fair-forge-lambda

To deploy Python modules not on PyPI to AWS Lambda, you can package them as container images with pre-built wheels. This approach bundles local dependencies directly into a Docker image, which is then pushed to ECR and deployed as a Lambda function with an API Gateway.

The best way to deploy Fair-Forge metrics and runners as serverless API endpoints is using AWS Lambda containers. You build a Docker image with pre-built wheels, push it to ECR, and create an API Gateway to expose the runners and generators via HTTP endpoints.

Yes, you need Docker and AWS CLI configured to deploy these serverless containers. Proper IAM permissions are also required to create ECR repositories, IAM roles, Lambda functions, and HTTP APIs for the deployment to succeed.

Yes, you can run local tests on Fair-Forge modules before deploying them to AWS Lambda. The deployment process includes guidelines for building the Docker image locally, allowing you to validate metrics, runners, and generators before pushing to ECR and creating the API Gateway.

Deploying Fair-Forge as AWS Lambda containers handles multi-region scalability by leveraging AWS serverless infrastructure. Once the container is pushed to ECR and the API Gateway is configured, the Lambda functions can scale automatically across regions to handle varying loads.

Pre-built wheels are required for deploying local repositories to Lambda because the source package is not available on PyPI. Installing wheels directly into the Docker image ensures all dependencies are resolved and bundled correctly for the AWS Lambda container environment.