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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