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Python SDK for the ML Artifact Registry

Project description

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

Registering external data

exp = reg.get_experiment("baseline")
exp.register("TrainingData", path="s3://bucket/data/v3", description="External dataset")

# Jobs can now resolve it
with exp.start_job(name="train", git_sha="abc") as job:
    data = job.resolve("TrainingData")

W&B integration

with exp.start_job(name="train", git_sha="abc123") as job:
    job.inject_wandb_env()  # Sets WANDB_API_KEY, WANDB_PROJECT, WANDB_RUN_ID etc.
    # Tracking URL auto-set to https://wandb.ai/{entity}/{project}/runs/{job_id}

    # Frameworks like Axolotl that respect W&B env vars just work.
    # For manual usage:
    import wandb
    wandb.init()
    wandb.log({"loss": 0.5})

License

MIT

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