1. Real-time AI Inference with Stateful Services on Service Fabric


    To set up a real-time AI inference with stateful services on Azure Service Fabric, one would typically establish the following:

    1. Service Fabric Cluster: A managed Service Fabric cluster to host the microservices that constitute the stateful services.
    2. Stateful Services: These are microservices that maintain a state that should persist across sessions and reboots.
    3. AI Inference Service: A service that can receive data, perform the inference using a pre-trained AI model, and return the results in real-time.
    4. Networking Setup: This includes the setup for ingress and egress traffic for the services, along with necessary security considerations.

    Pulumi enables you to define, deploy, and manage Azure Service Fabric clusters and services using infrastructure as code. Below, you'll find a detailed explanation and a corresponding Pulumi program that sets up a Service Fabric cluster, deploys a stateful service, and considers the aspects of networking and security you'd need for real-time AI inference.

    Service Fabric Cluster

    The cluster is the primary building block of Service Fabric, where you will deploy the microservices. We'll create a Service Fabric cluster with a defined node type. The node type specifies the size of the VM, the number of VMs, and their properties.

    Stateful Services

    Stateful services can store and manage state directly within the service. This is typically accomplished using the reliable collections API in Service Fabric, which provides high availability to state without the need for an external database.

    AI Inference Service

    The AI inference service will likely be built using Azure Machine Learning or a similar service that can integrate with Service Fabric. This isn't directly created with Pulumi, but you would deploy the model and inference code as a microservice within the Service Fabric cluster.

    Let's begin with a Pulumi Python program to define such infrastructure.

    import pulumi import pulumi_azure_native as azure_native # Create a resource group for the Service Fabric cluster resource_group = azure_native.resources.ResourceGroup("resource_group") # Create a Service Fabric cluster service_fabric_cluster = azure_native.servicefabric.Cluster("service_fabric_cluster", resource_group_name=resource_group.name, location=resource_group.location, management_endpoint="http://my-cluster.eastus.cloudapp.azure.com:19080", # Replace with actual endpoint node_types=[{ "name": "NodeType0", "isPrimary": True, "clientConnectionEndpointPort": 19000, "httpGatewayEndpointPort": 19080, "durabilityLevel": "Silver", # Choose a durability level "applicationPorts": { "endPort": 30000, "startPort": 20000 }, "ephemeralPorts": { "endPort": 65534, "startPort": 49152 }, "vmInstanceCount": 5, # The number of nodes in the node type # The other properties like VM size, OS type, etc., need to be defined here. }], # Define Azure Active Directory for client authentication, Fabric settings, Add-on features, etc. # Add appropriate configurations ) # Define a Service Fabric application application = azure_native.servicefabric.Application("application", resource_group_name=resource_group.name, location=resource_group.location, services=[{ # Define the stateful service here, including partition and replica settings }], # Define application types, services, and other settings ) # Export the endpoint of the Service Fabric cluster pulumi.export("service_fabric_endpoint", service_fabric_cluster.management_endpoint)

    This program defines the infrastructure for a primary node type within a Service Fabric cluster that is ready to host stateful services. It specifies the number of VM instances, application ports, and other crucial configurations. You would adapt the services list within the azure_native.servicefabric.Application resource definition to deploy your stateful services and AI inference services.

    To deploy your real-time AI inference as part of a stateful service in Service Fabric, you would:

    • Deploy AI models and inference code as microservices in this infrastructure.
    • Configure the AI inference service with the necessary environment to run in Service Fabric, either as containers or guest executables.
    • Implement the service logic to handle state using Service Fabric’s APIs.

    Lastly, to connect your Service Fabric infrastructure with Azure's AI and machine learning services, you would typically use the Azure SDKs within your application code. The setup and management of the real-time AI aspects would depend on the nature of the AI model and the framework (like TensorFlow, PyTorch, etc.) used to create it.