zvondb 0.1.0

Python SDK for the ML Artifact Registry

zvondb

Python SDK for the ML Artifact Registry — track artifacts, their lineage, and versions across ML experiments.

Installation

pip install zvondb

Quick start

from zvondb import Registry

with Registry(project="my-project") as reg:
    exp = reg.ensure_experiment("my-experiment", parent="baseline")

    with exp.start_job(name="train", git_sha="abc123") as job:
        # Resolve input artifacts (walks parent chain)
        data = job.resolve("TrainingData")
        print(f"Using {data.name} v{data.version} from {data.path}")

        # ... train your model ...

        # Register output artifact
        job.create(
            "TrainedModel",
            storage_name="s3",
            description="Model trained on latest data",
            metadata={"val_loss": 0.15},
            tags=["v1"],
        )
        job.complete()

Configuration

Environment variable	Constructor param	Default	Description
ZVONDB_URL	base_url	http://localhost:8000	Registry server URL
ZVONDB_API_KEY	api_key	""	API authentication token

Key concepts

• Registry — entry point; manages projects and experiments
• Experiment — a branch of work that can inherit artifacts from a parent experiment
• Job — a unit of work that consumes and produces artifacts
• Artifact — a versioned, named piece of data with metadata, tags, and lineage

MLflow integration

with exp.start_job(name="train", git_sha="abc123") as job:
    job.inject_mlflow_env()  # Sets MLFLOW_TRACKING_URI etc. in os.environ

    import mlflow
    with mlflow.start_run() as run:
        job.set_tracking_url(run.info.artifact_uri)
        # Use MLflow for metrics, zvondb for artifact lineage

License

MIT