Serverless Batch Processing for AI Tasks with Lambda

Question

Pulumi · Accepted Answer

Serverless batch processing is an efficient way to handle asynchronous, compute-intensive tasks, such as those commonly found in artificial intelligence (AI) applications. AWS Lambda is a serverless computing service that runs your code in response to events and automatically manages the underlying compute resources for you.

In the context of AI tasks, you might have a Lambda function that is triggered by various events – for example, a new image is uploaded to an S3 bucket, and you want to run some image recognition on it, or you are processing a batch of data stored in an SFTP location.

In the following program, we'll create a basic setup for a serverless batch processing using AWS Lambda. The program does the following:
1. Defines an AWS Lambda function with the necessary runtime and handler information.
2. Sets the required execution role with appropriate permissions.
3. Creates an S3 bucket to store the input data that the Lambda function will process.

Additionally, you will see comments explaining each part of the program. The role permissions here are kept minimal for demonstration, but in a real-world scenario, you may need to adjust these according to your specific use case.

```python
import pulumi
import pulumi_aws as aws

# Create an IAM role for the Lambda function
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 AWS Lambda Basic Execution Role policy to the IAM role
lambda_exec_policy_attachment = aws.iam.RolePolicyAttachment("lambdaExecPolicyAttachment",
    role=lambda_role.name,
    policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole")

# Create a Lambda function
lambda_function = aws.lambda_.Function("myLambdaFunction",
    runtime="python3.8",                         # Runtime language
    code=pulumi.AssetArchive({                   # Code directory, archive it
        '.': pulumi.FileArchive('./app')         # Assuming your lambda code is in "app" directory
    }),
    handler="app.lambda_handler",                # The entry point into your code e.g., app.lambda_handler calls lambda_handler in app.py
    role=lambda_role.arn)                        # IAM role with permissions for the Lambda function

# Creating an S3 bucket to store batch files
s3_bucket = aws.s3.Bucket("batchProcessingBucket")

# Output the name of the bucket
pulumi.export("bucket_name", s3_bucket.id)

# Output the Lambda Function ARN
pulumi.export("lambda_function_arn", lambda_function.arn)

# You must replace app.lambda_handler with the actual handler function in your application
# and 'python3.8' with the runtime you are going to use. If your dependencies are large,
# you might want to consider using Lambda Layers or S3 bucket to store your function code.
# Also, you would need to replace './app' with the path to the directory of your Lambda function code.
```

In this Pulumi program, we have set up a basic serverless environment in which you can deploy AI tasks. The Lambda function handler (`app.lambda_handler`) should be the entry-point to your AI application, and it's assumed this will handle the specific event sources and processing logic you need.

If you're going to have your Lambda function triggered by events, such as new files in an S3 bucket or messages on an SQS queue, you would need to set up these triggers using additional resources and permissions.

Remember to replace placeholder values like the path to the Lambda code (`./app`) and your handler function (`app.lambda_handler`) with actual values from your project. The `runtime` parameter should match the runtime of your application; for Python applications, it's typically set as a version of Python like 'python3.8'.

Run this Pulumi program by saving the code into a file named `__main__.py` within a Pulumi project directory. Ensure your AWS credentials are configured for Pulumi using the AWS CLI or environment variables. Then execute `pulumi up` in the command line within the same directory as your project to deploy the infrastructure.