1. Edge Inference for ML Models with AWS CloudFront


    To accomplish edge inference for machine learning (ML) models with AWS CloudFront, we would typically integrate a few AWS services together. At a high level, the setup would involve:

    1. Amazon S3: Hosting the ML model files.
    2. AWS Lambda: An AWS service that lets you run code without provisioning or managing servers. We will create a Lambda function that will perform inference using the ML model. This function is invoked by AWS CloudFront when a request is made.
    3. AWS CloudFront: A fast content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally with low latency. CloudFront can be configured to trigger the AWS Lambda function to perform edge inference.

    Here's a basic Pulumi program in Python to set up such a system:

    1. CloudFront Distribution: We'll create a CloudFront distribution that will act as the CDN for our system. When a request hits an edge location, if the request requires inference, it will trigger a Lambda function.
    2. Lambda Function: The Lambda function will perform the actual inference. This function will be deployed to AWS Lambda@Edge, which allows the function to execute closer to the end-users to ensure low latency.

    Let's go ahead and write the Pulumi program that will create these resources. Note that this program assumes that you've pre-built Lambda function zip that contains your model and inference code.

    import pulumi import pulumi_aws as aws # Create an S3 bucket to store our Lambda code and ML model model_bucket = aws.s3.Bucket("mlModelBucket", # Bucket settings can be customized as needed. For instance, you may want # to enable versioning to keep a history of your model binaries ) # Upload the Lambda function and ML model to S3 # This assumes that you've zipped your Lambda function and model into lambda.zip lambda_zip = aws.s3.BucketObject("lambdaZip", bucket=model_bucket.id, key="lambda.zip", source=pulumi.FileAsset("path/to/your/lambda.zip") # Replace with path to your Lambda function zip ) # Create the Lambda function that will do our inference # This function will run in response to CloudFront events inference_lambda = aws.lambda_.Function("inferenceLambda", runtime="python3.8", code=aws.s3.get_bucket_object(bucket=model_bucket.id, key=lambda_zip.key), handler="handler.main", # Replace 'handler.main' with your function handler role=inference_lambda_role.arn ) # Create a new CloudFront distribution cdn = aws.cloudfront.Distribution("cdn", enabled=True, # The 'default_cache_behavior' defines the behavior for requests that do not # match any other cache behavior default_cache_behavior={ "viewer_protocol_policy": "allow-all", "target_origin_id": "myOriginID", "lambda_function_associations": [{ "event_type": "origin-request", "lambda_arn": inference_lambda.arn }], }, origins=[{ "origin_id": "myOriginID", "domain_name": pulumi.Output.concat(model_bucket.bucket_regional_domain_name, "/", lambda_zip.key), }], ) # Export the CloudFront domain name to access the CDN pulumi.export("cdn_url", cdn.domain_name)

    This is a basic setup and doesn't cover aspects like authentication, fine-grained cache behaviors, or complex CloudFront configurations. Depending on your needs you may have to adjust the above resources. For example, if your inference code is large or depends on many libraries, you might need to use AWS Lambda Layers to manage your code and dependencies better. Additionally, your Lambda function's IAM role will need permissions that are specific to what your function needs to do.

    For the actual inference code running within AWS Lambda, you would typically load your ML model from the S3 bucket and then run predictions based on the input data extracted from the CloudFront request events.

    Please make sure you have all prerequisites, such as having the AWS CLI set up with appropriate credentials, Pulumi CLI installed, and your Lambda zip package ready with all necessary dependencies included.