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Overview

ModelMesh is a mature, general-purpose model serving management/routing layer designed for high-scale, high-density and frequently-changing model use cases. It works with existing or custom-built model servers and acts as a distributed LRU cache for serving runtime models.

For full Kubernetes-based deployment and management of ModelMesh clusters and models, see the ModelMesh Serving repo. This includes a separate controller and provides K8s custom resource based management of ServingRuntimes and InferenceServices along with common, abstracted handling of model repository storage and ready-to-use integrations with some existing OSS model servers.

For more information on supported features and design details, see these charts.

What is a model?

In ModelMesh, a model refers to an abstraction of machine learning models. It is not aware of the underlying model format. There are two model types: model (regular) and vmodel. Regular models in ModelMesh are assumed and required to be immutable. VModels add a layer of indirection in front of the immutable models. See VModels Reference for further reading.

Implement a model runtime

1. Wrap your model-loading and invocation logic in this model-runtime.proto gRPC service interface.
   • runtimeStatus() - called only during startup to obtain some basic configuration parameters from the runtime, such as version, capacity, model-loading timeout.
   • loadModel() - load the specified model into memory from backing storage, returning when complete.
   • modelSize() - determine size (memory usage) of previously-loaded model. If very fast, can be omitted and provided instead in the response from loadModel.
   • unloadModel() - unload previously loaded model, returning when complete.
   • Use a separate, arbitrary gRPC service interface for model inferencing requests. It can have any number of methods and they are assumed to be idempotent. See predictor.proto for a very simple example.
   • The methods of your custom applier interface will be called only for already fully-loaded models.
2. Build a grpc server docker container which exposes these interfaces on localhost port 8085 or via a mounted unix domain socket.
3. Extend the Kustomize-based Kubernetes manifests to use your docker image, and with appropriate memory and CPU resource allocations for your container.
4. Deploy to a Kubernetes cluster as a regular Service, which will expose this grpc service interface via kube-dns (you do not implement this yourself), consume using grpc client of your choice from your upstream service components.
   • registerModel() and unregisterModel() for registering/removing models managed by the cluster
   • Any custom inferencing interface methods to make a runtime invocation of previously-registered model, making sure to set a mm-model-id or mm-vmodel-id metadata header (or -bin suffix equivalents for UTF-8 ids)

Development

Please see the Developer Guide for details.