##### Croissant [image: .md][image]

Croissant 🥐 is a high-level format building on schema.org for machine
learning datasets that combines metadata, resource file descriptions,
data structure, and default ML semantics into a single file. It works
with existing datasets to make them easier to find, use, and support
with tools.

Here, we demonstrate how LaminDB can be used to validate Croissant
files:

 # pip install lamindb
 !lamin init --storage ./test-lamin-croissant

 import lamindb as ln
 import json

 ln.track()

 croissant_path, dataset1_path = ln.examples.croissant.mini_immuno()
 croissant_path

 with open(croissant_path) as f:
 dictionary = json.load(f)

 print(json.dumps(dictionary, indent=2))

 dataset1_path

 artifact = ln.integrations.curate_from_croissant(croissant_path)

Project label, license, description, version tag, and file paths are
automatically extracted from the Croissant file. More metadata can be
supported in the future.

 artifact.describe()

 ln.finish()