Model registry provides a central repository for model developers to store and manage models, versions, and artifacts metadata. A Go-based application that leverages ml_metadata project under the hood.

• Red Hat drives the project's development through Open Source principles, ensuring transparency, sustainability, and community ownership.
• Red Hat values the Kubeflow community and commits to providing a minimum of 12 months' notice before ending project maintenance after the initial release.

• go >= 1.19
• protoc v24.3 - Protocol Buffers v24.3 Release
• npm >= 10.2.0 - Installing Node.js and npm
• Java >= 11.0
• python 3.9

The model registry proxy server implementation follows a contract-first approach, where the contract is identified by model-registry.yaml OpenAPI specification.

You can also easily display the latest OpenAPI contract for model-registry in a Swagger-like editor directly from this repository; for example, here.

Run the following command to start the OpenAPI proxy server from source:

make run/proxy

The proxy service implements the OpenAPI defined in model-registry.yaml to create a Model Registry specific REST API on top of the existing ml-metadata server.

NOTE The ml-metadata server must be running and accessible from the environment where model-registry starts up.

For a high-level documentation of the Model Registry logical model, please check this guide.

The model registry core is the layer which implements the core/business logic by interacting with the underlying ml-metadata server. It provides a model registry domain-specific api that is in charge to proxy all, appropriately transformed, requests to ml-metadata using gRPC calls.

For more background on Model Registry Go core library and instructions on using it, please check getting started guide.

Run the following command to build the server binary:

make build

The generated binary uses spf13 cmdline args. More information on using the server can be obtained by running the command:

./model-registry --help

Run the following command to clean the server binary, generated models and etc.:

make clean

Run the following command to trigger all tests:

make test

or, to see the statement coverage:

make test-cover

The following command builds a docker image for the server with the tag model-registry:

docker build -t model-registry .

Note that the first build will be longer as it downloads the build tool dependencies. Subsequent builds will re-use the cached tools layer.

NOTE: ml-metadata server must be running and accessible, see more info on how to start the gRPC server in the official ml-metadata documentation.

The following command starts the proxy server:

docker run -d -p <hostname>:<port>:8080 --user <uid>:<gid> --name server model-registry proxy -n 0.0.0.0

Where, <uid>, <gid>, and <host-path> are the same as in the migrate command above. And <hostname> and <port> are the local ip and port to use to expose the container's default 8080 listening port. The server listens on localhost by default, hence the -n 0.0.0.0 option allows the server port to be exposed.

NOTE: Docker compose must be installed in your environment.

There are two docker-compose files that make the startup of both model registry and ml-metadara easier, by simply running:

docker compose -f docker-compose[-local].yaml up

The main difference between the two docker compose files is that -local one build the model registry from source, the other one, instead, download the latest pushed quay.io image.

When shutting down the docker compose, you might want to clean-up the SQLite db file generated by ML Metadata, for example ./test/config/ml-metadata/metadata.sqlite.db

The following diagram illustrate testing strategy for the several components in Model Registry project:

Go layers components are tested with Unit Tests written in Go, as well as Integration Tests leveraging Testcontainers. This allows to verify the expected "Core layer" of logical data mapping developed implemented in Go, matches technical expectations.

Python client is also tested with Unit Tests and Integration Tests written in Python.

End-to-end testing is developed with Robot Framework; this higher-lever layer of testing is used to:

• demonstrate User Stories from high level perspective
• demonstrate coherent logical data mapping by performing the same high level capabilities, using REST API flow Vs Python client flow, directly checking the end results in the backend gRPC MLMD server.