Large Language Model API Hosting on AWS Lambda

Question

Pulumi · Accepted Answer

Hosting a Large Language Model API on AWS Lambda involves creating a serverless compute environment where your API code can execute. AWS Lambda allows you to run code without provisioning or managing servers, and you pay only for the compute time you consume. Integrating an API with AWS Lambda can be done through Amazon API Gateway, which will handle the HTTP requests and route them to your Lambda function.

Here's an outline of the steps involved:

1. **Create an AWS Lambda Function**: This is where your API logic will reside. You'll need to package your code and dependencies in a way that Lambda can execute.
2. **Set Up Amazon API Gateway**: You'll configure an HTTP endpoint that listens for requests and forwards them to your Lambda function.
3. **Permissions**: Proper permissions need to be set so that API Gateway can invoke your Lambda function.
4. **Deployment**: After setting up the resources, you deploy your API to make it accessible from the internet.

Now let's dive into the Pulumi code to accomplish this.

```python
import pulumi
import pulumi_aws as aws

# Define the IAM role that will allow the Lambda function to run
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 created above
policy_attachment = aws.iam.RolePolicyAttachment("lambdaPolicyAttachment", 
    role=lambda_role.name,
    policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole")

# Create the Lambda function
lambda_function = aws.lambda_.Function("myApiLambdaFunction",
    role=lambda_role.arn,
    runtime="python3.8",  # Replace with the runtime of your choice
    handler="app.handler",  # Replace with your handler file and method
    code=pulumi.FileArchive("./api_code.zip"),  # The path to the zipped code
    timeout=30,  # The function's maximum execution time
    memory_size=1024)  # Adjust memory based on your needs

# Define an API Gateway to make the Lambda function accessible over HTTPS
api_gateway = aws.apigatewayv2.Api("myApiGateway",
    protocol_type="HTTP")

# Create a default route linked to the Lambda function
integration = aws.apigatewayv2.Integration("myLambdaIntegration",
    api_id=api_gateway.id,
    integration_type="AWS_PROXY",  # This integrates the API with Lambda
    integration_uri=lambda_function.invoke_arn
)

# Set up the route to invoke the Lambda integration
route = aws.apigatewayv2.Route("anyRoute",
    api_id=api_gateway.id,
    route_key="$default",  # This is the default route. You can define specific routes as required.
    target=pulumi.Output.concat("integrations/", integration.id)
)

# Deploy the API Gateway
deployment = aws.apigatewayv2.Deployment("apiDeployment",
    api_id=api_gateway.id,
    # Enforce a new deployment when the API gateway or the route is modified
    triggers={"redeployment": pulumi.Output.all(api_gateway.id, route.id).apply(lambda args: str(hash(args)))},
    opts=pulumi.ResourceOptions(depends_on=[route])
)

# Create a stage for deploying
stage = aws.apigatewayv2.Stage("apiStage",
    api_id=api_gateway.id,
    name="v1",
    deployment_id=deployment.id,
    auto_deploy=True
)

# Give the API Gateway permission to invoke the Lambda function
invoke_permission = aws.lambda_.Permission("apiGatewayInvoke",
    action="lambda:InvokeFunction",
    function=lambda_function.name,
    principal="apigateway.amazonaws.com",
    # This is important for allowing invocation via API Gateway
    source_arn=stage.execution_arn.apply(lambda execution_arn: f"{execution_arn}*/*")
)

# Export the API endpoint URL
pulumi.export("api_url", pulumi.Output.concat("https://", api_gateway.id, ".execute-api.", pulumi.get_region(), ".amazonaws.com/", stage.name))
```

Here’s a walkthrough of each part of the program:

- **IAM Role and Policy**: We create an IAM role with a policy that allows Lambda to execute and log to CloudWatch.
- **Lambda Function**: We define our Lambda function, setting the runtime, handler, and uploading our zipped codebase.
- **API Gateway**: Set up an HTTP API Gateway as the front door to access our Lambda.
- **Lambda Integration**: Create an integration to connect API routes to our Lambda function.
- **Route**: A default `$default` route is used to forward all requests to the Lambda integration.
- **Deployment**: We create a deployment to make our API live.
- **Stage**: Stages are "environments" for deployments. We define a `v1` stage.
- **Permissions**: It's essential to give API Gateway permission to invoke your Lambda function.
- **Export API URL**: Finally, we export the URL for the deployed API so that you can access it externally.

To run this program, you must have:
- Python 3 installed
- Pulumi CLI installed
- AWS CLI configured with credentials

After setting your Pulumi project and installing the required Python packages, execute `pulumi up` to deploy the resources to AWS.

Please replace `"app.handler"` and `"./api_code.zip"` with the appropriate handler path and Lambda deployment package path, respectively. The handler is the entry point to your application, typically formatted as `"filename.method"`. The deployment package should contain your application code and all dependencies.