1. API Gateway for LLM Inference Endpoint Hosting


    To host a machine learning model inference endpoint, you can use various cloud providers. In this guide, let's use Amazon Web Services (AWS) and Pulumi to build an API Gateway that can securely expose your machine learning model for inference.

    AWS API Gateway is a fully managed service that makes it easy for developers to publish, maintain, monitor, and secure APIs at any scale. It acts as the front door for applications to access data, business logic, or functionality from your backend services.

    Setting up an API Gateway with AWS and Pulumi

    Here's what we'll do:

    1. Create an AWS Lambda function that will serve as our inference endpoint.
    2. Define the API Gateway REST API.
    3. Set up an Integration to connect the REST API method with the Lambda function.
    4. Define the method for the REST API, such as GET or POST.
    5. Deploy the API stage for the API Gateway.
    6. Export the Invoke URL to access the API.

    Below is the Pulumi program written in Python to set up an AWS API Gateway for a Lambda function, which you would replace with your LLM inference Lambda function.

    import pulumi import pulumi_aws as aws # Assume you have the AWS Lambda function for inference: # Replace 'YOUR_LAMBDA_FUNCTION_NAME' with your actual Lambda function name. llm_lambda = aws.lambda_.Function.get("llm_lambda_function", "YOUR_LAMBDA_FUNCTION_NAME") # Create an API Gateway REST API. api = aws.apigateway.RestApi("llmApi", description="API for LLM Inference Endpoint", ) # Set up an AWS API Gateway Resource (e.g., the endpoint URL). resource = aws.apigateway.Resource("llmResource", parent_id=api.root_resource_id, # Use the root path. path_part="infer", # This is the URL path part for the inference. For example: https://api.example.com/infer rest_api=api.id, ) # Set up the integration between the API Gateway and AWS Lambda function. integration = aws.apigateway.Integration("llmIntegration", rest_api=api.id, resource_id=resource.id, http_method="POST", # Assuming POST requests for inference. integration_http_method="POST", type="AWS_PROXY", # Use AWS_PROXY integration to ensure the input format is compatible with AWS Lambda. uri=llm_lambda.invoke_arn, ) # Define a method for the API Resource that is integrated with the Lambda function. method = aws.apigateway.Method("llmMethod", rest_api=api.id, resource_id=resource.id, http_method="POST", # Assuming POST requests for inference. authorization="NONE", # Using no authorization for simplicity; consider using an authorizer for production. integration=integration.id, ) # Deploy the API stage. stage = aws.apigateway.Stage("llmStage", rest_api=api.id, deployment=aws.apigateway.Deployment("llmDeployment", rest_api=api.id, # Deployment is triggered by referencing the 'method' resource. triggers={ "redeployment": method.deployment_str, }, ).id, stage_name="prod", # Deploy as the 'prod' stage. ) # Export the Invoke URL of the API Gateway. pulumi.export("invoke_url", pulumi.Output.concat("https://", api.id, ".execute-api.", aws.config.region, ".amazonaws.com/prod/infer"))

    In this program:

    • We define a lambda function called llm_lambda by retrieving it using its name.
    • We create an API Gateway REST API named llmApi, which acts as the entry point for our inference endpoint.
    • We set up a resource under the REST API with the path part infer, which specifies the local path in the URL request where the endpoint is accessible.
    • We configure an integration of type AWS_PROXY that connects the REST API to the Lambda function.
    • We define a POST method for making inference requests to our Lambda function.
    • We deploy our API to a stage called prod, which is equivalent to deploying it to production.
    • Finally, we export the invocation URL for your use in making inference requests.

    You would substitute "YOUR_LAMBDA_FUNCTION_NAME" with the name of the Lambda function that contains your machine learning inference code. This can either be an existing Lambda function or one you create with Pulumi's Function resource if you're deploying a new one.

    In a production scenario, you should consider adding an authorizer to Method to secure your API endpoint. This could include mechanisms for API key validation, IAM role checks, or using AWS Cognito for user authentication. For simplicity, the example above uses "NONE" for the authorization attribute.

    Remember, before running the Pulumi program, you need to set up your AWS credentials and install the required Pulumi AWS package.