End-to-End Encryption for LLMs Serving with GCP HTTPS Proxy

Question

Pulumi · Accepted Answer

To set up end-to-end encryption for Language Model (LLM) services in Google Cloud Platform (GCP) using an HTTPS proxy, we will be creating a configuration that encrypts the traffic from clients to the LLM service.

The primary components for this configuration will include:

1. An HTTPS proxy to handle incoming HTTPS requests. This requires creating an SSL certificate to secure the connection.
2. A URL map to direct the incoming requests to the appropriate backend service or backend bucket.
3. A backend service where the actual LLMs are running. The communication between the HTTPS proxy and the backend service also needs to be encrypted.

In this example, we will use the Pulumi GCP provider to create the following resources:

- `gcp.compute.TargetHttpsProxy`: This will be the entry point for incoming HTTPS encrypted traffic. We'll associate it with the URL map and the SSL certificates we create.
- `gcp.compute.UrlMap`: This will define the rules that the proxy uses to route incoming requests to the backend services.
- `gcp.compute.BackendService`: This is the backend service where our LLMs will be running. It should be configured to use an instance group or other backend that serves the LLMs.
- `gcp.compute.SslCertificate`: This resource represents an SSL certificate that the HTTPS proxy will use to establish secure connections.

In the example below, I'll showcase how to create a basic configuration. The actual backend serving the LLMs would need to be defined in detail based on your specific implementation, which could either be an instance group, a custom service running on GKE, Cloud Run, or any other compute resource that can serve HTTP requests.

```python
import pulumi
import pulumi_gcp as gcp

# Replace these variables with appropriate values
project = 'my-project'
region = 'us-central1'
ssl_cert_name = 'my-ssl-cert'
target_proxy_name = 'my-target-https-proxy'
url_map_name = 'my-url-map'
backend_service_name = 'my-backend-service'
llm_instance_group_name = 'my-llm-instance-group'

# Create an SSL Certificate
ssl_certificate = gcp.compute.SslCertificate(ssl_cert_name,
    project=project,
    managed=gcp.compute.SslCertificateManagedArgs(
        domains=["llm.example.com"]  # Replace with your domain
    )
)

# Create a URL map
url_map = gcp.compute.UrlMap(url_map_name,
    project=project,
    default_service=backend_service.id
)

# Create a Backend Service
backend_service = gcp.compute.BackendService(backend_service_name,
    project=project,
    backends=[
        gcp.compute.BackendServiceBackendArgs(
            group=pulumi.Output.concat("https://www.googleapis.com/compute/v1/projects/", project, "/zones/", region, "-a/instanceGroups/", llm_instance_group_name)
        )
    ],
    protocol="HTTPS",
    health_checks=[health_check.id]
    # You would typically add health_checks here, but they are omitted for brevity.
)

# Create a Target HTTPS Proxy
target_https_proxy = gcp.compute.TargetHttpsProxy(target_proxy_name,
    project=project,
    url_map=url_map.id,
    ssl_certificates=[ssl_certificate.id]
)

# Export the URL of the LLM service
pulumi.export('llm_service_url', pulumi.Output.concat("https://", ssl_cert_name, "."))
```

In this program:

- We start by declaring the project and region variables to be used across all resources. 
- An `SslCertificate` resource represents the SSL Certificate that will be used by the HTTPS Proxy.
- An `UrlMap` resource defines the rules that the proxy will follow to route incoming requests to the backend service.
- The `BackendService` resource refers to the backend feature where your LLM is running.
- Finally, we create a `TargetHttpsProxy` that uses the previously created SSL Certificate and URL map to handle incoming HTTPS requests.

Note that I've included placeholders for the health checks in the `BackendService` resource, as you would need to specify the health check for the backend service so Google Cloud can know how to confirm that the LLM service is up and running.

This script is a skeleton that points out how you might structure your infrastructure for end-to-end encryption with Google Cloud HTTPS Load Balancers and Pulumi. You would need to fill in details pertaining to your specific LLM setup, especially how the traffic should be routed once it reaches the GCP infrastructure, and the specifics of the backend service, such as instance group definitions and health checks.