Low-Latency Serving of ML Models with GCP Routes

Question

Pulumi · Accepted Answer

To achieve low-latency serving of ML models with Google Cloud Platform (GCP), one can employ a few different services that GCP offers:

1. **Compute Engine** for hosting the servers where the ML models will be running.
2. **Google Cloud Load Balancer** to distribute incoming traffic across these servers efficiently.
3. **Cloud CDN** to cache content closer to the users and consequently reduce latency.
4. **Routes** to define network-wide routing rules that control the paths requests take from the user to the cloud resources.
5. **Backend Service** to configure backends for load balancing with advanced traffic management capabilities.

The following Pulumi program illustrates how you can create a GCP compute instance, deploy an ML model to that instance, and set up routing for serving that model. The routing is configured to direct traffic to the nearest instance of the compute engine running the ML model, thus ensuring low latency.

This program doesn't include the actual setup of an ML model but assumes you have a containerized ML serving system, such as TensorFlow Serving or a custom Flask app with a scikit-learn model, ready to be deployed.

```python
import pulumi
import pulumi_gcp as gcp

# Create a GCP network for the compute instances
network = gcp.compute.Network("ml-network")

# Create a GCP instance to host our ML model serving server
compute_instance = gcp.compute.Instance(
    "ml-instance",
    machine_type="n1-standard-1",
    boot_disk=gcp.compute.InstanceBootDiskArgs(
        initialize_params=gcp.compute.InstanceBootDiskInitializeParamsArgs(
            image="debian-cloud/debian-9",
        ),
    ),
    network_interfaces=[
        gcp.compute.InstanceNetworkInterfaceArgs(
            network=network.id,
            access_configs=[gcp.compute.InstanceNetworkInterfaceAccessConfigArgs()],
        ),
    ],
    # In an actual deployment, you would use a containerized version of ML model serving like TensorFlow Serving.
    # metadata_startup_script="gcloud docker -- run -d -p 8501:8501 --name=model-serving <container_image>",
)

# Create an HTTP(S) load balancer to manage incoming requests
health_check = gcp.compute.HealthCheck(
    "http-health-check",
    check_interval_sec=5,
    timeout_sec=5,
    healthy_threshold=2,
    unhealthy_threshold=2,
    http_health_check=gcp.compute.HealthCheckHttpHealthCheckArgs(
        port=80,
        request_path="/",  # In actual deployment, this path should point to your ML model serving health check endpoint
    ),
)

backend_service = gcp.compute.BackendService(
    "ml-backend-service",
    backends=[gcp.compute.BackendServiceBackendArgs(
        group=compute_instance.instance_group,
    )],
    health_checks=[health_check.self_link],
    port_name="http",
    protocol="HTTP",
)

url_map = gcp.compute.URLMap(
    "url-map",
    default_service=backend_service.self_link,
    # In an actual deployment, you might have more complex URL routing.
)

target_http_proxy = gcp.compute.TargetHttpProxy(
    "http-lb-target-proxy",
    url_map=url_map.self_link,
)

forwarding_rule = gcp.compute.GlobalForwardingRule(
    "http-content-forwarding-rule",
    target=target_http_proxy.self_link,
    port_range="80",
)

# Outputs the IP of the Load Balancer to which we can send HTTP requests
pulumi.export("lb_ip", forwarding_rule.ip_address)
```

This program starts by creating a network on the GCP, which forms the foundational communication layer for your compute resources. It then creates a compute instance and assumes you would deploy your ML serving application to it. In this case, it's using a standard Debian image for simplicity, but in a real scenario, you would replace this with your ML container example.

We're creating a health check that the load balancer will use to ensure the ML service is running smoothly. If the service goes down, or becomes unhealthy, the load balancer will stop directing traffic to the failing instance.

With the backend service, we tell GCP how to manage our compute resources, defining things like port names and the backend group, which consists of the compute instance we created earlier.

The URL map object is effectively what tells the load balancer how to route incoming requests. In this simple case, we just have a default service which all traffic is directed to, but in a more complex application, you might define specific routes depending on the request URL.

The target HTTP proxy is the component that checks the URL map to decide how to route requests, and the forwarding rule connects that proxy to the public internet on port 80, so that HTTP requests can reach it.

Finally, we export the IP address of the load balancer. This IP address is what you would give to clients to access your ML model's serving endpoint.

Remember, this is a high-level overview and each application's requirements will adjust the specifics of the implementation. Additionally, to serve ML models with very low latency, consider geographic distribution of your compute instances and use of [GCP's Premium Tier network service](https://cloud.google.com/network-tiers).