---
id: mlcroissant
version: "1.1.0"
license: unclear
license_treatment: unclear
maintenance: active
---

# mlcroissant — MLCommons datasets format.

License: unclear · Maintenance: active · Downloads: 103.2K/mo

## What it is and what it does

mlcroissant is a Python library for working with datasets described in the MLCommons Croissant format, a JSON-LD standard for machine learning dataset metadata. It provides two main analysis layers: a static layer that validates Croissant files and converts them into a structure graph (using NetworkX), catching schema violations and logic errors; and a dynamic layer that loads actual dataset examples by converting the structure into an operation graph that executes transformations like downloads and extractions.

The library is designed for teams managing ML datasets at scale. It includes a command-line interface for validation and loading, a Python API for programmatic metadata construction, and support for authentication (git+https and HTTP Basic Auth) when accessing remote distributions. Downloaded files are cached locally by default, and the package depends on common data-science libraries (pandas, scipy, requests, rdflib) plus utilities for graph processing and JSON path queries.

Use it for:

- Validate Croissant metadata files for correctness before publishing or using datasets in ML pipelines.
- Load and iterate over dataset examples defined in Croissant format without manually parsing JSON-LD.
- Programmatically generate Croissant metadata files for new datasets using the Python dataclass API.
- Extend the Croissant standard by defining custom RDF properties on dataset nodes.
- Integrate dataset loading into ML workflows that require standardized, auditable dataset descriptions.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Validates and loads datasets described in the MLCommons Croissant JSON-LD format, converting metadata into Python representations for static and dynamic analysis.

Yes, if you work with MLCommons Croissant datasets or need to standardize dataset metadata in your ML pipeline. The package is actively maintained, has low install friction, and provides both validation and loading capabilities. The unclear license status and lack of explicit Python version constraints in the package metadata are minor concerns but do not block use; verify the license and Python 3.10 requirement for your environment before deploying.

## Install

pip install mlcroissant
uv add mlcroissant
poetry add mlcroissant

## Installing mlcroissant

Before you install: Low install friction with a pure-Python wheel, though the description notes optional system dependencies (graphviz, libgraphviz-dev) may be needed depending on the environment. Maintenance status is active with a recent release.

Quickstart:

pip install mlcroissant

import mlcroissant as mlc

# Validate a Croissant metadata file
metadata = mlc.nodes.Metadata(name="my_dataset")
print(metadata.to_json())

# Or use the CLI: mlcroissant validate --jsonld metadata.json

Python >= 3.10 required; optional system dependencies (graphviz, libgraphviz-dev) may be needed on some systems; git+https loading requires CROISSANT_GIT_USERNAME and CROISSANT_GIT_PASSWORD environment variables.

Verify before relying:

- Whether the package requires Python 3.10 as a hard minimum or if earlier versions are supported despite the description stating >= 3.10
- Whether all 11 runtime dependencies are always required or if some are optional (e.g., for dev/test extras)
- Current state of the GitHub repository (stars, last commit, archived status) to confirm active maintenance beyond the release date

## Package facts

- License: not declared (unclear)
- Python support: unspecified
- Install friction: low
- Maintenance: active
- Downloads: 103.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

croissant dataset format, mlcommons dataset validation, json-ld dataset loader, machine learning dataset metadata, dataset format parser, croissant metadata validation, structured dataset loading, dataset-metadata, ml-commons, json-ld

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