Scaling Model Inference with GCP Endpoints

Question

Pulumi · Accepted Answer

Scaling model inference with Google Cloud Endpoints involves creating and deploying a machine learning model on Google Cloud AI Platform and then exposing the model through a secure, scalable API using Google Cloud Endpoints. Here's how you can achieve this using Pulumi in Python:

1. **Deploy the Machine Learning Model:**
   - Deploy a pre-trained model to AI Platform Predictions.
   - This step usually happens outside of Pulumi, using tools like `gcloud` CLI or Google Cloud Console.

2. **Create the Google Cloud Endpoints Service:**
   - This service acts as the front door for your machine learning model, handling incoming API requests and routing them to the model hosted on AI Platform.
   - Define the service configuration in a YAML file that specifies the API surface.

3. **Deploy the Endpoints Service:**
   - Use Pulumi to create a `Service` resource which deploys the configuration to Cloud Endpoints.

4. **Protect and Scale the Endpoint:**
   - Define IAM policies to protect your API with authentication and access control.
   - You can apply scaling options and allocate more resources as needed.

The Pulumi code for these steps includes creating a `gcp.endpoints.Service` resource for deploying the Cloud Endpoints service configuration. In addition, we may use `ServiceIamPolicy`, `ServiceIamBinding`, and `ServiceIamMember` resources for setting up the necessary IAM policies.

Below is a skeleton Pulumi program in Python that assumes you have already deployed your machine learning model to AI Platform and have a service configuration YAML file ready. The example demonstrates how to deploy a Google Cloud Endpoints service and set up IAM policies. You'd need to replace `<YOUR-MODEL-DETAILS-HERE>` with actual details of your AI Platform model and service.

```python
import pulumi
import pulumi_gcp as gcp

# Deploy the Endpoints Service.
# The `openapi_config` parameter should link to a YAML file with your service's definition
# that includes the machine learning API setup.
endpoints_service = gcp.endpoints.Service("my-endpoints-service",
    service_name="my-model-endpoints.example.com",
    openapi_config=pulumi.FileAsset("openapi.yaml") # Replace with path to your OpenAPI configuration file
)

# Define and apply an IAM policy to the Endpoints Service to control access.
# Note that member should be the appropriate identity and role should be the access level
# granted to that identity.
service_iam_policy = gcp.endpoints.ServiceIamPolicy("my-endpoints-service-iam-policy",
    service_name=endpoints_service.service_name,
    policy_data="""
    {
        "bindings": [{
            "role": "roles/endpoints.viewer",
            "members": ["user:myemail@example.com"]
        }]
    }
    """
)

# Export the service configuration id and the service's HTTP address so it can be accessed.
pulumi.export('service_config_id', endpoints_service.service_config_id)
pulumi.export('service_http_address', endpoints_service.service_name)
```

In this example:
- We deployed an Endpoints Service that would act as a proxy to the AI Platform model.
- We defined a basic IAM policy for the service to ensure it's securely accessible. In a real-world scenario, you would need to adjust the IAM policy members and roles to fit your access control requirements.

To complete the deployment, you would need the Cloud Endpoints service configuration defined in the `openapi.yaml` file. This configuration file outlines the API's surface, including resource paths, HTTP methods, request, and response definitions.

The policy data in the `ServiceIamPolicy` resource is a JSON string that specifies the access control bindings for the service. Replace `"roles/endpoints.viewer"` with the appropriate IAM role, and `"user:myemail@example.com"` with the IAM identity that should have access to the service.

Please make sure to replace placeholder entries with actual configuration that aligns with your existing AI Platform model and Cloud Endpoints service setup.