Serverless API Endpoints for AI Model Serving

Question

Pulumi · Accepted Answer

Serverless API endpoints are widely used in cloud computing to host AI models, allowing them to serve predictions without the overhead of managing the underlying infrastructure. This enables you to focus on the AI model itself and the application logic, without worrying about servers, scaling, or server maintenance.

In this guide, we will create a serverless API endpoint for AI model serving using Pulumi. We will deploy a Lambda function that serves an AI model, and we will expose this Lambda through an API Gateway, allowing it to accept HTTP requests and return predictions.

Here's a step-by-step program in Python that shows how to deploy a serverless API endpoint using AWS services with Pulumi:

1. **AWS Lambda Function**: This is the serverless compute service we'll use to run our AI model serving code without provisioning or managing servers. We'll package our AI model and inference code as a Lambda function.

2. **Amazon API Gateway**: This service will be used to create a RESTful API that acts as a front door for applications to access data, business logic, or functionality from the Lambda function.

3. **IAM Role and Policy**: We will create an IAM role and policy to grant the necessary permissions to the Lambda function for logging and execution.

4. **Pulumi Outputs**: Finally, we will export the URL of the API Gateway endpoint so that you can access the AI model serverless endpoint.

Let's write the Pulumi program to accomplish this:

```python
import pulumi
import pulumi_aws as aws

# Define the IAM role that the Lambda function will assume
lambda_role = aws.iam.Role("lambdaRole", assume_role_policy="""{
     "Version": "2012-10-17",
     "Statement": [{
         "Action": "sts:AssumeRole",
         "Effect": "Allow",
         "Principal": {
             "Service": "lambda.amazonaws.com"
         }
     }]
 }""")

# Attach the AWSLambdaBasicExecutionRole policy to the role
lambda_policy_attachment = aws.iam.RolePolicyAttachment("lambdaPolicyAttachment",
    role=lambda_role.id,
    policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole")

# Define the Lambda function, assuming you have a zipped file of your function's code and dependencies
# Make sure to replace 'path_to_your_lambda_function_zip' with your actual Lambda function's code.
ai_model_lambda = aws.lambda_.Function("aiModelLambda",
    role=lambda_role.arn,
    handler="lambda_function.lambda_handler",
    runtime="python3.8",
    code=pulumi.FileArchive("path_to_your_lambda_function_zip"),
    timeout=30)

# Define the API Gateway to expose the Lambda function
api_gateway = aws.apigatewayv2.Api("apiGateway",
    protocol_type="HTTP")

# Define the integration between the API Gateway and the Lambda function
integration = aws.apigatewayv2.Integration("lambdaIntegration",
    api_id=api_gateway.id,
    integration_type="AWS_PROXY",
    integration_uri=ai_model_lambda.invoke_arn,
    payload_format_version="2.0")

# Define the route for the API Gateway
route = aws.apigatewayv2.Route("apiRoute",
    api_id=api_gateway.id,
    route_key="POST /predict",
    target=pulumi.Output.concat("integrations/", integration.id))

# Define the Lambda permission to allow the API Gateway to invoke the Lambda function
lambda_permission = aws.lambda_.Permission("lambdaPermission",
    action="lambda:InvokeFunction",
    function=ai_model_lambda.name,
    principal="apigateway.amazonaws.com",
    source_arn=pulumi.Output.concat(api_gateway.execution_arn, "/*"))

# Deploy the API Gateway
deployment = aws.apigatewayv2.Deployment("apiDeployment",
    api_id=api_gateway.id,
    # This is a dummy value to ensure a new deployment on update. A timestamp or hash mechanism is recommended.
    triggers={"redeployment": str(pulumi.output(pulumi.get_stack()))})

# Configure the API Gateway's stage
stage = aws.apigatewayv2.Stage("apiStage",
    api_id=api_gateway.id,
    deployment_id=deployment.id,
    auto_deploy=True)

# Export the invoke URL of the API Gateway
pulumi.export("invoke_url", api_gateway.api_endpoint.apply(lambda endpoint: f"{endpoint}/predict"))
```

In this example, `path_to_your_lambda_function_zip` should be replaced with the actual path to the ZIP file containing your Lambda function code and any dependencies it has. Your Lambda's handler function should be named `lambda_handler` in a file named `lambda_function.py`.

Please ensure the AWS credentials are configured on the machine that runs Pulumi commands and that the AWS user/role has sufficient permissions to create these resources.

Once you run `pulumi up`, this code will create the necessary AWS resources, deploy your Lambda AI model, and provide you with an HTTP endpoint that you can POST your inference requests to. You will find the `invoke_url` among the stack outputs after a successful deployment.