1. Autoscaling Backend Services for AI Model Serving.


    Setting up autoscaling backend services for AI model serving involves creating a scalable infrastructure that automatically adjusts the number of instances or resources according to the workload demand. This can be critical for AI applications, which may have highly variable computational requirements.

    For our example, I'll focus on setting up such a system on Google Cloud Platform (GCP), using Google Compute Engine (GCE). We'll create a managed instance group with an autoscaler that defines how the group should scale. Additionally, we'll set up a load balancer to distribute traffic across the instances in the group.

    The chosen resources from Pulumi's GCP offerings are:

    1. Compute Engine Instance Template (google-native.compute/v1.InstanceTemplate): This defines the blueprint for virtual machine instances within the managed group, including the machine type, disk setup, and network interfaces.
    2. Compute Engine Managed Instance Group (google-native.compute/v1.RegionInstanceGroupManager): This group uses the instance template to create a pool of instances across the chosen region that can be autoscaled.
    3. Compute Engine Autoscaler (google-native.compute/v1.RegionAutoscaler): This automatically adjusts the number of instances in the instance group based on the defined policies (CPU utilization, load balancer capacity, or custom metrics).
    4. Compute Engine Load Balancer (Backend Service and URL Map): The backend service (google-native.compute/v1.BackendService) is responsible for managing how the load balancer interacts with the instance group, whereas the URL map (google-native.compute/v1.UrlMap) defines how requests are routed to various backend services.

    Now let's create a Pulumi program in Python that sets up the autoscaling backend services. I'll provide detailed explanations throughout the code as comments.

    import pulumi from pulumi_google_native.compute import v1 as compute_v1 # Initialize GCP project and region configuration. project = 'your-gcp-project' region = 'us-central1' # Define a Compute Engine Instance Template. instance_template = compute_v1.InstanceTemplate("ai-model-server-template", project=project, properties={ "machineType": "n1-standard-1", # Standard instance type. "disks": [{ "boot": True, "initializeParams": { "sourceImage": "projects/debian-cloud/global/images/family/debian-10" # The boot image. } }], "networkInterfaces": [{ "network": "global/networks/default" # The network configuration. }] }) # Define a Managed Instance Group with the Instance Template. managed_instance_group = compute_v1.RegionInstanceGroupManager("ai-model-server-group", project=project, region=region, baseInstanceName="ai-model-server", # Base name for instances in the group. instanceTemplate=instance_template.self_link, # Reference to the instance template. targetSize=1 # Start with 1 instance. ) # Define an Autoscaler to automatically scale the Managed Instance Group. autoscaler = compute_v1.RegionAutoscaler("ai-model-server-autoscaler", project=project, region=region, target=managed_instance_group.self_link, # Reference to the managed instance group. autoscalingPolicy={ "cpuUtilization": {"utilizationTarget": 0.6}, # Scale based on CPU utilization. "maxNumReplicas": 5, # Maximum number of instances. "minNumReplicas": 1, # Minimum number of instances. }) # Define a Backend Service to manage load balancing for the Managed Instance Group. backend_service = compute_v1.BackendService("ai-model-server-backend", project=project, backends=[{ "group": managed_instance_group.instanceGroup, # Reference to the instance group. }], loadBalancingScheme="EXTERNAL", # Use external load balancing. portName="http", # Port name used for the service. protocol="HTTP", # Use HTTP protocol. healthChecks=["projects/{}/global/healthChecks/healthy-check-1".format(project)] # Reference to health check. ) # Define a URL Map to route incoming requests. url_map = compute_v1.UrlMap("ai-model-service-url-map", project=project, defaultService=backend_service.self_link # Reference to the backend service. ) # Export the URLs for the Backend Service to access the autoscaling group. pulumi.export("instanceGroupUrl", managed_instance_group.self_link) pulumi.export("backendServiceUrl", backend_service.self_link)

    Make sure you replace 'your-gcp-project' with your actual GCP project ID. This program is ready to run and will set up all necessary resources for serving an AI model on an autoscaling backend service. Just run pulumi up in your command line after setting up Pulumi with GCP.

    What happens when this code runs:

    • Pulumi sends a request to GCP to set up the resources as defined.
    • GCP will create an instance template that will be used as a blueprint for all instances in your managed group.
    • A managed instance group gets created with one instance initially, using the prescribed instance template.
    • An autoscaler is created and attached to the managed instance group, scaling based on CPU utilization.
    • The backend service and URL map are set up for the load balancer, ensuring that incoming traffic is distributed across the instances efficiently and can handle increased load by scaling the backend instances.

    This way, your AI model serving infrastructure will be able to scale automatically and handle varying loads without manual intervention, ensuring high availability and cost-efficiency.