1. API Gateway for Async AI Model Training Workflows.


    To create an API Gateway for asynchronous AI model training workflows, you'll need to define an API Gateway that can initiate the training process and return a response to the client without waiting for the training to complete. In this setup, you'll typically have an endpoint to start the training, which will trigger a background process (such as a Lambda function or a message queue to start the job on an EC2 instance or ECS container).

    Here's how you set up an AWS API Gateway to trigger an AWS Lambda function, which in turn can start an asynchronous AI model training workflow.

    1. AWS Lambda Function: It will serve as the backend for your API Gateway. This function will start the asynchronous process for AI model training.
    2. API Gateway: It exposes the Lambda function as a HTTP endpoint that clients can invoke.

    Below you will find a Python program using Pulumi to set up such an infrastructure:

    import pulumi import pulumi_aws as aws # Create an IAM role that can be assumed by AWS Lambda. 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 Lambda function role. lambda_role_policy_attachment = aws.iam.RolePolicyAttachment("lambdaRolePolicyAttachment", role=lambda_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole") # Define the Lambda function that will initiate the AI model training. training_lambda = aws.lambda_.Function("trainingLambda", code=pulumi.AssetArchive({ '.': pulumi.FileArchive('./training_lambda') # This should be the directory containing your Lambda code. }), role=lambda_role.arn, handler="training.handler", # Assuming the Lambda function is defined in a file called `training.py` with a handler named `handler`. runtime="python3.8") # Use the appropriate runtime for your Lambda function. # Create the API Gateway REST API. api = aws.apigateway.RestApi("api", description="API for asynchronous AI model training.") # Create a resource representing the AI model training endpoint. training_resource = aws.apigateway.Resource("trainingResource", parent_id=api.root_resource_id, path_part="train-model", rest_api=api) # Create a POST method on the training endpoint. training_post_method = aws.apigateway.Method("trainingPostMethod", rest_api=api, resource_id=training_resource.id, http_method="POST", authorization="NONE") # Integrate the POST method with the Lambda function. training_integration = aws.apigateway.Integration("trainingIntegration", http_method=training_post_method.http_method, resource_id=training_resource.id, rest_api=api, integration_http_method="POST", # The HTTP method used for the Lambda function invocation. type="AWS_PROXY", # Use AWS_PROXY integration for Lambda functions. uri=training_lambda.invoke_arn) # Deploy the API. api_deployment = aws.apigateway.Deployment("apiDeployment", rest_api=api, # The stage_name parameter is required; you can use any value you like. # This is the identifier for the deployment environment, e.g., staging, production, etc. stage_name="v1", # The depends_on parameter ensures that the deployment is only performed after the POST method integration is set up. depends_on=[training_integration]) # Export the API endpoint URL. pulumi.export('api_url', api_deployment.invoke_url.apply(lambda url: f"{url}train-model"))

    This code sets up the AWS Lambda and API Gateway with Pulumi:

    • We define an IAM role for the Lambda that allows it to run and log to CloudWatch.
    • We then create the Lambda function, specifying its location on disk, the role it uses, its handler, and the runtime.
    • An API Gateway is created with a description. It defines a single train-model resource that allows clients to POST to start the AI model training process.
    • Integration between the API Gateway and the Lambda function is established, connecting them so that a POST to the train-model endpoint will invoke the Lambda function.
    • Finally, we deploy the API and provide an endpoint URL.

    Make sure that you have the AWS Pulumi plugin installed, your AWS credentials configured, and the Lambda code available in the ./training_lambda directory.

    This program should give you a basic asynchronous AI model training API setup. You can build upon this depending on the specifics of your workflow. For example, you might want to use AWS Step Functions to orchestrate more complex workflows.