Triggering Lambda Functions for Real-time ML Inference

Question

Pulumi · Accepted Answer

To trigger AWS Lambda functions for real-time ML (Machine Learning) inference, you first need a trained ML model which can be deployed into a Lambda function. AWS Lambda allows you to run code without provisioning or managing servers, which is perfect for setting up a real-time inference endpoint.

Here’s how you can achieve this:

1. **Create a Lambda Function**: The Lambda function will contain your inference code. This could be a Python function that loads your ML model, receives input data (like an image or numerical data), and returns a prediction.

2. **Set Up an API Gateway or Function URL (optional)**: To expose your Lambda function to the outside world, you can use an API Gateway or a direct Lambda Function URL. This step is optional as your trigger could be something else, e.g., a new file in an S3 bucket or a new record in a database.

3. **Create the Invocation**: Set up the actual invocation of the Lambda function when your specified trigger occurs.

Let's write a Pulumi program that sets up an ML inference Lambda function and exposes it via a Function URL for real-time invocation using an HTTP endpoint:

```python
import pulumi
import pulumi_aws as aws

# Assuming you have your trained ML model packaged with your Lambda function code.
# For example, a ZIP archive containing your function code and the serialized ML model.
ml_model_lambda_code = aws.s3.BucketObject("mlModelLambdaCode",
    bucket="my-lambda-function-bucket",
    key="ml_model_lambda.zip",
    source=pulumi.FileAsset("path/to/your/lambda/deployment/package.zip"),
)

# Create an IAM role that your Lambda function will assume
lambda_execution_role = aws.iam.Role("lambdaExecutionRole",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Principal": {
                "Service": "lambda.amazonaws.com"
            },
            "Effect": "Allow",
            "Sid": ""
        }]
    }""",
)

# Attach the AWS managed LambdaBasicExecutionRole policy to the role
# This provides permissions for writing logs to CloudWatch
lambda_execution_policy_attachment = aws.iam.RolePolicyAttachment("lambdaExecutionPolicyAttachment",
    role=lambda_execution_role.name,
    policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole",
)

# Create the Lambda function
ml_inference_lambda = aws.lambda_.Function("mlInferenceLambda",
    role=lambda_execution_role.arn,
    runtime=aws.lambda_.Runtime.PYTHON_3_8,  # Set the runtime as per your application
    handler="index.handler",  # Replace with your handler's location
    s3_bucket=ml_model_lambda_code.bucket,
    s3_key=ml_model_lambda_code.key,
    # Setting environment variables if any required by your inference code
    environment=aws.lambda_.FunctionEnvironmentArgs(
        variables={
            "MODEL_NAME": "my-ml-model",
        }
    ),
)

# Creating a Lambda function URL, this allows HTTP(S) access to your function.
lambda_url = aws.lambda_.FunctionUrl("mlInferenceLambdaUrl",
    function_name=ml_inference_lambda.name,
    authorization_type="NONE",  # This is an open API endpoint, you might want to secure this.
)

pulumi.export("lambda_function_name", ml_inference_lambda.name)
pulumi.export("lambda_function_url", lambda_url.function_url)  # This URL can be used for inference requests
```

In this Pulumi program:

- We create a bucket object for the Lambda function's code and ML model.
- We define an IAM role that the Lambda function will use for permissions. The managed `AWSLambdaBasicExecutionRole` policy allows it to write logs to CloudWatch.
- We create the actual Lambda function, pointing to the S3 location of our deployment package. The `handler` should be set to the entry point of your Lambda code.
- We create a Lambda Function URL (an HTTP(S) endpoint), which allows invoking the Lambda function directly without the need for API Gateway. We set the authorization type to `NONE`, which means no authorization is required to invoke the function. This is fine for demonstration but in production, you should secure access to your function.
- We export the Lambda function name and the function URL for use outside of Pulumi, e.g., to invoke the function from an application or curl requests.

Please replace `path/to/your/lambda/deployment/package.zip` with the actual path to your Lambda deployment package that should include your machine learning model and inference code.

Remember to secure your Lambda function URL before using it in production, you might want to limit access to known clients or implement authentication and authorization mechanisms.