Scalable API Endpoints for AI Model Inference with AWS Lambda

Question

Pulumi · Accepted Answer

To set up scalable API endpoints for AI model inference with AWS Lambda, we'll need to create the following:

1. **AWS Lambda Function**: This will be our serverless compute service where our AI model code will be executed.
2. **Amazon API Gateway**: To provide a RESTful endpoint for clients to call the Lambda function.
3. **IAM Role and Policy**: To grant the necessary permissions for the Lambda function and API Gateway to interact with other AWS services.

Here's what each component does in the system:

- The **AWS Lambda function** runs your code in response to triggers such as changes in data, shifts in system state, or actions by users. For AI inference, the Lambda function can load a model and run inference based on input it receives.
- **API Gateway** acts as a front door for applications to access data, business logic, or functionality from your backend services, such as workloads running on AWS Lambda.
- The **IAM Role and Policy** grant your Lambda function permissions to access AWS services and resources. In this case, it must have enough permissions to log to CloudWatch and possibly to access other services like Amazon S3 if your Lambda needs to fetch the model or other resources.

Now let's write a Pulumi program in Python to create these resources on AWS.

```python
import pulumi
import pulumi_aws as aws

# Create an IAM role and policy to allow Lambda access to CloudWatch Logs
lambda_role = aws.iam.Role('lambdaRole',
    assume_role_policy=aws.iam.get_policy_document(statements=[
        aws.iam.GetPolicyDocumentStatementArgs(
            principals=[
                aws.iam.GetPolicyDocumentStatementPrincipalArgs(
                    type='Service',
                    identifiers=['lambda.amazonaws.com'],
                ),
            ],
            actions=['sts:AssumeRole'],
        ),
    ]).json,
)

# Attach the AWSLambdaBasicExecutionRole policy to the role
lambda_role_policy_attachment = aws.iam.RolePolicyAttachment('lambdaRolePolicyAttachment',
    role=lambda_role.name,
    policy_arn='arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole',
)

# Create a Lambda function, assuming you have a ZIP archive with your code/dependencies in `./function.zip`
lambda_function = aws.lambda_.Function('aiModelInferenceFunction',
    code=pulumi.FileArchive('./function.zip'),
    handler='index.handler',  # Name of the handler function
    role=lambda_role.arn,
    runtime='python3.8',
)

# Grant API Gateway permissions to invoke the Lambda function
api_gateway_policy = aws.lambda_.Permission('apiGatewayPolicy',
    action='lambda:InvokeFunction',
    function=lambda_function.name,
    principal='apigateway.amazonaws.com',
    # The `api` wildcard works here because we are not restricting which API Gateway can invoke this Lambda,
    # however, in production, it is recommended to scope this down to your specific API Gateway.
    source_arn=pulumi.Output.concat('arn:aws:execute-api:', aws.get_region().id, ':', aws.get_caller_identity().account_id, ':*/*'),
)

# Create an API Gateway to create RESTful API for the Lambda function
api_gateway = aws.apigatewayv2.Api('apiEndpoint',
    protocol_type='HTTP',
    route_selection_expression='$request.method $request.path',
)

# Create a default route linked to the Lambda function
default_route = aws.apigatewayv2.Route('defaultRoute',
    api_id=api_gateway.id,
    route_key='$default',
    target=pulumi.Output.concat('integrations/', lambda_function.arn),
)

# Deploy the API Gateway
deployment = aws.apigatewayv2.Deployment('apiDeployment',
    api_id=api_gateway.id,
    # This empty `depends_on` is a placeholder for any dependencies that must be satisfied before the deployment.
    # Example: other route creations or changes on the API Gateway.
    opts=pulumi.ResourceOptions(depends_on=[default_route]),
)

# Create a stage, which is a snapshot of the deployment
stage = aws.apigatewayv2.Stage('stage',
    api_id=api_gateway.id,
    deployment_id=deployment.id,
    auto_deploy=True,
)

# Export the HTTP endpoint for the AI model inference
pulumi.export('ai_model_inference_http_url', api_gateway.api_endpoint)
```

In the above program:

- We've defined an IAM role and attached a predefined AWS policy that allows Lambda functions to upload logs to CloudWatch (`AWSLambdaBasicExecutionRole`).
- We've created a Lambda function that expects a handler function at `index.handler` within the uploaded ZIP file (`./function.zip`). You should replace `index.handler` with the correct handler path and method for your application.
- We've set up an API Gateway for HTTP protocols and created a default route that triggers our Lambda function.
- We've deployed the API and created a stage for it named `stage`, with `auto_deploy` enabled to make sure any changes to the deployment are automatically deployed.
- Lastly, we export the endpoint URL that clients will use to access the AI inference capabilities.

Remember to replace the `function.zip` with the path to your actual Lambda deployment package, and adjust the handler and runtime if necessary.

You will need to ensure you have the AWS Pulumi provider installed and configured with appropriate AWS credentials before running this program. Once you run it with `pulumi up`, you will receive the endpoint URL as output, which you can use to invoke your AI inference Lambda function.