Machine Learning Workflow Orchestration with Lambda Policies

Question

Pulumi · Accepted Answer

To orchestrate a machine learning workflow using AWS services, we can use AWS Step Functions for workflow orchestration and AWS Lambda for executing compute tasks. AWS Step Functions is a serverless orchestration service that allows you to define and coordinate multi-step workflows. AWS Lambda is a serverless compute service that automatically manages the compute resources for you and runs your code in response to events such as HTTP requests or Step Functions' state transitions.

In this program, you will see an example of how to create a simple machine learning workflow using Pulumi, AWS Step Functions, and AWS Lambda. This workflow consists of a state machine with two states: the first state invokes a Lambda function to perform a pre-processing task, and the second state invokes another Lambda function to perform a ML model prediction.

Below, we will outline the Pulumi program that sets up the necessary resources for such a workflow:

1. Define two AWS Lambda functions: one for data pre-processing and another for model prediction.
2. Define an AWS Step Functions state machine with two states, each configured to invoke one of the deployed Lambda functions.
3. Set up IAM roles and policies to grant the necessary permissions for Lambda functions and the Step Functions state machine.

Here's the detailed Pulumi program written in Python:

```python
import json
import pulumi
import pulumi_aws as aws

# Define an IAM role that allows AWS Lambda functions to be invoked by Step Functions.
lambda_execution_role = aws.iam.Role("lambdaExecutionRole",
    assume_role_policy=json.dumps({
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {
                "Service": "lambda.amazonaws.com"
            },
        }]
    })
)

# Attach the AWSLambdaBasicExecutionRole policy to the execution role.
basic_execution_policy_attachment = aws.iam.RolePolicyAttachment("basicExecutionPolicyAttachment",
    role=lambda_execution_role.name,
    policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole"
)

# Define a Lambda function for data pre-processing.
preprocess_lambda = aws.lambda_.Function("preprocessLambda",
    role=lambda_execution_role.arn,
    runtime="python3.8",
    handler="preprocess.handler",  # Assuming 'handler' is the entry point in the 'preprocess' file.
    code=pulumi.FileArchive("path/to/preprocess.zip")  # Path to the zipped directory containing your Python code and dependencies.
)

# Define a Lambda function for ML model prediction.
predict_lambda = aws.lambda_.Function("predictLambda",
    role=lambda_execution_role.arn,
    runtime="python3.8",
    handler="predict.handler",  # Assuming 'handler' is the entry point in the 'predict' file.
    code=pulumi.FileArchive("path/to/predict.zip")  # Path to the zipped directory containing your Python code and dependencies.
)

# Define an IAM role that allows AWS Step Functions to invoke AWS Lambda functions.
step_function_execution_role = aws.iam.Role("stepFunctionExecutionRole",
    assume_role_policy=json.dumps({
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {
                "Service": "states.amazonaws.com"
            },
        }]
    })
)

# Attach necessary policies to the Step Functions execution role.
lambda_invoke_policy_attachment = aws.iam.RolePolicyAttachment("lambdaInvokePolicyAttachment",
    role=step_function_execution_role.name,
    policy_arn="arn:aws:iam::aws:policy/service-role/AWSStepFunctionsFullAccess"
)

# Define an AWS Step Functions state machine to orchestrate the ML workflow.
state_machine_definition = {
    "Comment": "An example state machine that orchestrates a simple ML workflow.",
    "StartAt": "Preprocess",
    "States": {
        "Preprocess": {
            "Type": "Task",
            "Resource": preprocess_lambda.arn,
            "Next": "Predict"
        },
        "Predict": {
            "Type": "Task",
            "Resource": predict_lambda.arn,
            "End": True
        }
    }
}

state_machine = aws.sfn.StateMachine("stateMachine",
    role_arn=step_function_execution_role.arn,
    definition=pulumi.Output.all(preprocess_lambda.arn, predict_lambda.arn).apply(
        lambda arns: json.dumps(state_machine_definition).replace("PREPROCESS_LAMBDA_ARN", arns[0]).replace("PREDICT_LAMBDA_ARN", arns[1])
    )
)

# Export the ARN of the created state machine, so it can be invoked as needed.
pulumi.export("state_machine_arn", state_machine.id)
```

In this program, the `preprocess_lambda` and `predict_lambda` are Lambda functions that are meant to represent the preprocessing and inference tasks in your machine learning workflow. You would replace the `path/to/your/code.zip` placeholders with the paths to the zipped files of your Lambda function code.

The `state_machine` resource represents the AWS Step Functions state machine. Its definition outlines the workflow, starting with the `Preprocess` state, which invokes the preprocessing Lambda function, then proceeding to the `Predict` state, which invokes the prediction Lambda function and ends the workflow.

Once this Pulumi program is executed using the Pulumi CLI, it will provision the defined resources in your AWS account. You can then trigger the workflow by invoking the state machine, either manually through the AWS Console or by using the AWS SDK in your applications.