1. Auto-scaled AI Model Inference Services with GCP Compute Engine


    To create an auto-scaled AI model inference service using Google Cloud Platform (GCP), you would typically need the following components:

    1. AI Model: The AI model that you want to serve for inference. This could be a pre-trained model or one that you have developed and trained yourself.

    2. Compute Engine Instances: The compute resources where the model will be loaded to perform inferences. These can be VMs (virtual machines) that are suitable for machine learning workloads, optionally with specialized hardware accelerators like GPUs or TPUs.

    3. Autoscaler: A mechanism to automatically scale the number of VM instances up or down based on workload. This can help in handling varying inference loads efficiently.

    4. Load Balancer: To distribute inference requests evenly across the available VM instances.

    5. Instance Template: This defines the blueprint for the VM instances that the autoscaler will create, including the disk image to use, machine type, network settings, and more.

    6. Instance Group Manager: Manages the instances created based on the template, and works with the Autoscaler to scale in or out.

    The following program demonstrates how you can use Pulumi to automate the deployment of such an infrastructure:

    import pulumi import pulumi_gcp as gcp # An example of an AI model typically does not exist as a Pulumi resource. # You would need to prepare your AI model outside of Pulumi and possibly use a custom service or a container. # Here, we're focusing on setting up the infrastructure for serving the model. # Define the instance template for Compute Engine instance_template = gcp.compute.InstanceTemplate("ai-model-instance-template", machine_type="n1-standard-1", # Choose an appropriate machine type disks=[{ "boot": True, "autoDelete": True, # You'd use an image that has your AI model and inference server pre-installed "initializeParams": { "image": "your-inference-server-image", }, }], network_interfaces=[{ "network": "default", "accessConfigs": [{}], }], ) # Create an Instance Group Manager, which uses the defined instance template instance_group_manager = gcp.compute.InstanceGroupManager("ai-model-group-manager", base_instance_name="ai-model-instance", instance_template=instance_template.id, target_size=1, # Start with one instance and let the autoscaler manage the size zone="us-central1-a", # Specify the appropriate zone ) # Define the autoscaling policy autoscaling_policy = { "max_replicas": 5, "min_replicas": 1, "cpu_utilization": { "target": 0.5, # Target half utilization before scaling up }, "cooldown_period": 60, # Cooldown period between scaling actions } # Create an Autoscaler that attaches to the Instance Group Manager autoscaler = gcp.compute.Autoscaler("ai-model-autoscaler", target=instance_group_manager.id, autoscaling_policy=autoscaling_policy, zone="us-central1-a", ) # Set up a simple HTTP health check to be used by the Load Balancer health_check = gcp.compute.HealthCheck("ai-model-health-check", http_health_check={ # Your inference server must expose an endpoint for health checks "port": 80, "request_path": "/health_check", }, ) # Set up a backend service for the Load Balancer backend_service = gcp.compute.BackendService("ai-model-backend-service", backends=[{ "group": instance_group_manager.instance_group, }], health_checks=[health_check.id], protocol="HTTP", timeout_sec=10, ) # Create a URL map to define how HTTP and HTTPS requests are directed to the backend services url_map = gcp.compute.URLMap("ai-model-url-map", default_service=backend_service.id, # Additional settings like host rules or path matchers can be defined here ) # Set up a target HTTP proxy to route requests to your URL map target_proxy = gcp.compute.TargetHttpProxy("ai-model-target-proxy", url_map=url_map.id, ) # Use a global forwarding rule to route incoming requests to the proxy forwarding_rule = gcp.compute.GlobalForwardingRule("ai-model-forwarding-rule", target=target_proxy.id, port_range="80", ) # Export the IP to which clients can send inference requests pulumi.export('inference_service_ip', forwarding_rule.ip_address)

    This program sets up a GCP Compute Engine environment with autoscaling capabilities to host an AI model inference service. Here's a breakdown of each part of the process:

    • InstanceTemplate: Encapsulates the VM configuration that will serve the AI model. The machine_type and disks properties would be configured according to your model's requirements. The disks configuration should use an image preloaded with your model and inference code.

    • InstanceGroupManager: Manages instances created from the InstanceTemplate. The target_size starts with one instance, and zone refers to the geographic location of the resources.

    • Autoscaler: Automatically adjusts the number of VMs in the InstanceGroupManager based on utilization, defined by the autoscaling_policy.

    • HealthCheck, BackendService, URLMap, TargetHttpProxy, and GlobalForwardingRule: These resources together set up a simple HTTP Load Balancer to distribute traffic across your instances.

    The pulumi.export outputs the IP address that you would use to send requests to your AI model inference service.

    Note: You'll also want to define firewall rules to allow incoming traffic, as well as other considerations like logging, monitoring, and security settings, which aren't covered in this example. Also, be sure to replace "your-inference-server-image" with the actual image that contains your AI model and inference server software.