BentoML

Why we included this project

BentoML wraps a model inference script into a REST API with a few lines of Python and standard type hints, which is exactly what a team needs after it has trained or downloaded a model and wants to put it behind an endpoint. The library handles the serving chores that tend to eat up time: dependencies and environments live in a simple config file, Docker images are generated automatically so deploys stay reproducible, and optimizations like dynamic batching and model parallelism get more throughput out of your CPUs and GPUs. Because it works across different frameworks and runtimes, you can compose multi-model pipelines or mix custom business logic with inference in a single service rather than gluing several tools together. The same code runs locally for debugging, then ships as a Docker container or to a managed cloud, which makes the jump from prototype to production fairly painless.