Cross-Origin Resource Sharing (CORS) for ML Model APIs

Question

Pulumi · Accepted Answer

Cross-Origin Resource Sharing (CORS) is a security feature that allows you to define how resources on a web server can be accessed from a different domain. This is particularly important when you are hosting APIs, including machine learning model APIs that may need to be accessed by client-side web applications running on different domains.

When setting up CORS for an ML Model API, we would typically configure the server hosting the API to include the proper CORS headers in its responses. These headers inform browsers about the allowed origins, methods, and headers for cross-origin requests.

For instance, if you're using Amazon API Gateway to serve your ML Model APIs, you can set up CORS by specifying allowed origins, methods, headers, and other CORS-related settings. Pulumi provides a way to define these settings in infrastructure as code, allowing you to version them, reuse them, and keep them consistent across environments.

In the following Pulumi Python example, we'll see how to configure CORS for an Amazon API Gateway, assuming you are serving your ML Model via this service. Remember, the exact specifics of your CORS configuration might differ based on your actual use case.

```python
import pulumi
import pulumi_aws as aws

# Configuration for the CORS rule
cors_configuration = {
    "allowMethods": ["POST", "OPTIONS"], # Typically, ML model APIs may only need POST for inference and OPTIONS for pre-flight requests.
    "allowHeaders": ["Content-Type", "X-Amz-Date", "Authorization", "X-Api-Key"],
    "allowOrigins": ["https://example.com"],  # The origin of the client application
    "exposeHeaders": ["X-Amz-Target"], 
    "maxAge": 3000  # Max age for the browser to cache the response to pre-flight requests.
}

# Create an API Gateway Rest API to serve the ML model
rest_api = aws.apigateway.RestApi("my-ml-model-api",
    description="API for ML Model")

# Create a resource representing the API endpoint
resource = aws.apigateway.Resource("my-resource",
    rest_api_id=rest_api.id,
    path_part="inference")  # The path part for the ML model's inference endpoint

# Create a method for the POST request
post_method = aws.apigateway.Method("my-post-method",
    rest_api_id=rest_api.id,
    resource_id=resource.id,
    http_method="POST",
    authorization="NONE")

# Create a method for OPTIONS request to handle CORS pre-flight request
options_method = aws.apigateway.Method("my-options-method",
    rest_api_id=rest_api.id,
    resource_id=resource.id,
    http_method="OPTIONS",
    authorization="NONE")

# Define the Mock integration for OPTIONS method to support CORS
mock_integration = aws.apigateway.Integration("my-mock-integration",
    rest_api_id=rest_api.id,
    resource_id=resource.id,
    http_method=options_method.http_method,
    type="MOCK",
    passthrough_behavior="WHEN_NO_MATCH",
    request_templates={"application/json": "{\"statusCode\": 200}"})

# Use a MethodResponse and IntegrationResponse to set the necessary CORS headers
method_response = aws.apigateway.MethodResponse("my-method-response",
    rest_api_id=rest_api.id,
    resource_id=resource.id,
    http_method=options_method.http_method,
    status_code="200",
    response_models={"application/json": "Empty"})

integration_response = aws.apigateway.IntegrationResponse("my-integration-response",
    rest_api_id=rest_api.id,
    resource_id=resource.id,
    http_method=options_method.http_method,
    status_code=method_response.status_code,
    response_parameters={
        "method.response.header.Access-Control-Allow-Headers": "'" + ','.join(cors_configuration['allowHeaders']) + "'",
        "method.response.header.Access-Control-Allow-Methods": "'" + ','.join(cors_configuration['allowMethods']) + "'",
        "method.response.header.Access-Control-Allow-Origin": "'" + ','.join(cors_configuration['allowOrigins']) + "'",
        "method.response.header.Access-Control-Expose-Headers": "'" + ','.join(cors_configuration['exposeHeaders']) + "'",
        "method.response.header.Access-Control-Max-Age": str(cors_configuration['maxAge']),
    },
    response_templates={"application/json": ""}
)

# Export the API endpoint URL
pulumi.export("api_url", rest_api.execution_arn.apply(lambda arn: f'https://{arn}.execute-api.{aws.get_region().name}.amazonaws.com/prod'))
```

In the example above, we're defining the CORS settings inside the `cors_configuration` dictionary and then applying these when creating `MethodResponse` and `IntegrationResponse` resources for our OPTIONS method. This way, any pre-flight requests made by browsers to check CORS policies will get the correct headers in response.

We have set up two methods, one for the `POST` request we expect to receive when a client application uses our ML API, and an `OPTIONS` method to handle any pre-flight CORS checks that a browser might carry out. The `OPTIONS` method is configured to use a mock integration, which is a way to send back a predetermined response (in this case, the necessary CORS headers) without invoking any backend.

Finally, we export the API URL using the execution ARN for the REST API. The ARN is a unique identifier for AWS resources, and we use it to construct the endpoint URL. This URL is what you would use in your client application to interact with your ML model API.

This program sets up the necessary infrastructure to support CORS for an ML Model API using Amazon API Gateway with Pulumi.