Large Language Models Inference with Azure Kubernetes Service (AKS)

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Pulumi · Accepted Answer

To set up an Azure Kubernetes Service (AKS) cluster capable of running Large Language Models for Inference, you'll need to go through several high-level steps using Pulumi. The process involves creating an AKS cluster, configuring node pools with sufficient resources, and setting up the appropriate networking and security for your application. Here is a general guide on how to do this with Pulumi in Python:

1. **Provision an AKS Cluster**: You'll need to set up an AKS cluster, which will be the environment where your applications will run. The `ManagedCluster` resource from the `azure-native` package will be used for this.

2. **Configure Node Pools**: Depending on the requirements of your Large Language Model, you might need to set up multiple node pools with specific VM sizes that can accommodate the computational demands of these models.

3. **Set Up Networking**: Proper networking configuration, including load balancers, network policies, and possibly an ingress controller to manage access to the services.

4. **Establish Security**: Implement security best practices, like setting up Kubernetes RBAC (Role-Based Access Control), using Azure's Active Directory integration, network security groups, and possibly a Container Registry to manage your images securely.

Here is a basic Pulumi program written in Python that outlines creating an AKS cluster with a default node pool. You should adapt the configuration to your specific needs, especially the `vm_size` to suit your model inference requirements, as well as including additional node pools if necessary.

```python
import pulumi
import pulumi_azure_native.containerservice as containerservice
import pulumi_azure_native.resources as resources

# Replace these variables with appropriate values
resource_group_name = 'my-aks-resource-group'
aks_cluster_name = 'my-aks-cluster'
location = 'WestUS'

# Create an Azure Resource Group to hold the AKS Cluster
resource_group = resources.ResourceGroup('resource_group', 
    resource_group_name=resource_group_name,
    location=location)

# Create an AKS Cluster
aks_cluster = containerservice.ManagedCluster('aksCluster',
    resource_group_name=resource_group.name,
    addon_profiles={
        "KubeDashboard": {
            "enabled": True,
        },
    },
    dns_prefix='akskube',
    location=resource_group.location,
    agent_pool_profiles=[{
        "count": 3,
        "max_pods": 110,
        "mode": "System",
        "name": "agentpool",
        "node_labels": {},
        "os_disk_size_gb": 30,
        "os_type": "Linux",
        "vm_size": "Standard_DS2_v2",  # Choose a VM size that suits your computational requirements
    }],
    linux_profile={
        "adminUsername": "adminuser",
        "ssh": {
            "publicKeys": [{
                "keyData": "ssh-rsa ...",  # Replace with your SSH public key
            }],
        },
    },
    node_resource_group=f'node-resource-group-{aks_cluster_name}',
    identity={
        "type": "SystemAssigned",
    }
)

# Export the cluster's kubeconfig
pulumi.export('kubeconfig', aks_cluster.kube_config_raw)
```

This program sets up the following:

- An Azure Resource Group that acts as a logical container for the AKS cluster.
- The AKS cluster itself, named `my-aks-cluster`, with a default node pool.
- The node pool is configured to use VMs of size `Standard_DS2_v2`, but you should select a VM size that matches your inference workloads.
- SSH access to the nodes is set using a public SSH key that you must provide.
- A `kubeconfig` is exported, which you'll need to interact with your Kubernetes cluster using `kubectl` or other Kubernetes tools.

Before running this program, ensure you have the Azure CLI installed and configured.

Additionally, you will likely want to deploy the Large Language Model and inference service as a Kubernetes application, potentially using Helm charts for deployment, and custom Docker images that you'll store in an Azure Container Registry (ACR).

Keep in mind that actual Large Language Model inference may require more specific configuration for things like:
- GPU-enabled nodes if using GPU-based inference
- Horizontal Pod Autoscalers for scaling the deployment
- Network and security configurations for safe and secure access
- Persistent storage configurations for models and data handling

The above program is a solid starting point, and from here, you can add more specific configurations tailored to your actual application's needs.