Serverless Model Inference with AWS Lambda

Question

Pulumi · Accepted Answer

To accomplish serverless model inference with AWS Lambda, you'll deploy a Lambda function that performs inference using a pre-trained machine learning model. AWS Lambda is a serverless compute service that allows you to run code without provisioning or managing servers. To integrate the Lambda function with your client applications, you can use Amazon API Gateway, which will provide a RESTful endpoint for clients to invoke your Lambda function.

Here's an outline of the steps you'll take:

1. Package your inference code and any dependencies into a deployment package (a .zip file that contains your code and any dependencies).
2. Define an IAM role that the Lambda function will assume, allowing it to access other AWS services if necessary.
3. Create a Lambda function by providing the deployment package and specifying the IAM role and other configuration details.
4. Create an API Gateway to expose your Lambda function as an HTTP endpoint.
5. Deploy your API Gateway so that it's available on the internet.

Below is a Pulumi program written in Python that demonstrates how to set up serverless model inference using AWS Lambda and API Gateway:

```python
import pulumi
import pulumi_aws as aws

# Step 1: Assume you've built a zip file "inference_function.zip" that includes your code and any dependencies
# This file should be located in the same directory as your Pulumi program.

# Step 2: Create an IAM role and attach the AWSLambdaBasicExecutionRole policy
lambda_role = aws.iam.Role("lambdaRole",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {"Service": "lambda.amazonaws.com"}
        }]
    }""")

lambda_role_policy_attachment = aws.iam.RolePolicyAttachment("lambdaRoleAttachment",
    role=lambda_role.name,
    policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole")

# Step 3: Create the lambda function
lambda_function = aws.lambda_.Function("modelInferenceFunction",
    code=pulumi.FileArchive("./inference_function.zip"),
    role=lambda_role.arn,
    handler="inference.handler",  # Assuming your handler function is named 'handler' and is within a file named 'inference.py'
    runtime="python3.8",  # Make sure to specify the correct runtime
    timeout=30)  # Timeout for the function execution in seconds

# Step 4: Set up API Gateway to create an HTTP endpoint for the Lambda function
api_gateway = aws.apigatewayv2.Api("apiGateway",
    protocol_type="HTTP")

# Create a Lambda permission to allow API Gateway to invoke the Lambda function
lambda_permission = aws.lambda_.Permission("lambdaPermission",
    action="lambda:InvokeFunction",
    function=lambda_function.arn,
    principal="apigateway.amazonaws.com",
    source_arn=api_gateway.execution_arn.apply(lambda execution_arn: f"{execution_arn}/*/*"))

# Create an integration between API Gateway and Lambda
integration = aws.apigatewayv2.Integration("apiGatewayIntegration",
    api_id=api_gateway.id,
    integration_type="AWS_PROXY",  # Use AWS_PROXY integration for Lambda
    integration_uri=lambda_function.arn,
    payload_format_version="2.0")  # Use payload format version 2.0

# Define API Gateway route for invoking the Lambda function
route = aws.apigatewayv2.Route("apiGatewayRoute",
    api_id=api_gateway.id,
    route_key="POST /infer",  # POST method for the inference endpoint
    target=integration.id.apply(lambda id: f"integrations/{id}"))

# Step 5: Deploy the API Gateway
deployment = aws.apigatewayv2.Deployment("apiGatewayDeployment",
    api_id=api_gateway.id,
    description="Deployment for the model inference API")

# Create a stage for deployment
stage = aws.apigatewayv2.Stage("apiGatewayStage",
    api_id=api_gateway.id,
    deployment_id=deployment.id,
    name="prod")  # Stage name can be anything. Here we name it 'prod'.

# Export the HTTP endpoint URL
pulumi.export("endpoint_url", stage.invoke_url.apply(lambda url: f"{url}infer"))
```

Explanation:
- **IAM Role**: It's essential for Lambda to execute under an IAM role with appropriate permissions. In this code, we create an IAM role and attach the `AWSLambdaBasicExecutionRole` policy. This policy has the necessary permissions for the Lambda function to execute and create logs in CloudWatch.
  
- **Lambda Function**: We define a Lambda function named `modelInferenceFunction`, which is given the deployment package with the inference code, the IAM role ARN, the handler's location within the deployment package, and the runtime corresponding to the language you're using.

- **API Gateway**: An API Gateway named `apiGateway` is created with an HTTP protocol to expose the Lambda function.

- **Lambda Permission**: The `lambdaPermission` allows the API Gateway to invoke the Lambda function.

- **Integration**: The `integration` connects the API Gateway to the Lambda function using the `AWS_PROXY` integration, allowing for full control over the request and response.

- **Route**: The `route` defines the URL path and method for the REST API that will invoke the Lambda function.

- **Deployment**: Finally, the API Gateway is deployed using a `deployment` and `stage`. You can then access the model inference function via the URL provided after deployment.

This Pulumi program sets up a complete serverless architecture for model inference. Ensure you have the Pulumi CLI installed and AWS credentials configured for Pulumi to deploy these resources.