Lambda for Efficient Batch Scoring of Machine Learning Models

Question

Pulumi · Accepted Answer

To create a Lambda function for efficient batch scoring of machine learning models, you will need to write a Pulumi program that sets up an AWS Lambda function with the necessary configuration to run your machine learning inference code. The following components are typically involved in setting up such a Lambda function:

1. **Lambda Function**: The AWS Lambda service will run your machine learning model's scoring code. You need to package your code and dependencies and specify the runtime environment suitable for your code.

2. **IAM Role**: This role provides the Lambda function with the necessary permissions to access other AWS services that might be needed during the execution, such as Amazon S3 for reading input data or writing results.

3. **Layers (optional)**: If your function dependencies are large or you want to share code between multiple Lambda functions, you might opt to use Lambda layers.

4. **Lambda Function URL (optional)**: In case you want to invoke your Lambda function over HTTPS without using API Gateway, creating a Lambda Function URL would be the way to go.

Below is a detailed Pulumi program written in Python that sets up a Lambda function for batch scoring with an accompanying IAM role. This program assumes you have a machine learning inference code packaged in a ZIP file referred to as `function_code_path`.

```python
import pulumi
import pulumi_aws as aws

# Assume the ML model code is zipped and available at a specific path
function_code_path = 'path/to/your/deployment/package.zip'

# IAM Role for the Lambda Function - Grants the Lambda Function necessary permissions
lambda_role = aws.iam.Role('lambda-role',
    assume_role_policy="""{
      "Version": "2012-10-17",
      "Statement": [{
        "Action": "sts:AssumeRole",
        "Principal": {
            "Service": "lambda.amazonaws.com"
        },
        "Effect": "Allow",
        "Sid": ""
      }]
    }"""
)

# Attach a policy to the role. This policy allows writing logs to CloudWatch
policy_attachment = aws.iam.RolePolicyAttachment('lambda-role-policy',
    role=lambda_role.name,
    policy_arn=aws.iam.ManagedPolicy.AWS_LAMBDA_BASIC_EXECUTION_ROLE
)

# Create the Lambda function
lambda_function = aws.lambda_.Function('ml-batch-scoring-lambda',
    code=pulumi.FileArchive(function_code_path),
    role=lambda_role.arn,
    handler='lambda_function.lambda_handler',  # Assuming the entry point to your ML model scoring code is `lambda_handler`
    runtime='python3.8',  # Use the appropriate runtime for your lambda function code
    # Define environment variables if your function needs them
    environment=aws.lambda_.FunctionEnvironmentArgs(
        variables={
            'MODEL_NAME': 'my-model',  # example environment variable
            # Add more environment variables if needed
        },
    ),
    # You can set this value to increase the Lambda memory size for resource-intensive tasks
    memory_size=512
)

# Output the Lambda Function Name and ARN which can be used to invoke this function
pulumi.export('lambda_function_name', lambda_function.name)
pulumi.export('lambda_function_arn', lambda_function.arn)
```

This Pulumi program sets up a Lambda function along with the necessary IAM role for batch scoring a machine learning model. You can modify the `function_code_path` to point to your deployment package and the `runtime` to match the programming language of your Lambda function.

The `handler` is set to `lambda_function.lambda_handler`, which assumes that your zip file includes a file named `lambda_function.py` with a `lambda_handler` function defined in it. This is the entry point for your batch scoring code.

Notice the use of `pulumi.FileArchive` to point to the zip file containing your Lambda function code, and `aws.lambda_.FunctionEnvironmentArgs` to set any environment variables your Lambda function code might need.

The IAM role is configured using a trust relationship policy that allows Lambda services to assume this IAM role (`aws.iam.Role`), and a policy attachment (`aws.iam.RolePolicyAttachment`) that grants permission for basic Lambda execution, including writing logs to Amazon CloudWatch.

Finally, `pulumi.export` statements allow you to output the Lambda function's name and ARN, which can be used to invoke your function either programmatically or via AWS CLI.

Please ensure that your AWS account is set up with the correct permissions and that the AWS Pulumi provider is properly configured before running this program.