model-registry 0.2.3a1

Client for Kubeflow Model Registry

Model Registry Python Client

Documentation

This library provides a high level interface for interacting with a model registry server.

Basic usage

from model_registry import ModelRegistry

registry = ModelRegistry("https://server-address", author="Ada Lovelace")  # Defaults to a secure connection via port 443

# registry = ModelRegistry("http://server-address", 1234, author="Ada Lovelace", is_secure=False)  # To use MR without TLS

model = registry.register_model(
    "my-model",  # model name
    "https://storage-place.my-company.com",  # model URI
    version="2.0.0",
    description="lorem ipsum",
    model_format_name="onnx",
    model_format_version="1",
    storage_key="my-data-connection",
    storage_path="path/to/model",
    metadata={
        # can be one of the following types
        "int_key": 1,
        "bool_key": False,
        "float_key": 3.14,
        "str_key": "str_value",
    }
)

model = registry.get_registered_model("my-model")

version = registry.get_model_version("my-model", "2.0.0")

experiment = registry.get_model_artifact("my-model", "2.0.0")

Importing from S3

When registering models stored on S3-compatible object storage, you should use utils.s3_uri_from to build an unambiguous URI for your artifact.

from model_registry import ModelRegistry, utils

registry = ModelRegistry(server_address="server-address", port=9090, author="author")

model = registry.register_model(
    "my-model",  # model name
    uri=utils.s3_uri_from("path/to/model", "my-bucket"),
    version="2.0.0",
    description="lorem ipsum",
    model_format_name="onnx",
    model_format_version="1",
    storage_key="my-data-connection",
    metadata={
        # can be one of the following types
        "int_key": 1,
        "bool_key": False,
        "float_key": 3.14,
        "str_key": "str_value",
    }
)

Importing from Hugging Face Hub

To import models from Hugging Face Hub, start by installing the huggingface-hub package, either directly or as an extra (available as model-registry[hf]). Models can be imported with

hf_model = registry.register_hf_model(
    "hf-namespace/hf-model",  # HF repo
    "relative/path/to/model/file.onnx",
    version="1.2.3",
    model_name="my-model",
    description="lorem ipsum",
    model_format_name="onnx",
    model_format_version="1",
)

There are caveats to be noted when using this method:

• It's only possible to import a single model file per Hugging Face Hub repo right now.

• If the model you want to import is in a global namespace, you should provide an author, e.g.

  hf_model = registry.register_hf_model(
      "gpt2",  # this model implicitly has no author
      "onnx/decoder_model.onnx",
      author="OpenAI",  # Defaults to unknown in the absence of an author
      version="1.0.0",
      description="gpt-2 model",
      model_format_name="onnx",
      model_format_version="1",
  )
  

Development

Common tasks, such as building documentation and running tests, can be executed using nox sessions.

Use nox -l to list sessions and execute them using nox -s [session].

Alternatively, use make install to setup a local Python virtual environment with poetry.

To run the tests you will need docker (or equivalent) and the compose extension command. This is necessary as the test suite will manage a Model Registry server and an MLMD instance to ensure a clean state on each run. You can use make test to execute pytest.

Running Locally on Mac M1 or M2 (arm64 architecture)

Check out our recommendations on setting up your docker engine on an ARM processor.