openml
Python API for OpenML
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has low install friction, carries a permissive BSD license, and solves a real problem for anyone doing collaborative or reproducible ML research. It integrates cleanly with the scientific Python stack (scikit-learn, pandas, numpy) and has no known vulnerabilities. Install it if you work with OpenML datasets or benchmarks; skip it if you manage datasets locally or use a different platform.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later.
- Network access to openml.org is needed to download datasets and tasks.
- Low friction installation with a pure-Python wheel.
License · maintenance · safety
permissive license (permissive) — BSD 3-Clause License (permissive). You may use, modify, and distribute the package freely in commercial and private projects provided you retain the copyright notice and disclaimer.
last release 2025-01-25 (566 days) · last repo commit 2026-08-10 · 353 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 97,049 downloads/mo, #13,176 on PyPI
Alternatives
Verify before relying
pip install openml
import openml
# Download a dataset
dataset = openml.datasets.get_dataset("credit-g")
X, y, categorical_indicator, attribute_names = dataset.get_data(target="class")
# Or retrieve a task
task = openml.tasks.get_task(31)
train_indices, test_indices = task.get_train_test_split_indices(fold=0)- Whether the package handles authentication or API keys for private datasets or uploads.
- Performance characteristics when working with very large datasets or many concurrent requests.
- Offline mode or caching strategy for repeated access to the same datasets.
What it is and what it does
OpenML-Python is a client library that connects your Python environment to OpenML, an online platform for sharing and benchmarking machine learning datasets and experiments. It lets you programmatically fetch datasets, retrieve pre-defined machine learning tasks with standardized train-test splits, and access curated benchmarking suites—all without manually downloading files or managing data locally. The library wraps OpenML's REST API and integrates with pandas, scikit-learn, and numpy, so datasets come back in formats you already work with.
The package is designed for researchers and practitioners who want to run reproducible ML experiments on shared, versioned datasets, compare results across a community, or build on existing benchmarks. It handles the network communication, data parsing, and format conversion, so you focus on model development rather than data plumbing.
Use it for
- Download standardized datasets for machine learning experiments without manual file management.
- Retrieve pre-defined classification or regression tasks with consistent train-test splits for benchmarking.
- Access curated benchmarking suites to run your models against a community-agreed set of problems.
- Upload experiment results back to OpenML to contribute to collaborative research and compare performance.
- Build reproducible ML pipelines that reference datasets by ID, ensuring consistency across runs and teams.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has low install friction, carries a permissive BSD license, and solves a real problem for anyone doing collaborative or reproducible ML research. It integrates cleanly with the scientific Python stack (scikit-learn, pandas, numpy) and has no known vulnerabilities. Install it if you work with OpenML datasets or benchmarks; skip it if you manage datasets locally or use a different platform.
Install
openml on PyPI
Before you install
Low friction installation with a pure-Python wheel. Active maintenance with a recent commit on 2026-08-10 and steady development since first release in 2018. Depends on well-established packages like scikit-learn, pandas, and requests.
Requires Python 3.8 or later. Network access to openml.org is needed to download datasets and tasks.
License in practice
BSD 3-Clause License (permissive). You may use, modify, and distribute the package freely in commercial and private projects provided you retain the copyright notice and disclaimer.
Quickstart
pip install openml
import openml
# Download a dataset
dataset = openml.datasets.get_dataset("credit-g")
X, y, categorical_indicator, attribute_names = dataset.get_data(target="class")
# Or retrieve a task
task = openml.tasks.get_task(31)
train_indices, test_indices = task.get_train_test_split_indices(fold=0)Verify before relying
- Whether the package handles authentication or API keys for private datasets or uploads.
- Performance characteristics when working with very large datasets or many concurrent requests.
- Offline mode or caching strategy for repeated access to the same datasets.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 12 packagesliac-arffxmltodictrequestsscikit-learnpython-dateutilpandasscipynumpyminiopyarrowtqdmpackaging |
| Maintenance | Actively maintained 566 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 97,049 / month, #13,176 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: MacOSOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: openml-0.15.1-py3-none-any.whl
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See also azureml-core · azureml · ucimlrepo · osfclient · datazets · clearml · azureml-mlflow · ogb · azureml-defaults · clip-benchmark