1. Serverless AI Model Hosting on AWS S3


    Serverless AI Model Hosting on AWS typically involves deploying a machine learning model that can be run without provisioning or managing servers. In this context, AWS S3 can serve as the storage service to host the model artifacts, and AWS Lambda can be used to run code in response to triggers such as HTTP requests via API Gateway. Amazon SageMaker can also be used to build, train, and deploy machine learning models quickly.

    The following Pulumi Python program demonstrates how to set up serverless AI model hosting on AWS using Pulumi. The program will do the following:

    1. Create an S3 bucket for storing the AI model.
    2. Deploy a SageMaker model using a pre-existing Docker image (typically, you'd have a Docker image containing your model and inference code).
    3. Set up a Lambda function which will serve as the serverless inference endpoint.
    4. Create an API Gateway that triggers the Lambda function, providing an HTTP endpoint for model inference.

    Here's what the Pulumi Python program looks like:

    import pulumi import pulumi_aws as aws # Create an S3 bucket to store the model artifacts. ai_model_bucket = aws.s3.Bucket("aiModelBucket", acl="private", versioning=aws.s3.BucketVersioningArgs( enabled=True, )) # Deploy a SageMaker model. # Assuming a Docker image for the model is already available in ECR. sagemaker_model = aws.sagemaker.Model("aiModel", execution_role_arn=aws.iam.Role("aiModelRole", assume_role_policy="...").arn, # Replace "..." with the appropriate assume role policy json. primary_container=aws.sagemaker.ModelPrimaryContainerArgs( image="123456789012.dkr.ecr.region.amazonaws.com/your-model:latest", # Replace with your actual image URI. # The model artifact (e.g., a serialized model file) would be in S3. model_data_url=ai_model_bucket.bucket.apply(lambda bucket_name: f"s3://{bucket_name}/model.tar.gz"), )) # Create an AWS Lambda function which acts as an endpoint to perform inference using the model. inference_lambda_function = aws.lambda_.Function("aiModelInferenceFunction", code=pulumi.AssetArchive({ '.': pulumi.FileArchive('./path_to_your_inference_code'), # Replace with the path to your inference code folder. }), runtime=aws.lambda_.Runtime.PYTHON_3_8, # Change if you use a different runtime. role=aws.iam.Role("lambdaExecutionRole", assume_role_policy="...").arn, # Replace "..." with the appropriate assume role policy json. handler="handler.main", # Update this to the file and method you want to use as an entry point for Lambda invocations. # Here, you might also set up environment variables or other configurations needed by your Lambda. ) # Provision an API Gateway to trigger the Lambda function. api_gateway = aws.apigatewayv2.Api("aiModelApiGateway", protocol_type="HTTP", route_key="POST /model/infer", target=inference_lambda_function.invoke_arn, ) pulumi.export('sagemakerModelName', sagemaker_model.name) pulumi.export('inferenceLambdaFunctionName', inference_lambda_function.name) pulumi.export('apiEndpoint', api_gateway.api_endpoint)

    Make sure to replace placeholders such as the role policies, Docker image URI, and inference code path with actual values corresponding to your AWS setup and model. Additionally, the Lambda function's code and handler will need to be written by you to perform actual model inference.

    This program uses AWS Pulumi resources to set up the serverless architecture. Here's the breakdown:

    • S3 Bucket: Used to store and version control the AI model artifacts.
    • SageMaker Model: Represents the machine learning model hosted on AWS. It points to the Docker image stored in ECR and the model data in S3.
    • Lambda Function: This is the serverless component that runs inference code upon receiving an HTTP request. The code for inference needs to be provided by the user.
    • API Gateway: Sets up an HTTP endpoint that triggers the Lambda function, allowing for model inference through POST requests.

    Before running this code, ensure you have Pulumi set up along with the AWS Pulumi plugin and your AWS credentials configured. You will also need to write the AWS Lambda handler to load your model and perform inference. This handler should be placed in the specified path, './path_to_your_inference_code'.