1. High-Performance Data Science Workflows with AKS


    To set up a high-performance data science workflow, you can use Azure Kubernetes Service (AKS) which is a managed container orchestration service provided by Azure. AKS simplifies the deployment, management, and operations of Kubernetes and is a great platform for running data science workloads that often require scalable compute resources.

    In this setup, you will create an AKS cluster, which will be the foundation for running your data science workflows. Below, you will find a Pulumi program that demonstrates how to create an AKS cluster in Python using the azure-native Pulumi provider.

    Key components of this Pulumi program for AKS cluster setup:

    • ManagedCluster: This resource represents the managed Kubernetes cluster in AKS.
    • Identity: You will need an identity for the AKS cluster to interact with other Azure services.
    • AgentPoolProfile: This defines the configurations for the node pools in the cluster – you can configure things like the instance type, the number of nodes, etc.

    Here is a Pulumi program that provisions an AKS cluster suitable for high-performance data science workflows:

    import pulumi import pulumi_azure_native as azure_native from pulumi_azure_native.resources import ResourceGroup from pulumi_azure_native.containerservice import ManagedCluster, ManagedClusterIdentity, ManagedClusterAgentPoolProfile # Create an Azure Resource Group resource_group = ResourceGroup("resource_group") # Define the managed cluster identity identity = ManagedClusterIdentity( type=azure_native.containerservice.ResourceIdentityType.SYSTEM_ASSIGNED, ) # Define an agent pool profile with desirable VM size and count based on your data science workload requirements agent_pool_profile = ManagedClusterAgentPoolProfile( mode="System", name="agentpool", vm_size="Standard_DS3_v2", # This is an example size. Choose a VM size that suits your needs. count=3, # You can define the number of nodes here. os_type=azure_native.containerservice.OSType.LINUX, ) # Create an AKS cluster aks_cluster = ManagedCluster( "aksCluster", resource_group_name=resource_group.name, identity=identity, agent_pool_profiles=[agent_pool_profile], dns_prefix="aksnodes", service_principal_profile=ManagedClusterServicePrincipalProfileArgs( client_id="client-id", # replace with your service principal's client ID secret="client-secret", # replace with your service principal's secret ), kubernetes_version="1.20.7", # specify desired K8s version ) # Export the AKS cluster name and Kubernetes configuration pulumi.export('aks_cluster_name', aks_cluster.name)

    To run this program, you’ll need to have Pulumi installed and configured with your Azure account. Then, you can put this code into a file named __main__.py and run it using Pulumi CLI commands pulumi up.

    This program defines an AKS cluster with a system-assigned identity for interaction with other Azure services, a specified version of Kubernetes, and an agent pool profile where you set the VM size and node count. Based on the data science workloads you are running, choose appropriate VM sizes and node counts to handle the workload effectively. Consider using VM sizes that are optimized for compute-intensive tasks or have GPU support if your workload requires it.

    The pulumi.export lines at the end output the AKS cluster name and Kubernetes configuration, which can be used to interact with your cluster after it has been provisioned. You may also need to configure additional resources such as storage, networking, or enabling features like Azure Monitor or Network Policies based on the requirements of your data science workflows.

    Remember to replace the placeholder values for client_id and client-secret with your actual Azure Service Principal credentials. These credentials are used by AKS to interact with other Azure services on your behalf.