1. Real-time Inference APIs for ML Models with API Gateway


    To create real-time inference APIs for ML models with AWS API Gateway, you'll be using various AWS services, such as AWS Lambda to run your machine learning inference code, and AWS API Gateway to provide a RESTful API endpoint for clients to interact with your ML model. AWS S3 can be used to store any model artifacts if needed.

    The basic steps for setting this up include:

    1. Creating a Lambda Function: You'll use AWS Lambda to host your machine learning inference code. This code will be executed in response to API requests. You need to ensure that your Lambda function has the necessary permissions and runtime environment to execute your ML model.

    2. Defining an API Gateway: API Gateway acts as a front door for your API, handling incoming API calls, managing access, and routing requests to the designated backend, which in this case is the Lambda function.

    3. Configuring Integration: You need to set up an integration in API Gateway that connects your API endpoint to the Lambda function. This way, when API Gateway receives a request at the endpoint, it knows to invoke your Lambda function.

    4. Deploying the API: After setting up your resources, you'll create a deployment for your API Gateway. This puts your API into a stage (like a version) that can be called by clients.

    Below is a Pulumi program in Python that sets up these resources:

    import pulumi import pulumi_aws as aws # Create an S3 bucket to store the ML model artifacts if necessary ml_model_bucket = aws.s3.Bucket("mlModelBucket") # Create an IAM role which the Lambda function will use 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 give the function basic execution permissions lambda_execution_policy_attachment = aws.iam.RolePolicyAttachment("lambdaExecutionPolicyAttachment", role=lambda_role.name, policy_arn=aws.iam.ManagedPolicy.AWS_LAMBDA_BASIC_EXECUTION_ROLE.arn ) # You would include your ML inference code and any dependencies in a zipped package # For the purposes of this demo, we'll assume you have a zipped file named 'ml_inference.zip' lambda_function = aws.lambda_.Function("mlInferenceFunction", runtime=aws.lambda_.Runtime.PYTHON_3_8, code=pulumi.FileAsset("path_to_your_ml_inference_package.zip"), handler="your_module.your_handler_function", # Replace with the appropriate handler role=lambda_role.arn, timeout=90 # Adjust the timeout to your function's requirements ) # Create an API Gateway to make your Lambda accessible via HTTP api_gateway = aws.apigatewayv2.Api("mlInferenceApi", protocol_type="HTTP" ) # Create an integration to connect the Lambda to the API Gateway integration = aws.apigatewayv2.Integration("lambdaIntegration", api_id=api_gateway.id, integration_type="AWS_PROXY", integration_uri=lambda_function.invoke_arn ) # Set up a default route that connects to the Lambda integration default_route = aws.apigatewayv2.Route("defaultRoute", api_id=api_gateway.id, route_key="$default", # Note: $default route captures all requests target=pulumi.Output.concat("integrations/", integration.id) ) # Deploy the API Gateway deployment = aws.apigatewayv2.Deployment("apiDeployment", api_id=api_gateway.id, # Depends on explicit association to prevent deployment before the route is created __opts__=pulumi.ResourceOptions(depends_on=[default_route]) ) # Create a stage, which is a snapshot of the API deployment stage = aws.apigatewayv2.Stage("apiStage", api_id=api_gateway.id, deployment_id=deployment.id, name="prod" # Or any other stage name you prefer ) # Export the HTTP endpoint of the API Gateway so you can access it pulumi.export("api_endpoint", api_gateway.api_endpoint.apply(lambda endpoint: f"{endpoint}/{stage.name}"))

    In this program:

    • We create an S3 bucket to potentially store the ML model artifacts if your implementation requires this.
    • We define an IAM role that the Lambda function will assume, which grants it basic execution permissions.
    • We create the Lambda function that will run the ML inference code.
    • We create an API Gateway and an integration to invoke the Lambda function when the API endpoint is called.
    • We set up a default route $default which captures all requests made to the API.
    • We deploy the API and create a stage called prod.
    • Finally, the URL endpoint of the API Gateway is exported so it can be accessed outside of Pulumi.

    In a production environment, you would need to add more configurations such as security (like authentication and authorization), logging, monitoring, and fine-tuning Lambda and API Gateway settings, such as request and payload sizes. Ensure that you properly handle any sensitive information, like API keys and other credentials, using AWS Secrets Manager or AWS Systems Manager Parameter Store, and access them securely within your Pulumi code.