address, so you can use them later on, such as when setting up your domain name service (DNS) or when configuring your Kubernetes services for external access.

Make sure that `pulumi up` is run in the directory with this `__main__.py` file to deploy this infrastructure to Azure. After the infrastructure is deployed, you can continue by deploying your machine learning models as containers in the AKS cluster and configure Kubernetes services and deployments as needed.