Medical Imaging Processing with Azure Kubernetes Service

Question

Pulumi · Accepted Answer

To set up an infrastructure for medical imaging processing using Azure Kubernetes Service (AKS), you'd need to provision an AKS cluster, storage to handle the medical images, and potentially a managed identity for secure interactions with other Azure services. AKS simplifies the deployment and management of Kubernetes on Azure, providing a managed Kubernetes cluster that can automatically scale based on processing needs, which is critical in processing medical imaging data efficiently.

Here's a step-by-step guide, followed by a Pulumi program in Python:

1. **Managed Kubernetes Cluster**: We'll use AKS to create a Kubernetes cluster. This will be the orchestration layer that manages your containerized applications, such as medical imaging processing.
  
2. **Agent Pool Profiles**: Define the agent pools for different types of tasks, such as CPU-intensive image processing.

3. **Storage Account and Share**: Set up an Azure Storage account and Azure Files share to store processed images.

4. **Azure Container Registry**: Optional, if you need a private container registry to store your Docker container images.

5. **Managed Identity**: Set up an Azure managed identity for secure access to other Azure services without managing credentials.

Now, I'll write a Pulumi program to provision these resources on Azure. Note that you'll need the Azure Native Pulumi provider to interact with Azure resources.

```python
import pulumi
import pulumi_azure_native as azure_native

# Define the resource group where all our resources will live
resource_group = azure_native.resources.ResourceGroup('medical-imaging-rg')

# Provision an Azure Kubernetes Service cluster
aks_cluster = azure_native.containerservice.ManagedCluster(
    "aksCluster",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    identity=azure_native.containerservice.ManagedClusterIdentityArgs(type="SystemAssigned"),
    agent_pool_profiles=[azure_native.containerservice.ManagedClusterAgentPoolProfileArgs(
        name="agentpool",
        count=3,
        vm_size="Standard_DS2_v2",
        os_type=azure_native.containerservice.OSType.LINUX,
        mode=azure_native.containerservice.AgentPoolMode.SYSTEM,
    )],
    dns_prefix="medical-imaging-aks",
)

# Create an Azure Storage Account for medical imaging data
storage_account = azure_native.storage.StorageAccount(
    "medimagesstorage",
    resource_group_name=resource_group.name,
    kind=azure_native.storage.Kind.STORAGE_V2,
    sku=azure_native.storage.SkuArgs(name=azure_native.storage.SkuName.STANDARD_LRS),
)

# Create a file share to store medical images
file_share = azure_native.storage.FileShare(
    "medimagesfileshare",
    resource_group_name=resource_group.name,
    account_name=storage_account.name,
    share_name="processedimages",
)

# Outputs
pulumi.export("kubeconfig", aks_cluster.kube_config_raw)
pulumi.export("storage_account_name", storage_account.name)
pulumi.export("file_share_name", file_share.name)
```

This program sets up a resource group, an AKS cluster with a single agent pool, a storage account, and a file share within that account.

- The AKS cluster is configured with a system-assigned managed identity, making it easier to give the cluster access to other Azure resources without managing service principal credentials manually.
- The agent pool is where the Kubernetes nodes will live. In this example, we've set up a pool with 3 nodes of size Standard_DS2_v2, which should be sufficient for small to medium medical imaging workloads. You may need to adjust this based on your specific needs.
- We create a storage account with the SKU set to `STANDARD_LRS` (Locally Redundant Storage) for cost efficiency, and a file share inside it named `processedimages`. This is where your processed medical images could be stored.

To get started with this Pulumi program:

1. Install Pulumi and the Azure CLI, and log in to both.
2. Create a new Python Pulumi project and replace the contents of `__main__.py` with the provided code.
3. Run `pulumi up` to preview and deploy the changes.

The Kubernetes cluster's kubeconfig is exported as an output, which you can use to interact with your cluster using `kubectl`. The names of the storage account and file share are also exported, which can be useful if you're referencing them in other scripts or infrastructure outside of Pulumi.

Remember to configure your workload deployments and any necessary Kubernetes resources (like namespaces, deployments, services, etc.) to get your image processing application up and running on your AKS cluster, which is beyond the scope of this infrastructure provisioning.