Comparison
auto-sklearn vs nni
Verdict
Pick auto-sklearn if auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows; pick nni if nNI is an AutoML toolkit that supports feature engineering, neural architecture search, model compression, and hyperparameter tuning with the flexibility of Python programming.
Markdown twin · auto-sklearn alternatives · nni alternatives
GraphCanon updated Oct 2, 2026
13views this month
Trust & integrity
| Signal | auto-sklearn | nni |
|---|---|---|
| Maintenance | Active (14d since push) As of Sep 30, 2026 · github_public_v1 | Archived (820d since push) As of Oct 2, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 2, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- auto-sklearn
- Automated Machine Learning with scikit-learn
- nni
- An open source AutoML toolkit for automating machine learning lifecycle
Stars
- auto-sklearn
- 8.1k
- nni
- 14k
Forks
- auto-sklearn
- 1.3k
- nni
- 1.9k
Open issues
- auto-sklearn
- 209
- nni
- 415
Language
- auto-sklearn
- Python
- nni
- Python
Adopt for
- auto-sklearn
- auto-sklearn is an automated machine learning toolkit designed to automate hyperparameter optimization and function seamlessly with scikit-learn workflows.
- nni
- NNI is an AutoML toolkit that supports feature engineering, neural architecture search, model compression, and hyperparameter tuning with the flexibility of Python programming.
Persona
- auto-sklearn
- -
- nni
- -
Runtime
- auto-sklearn
- -
- nni
- -
License
- auto-sklearn
- BSD-3-Clause
- nni
- MIT
Last pushed
- auto-sklearn
- Sep 15, 2026
- nni
- Jul 3, 2024
Categories
- auto-sklearn
- Model Training
- nni
- Model Training
Trust and health
Maintenance
- auto-sklearn
- Active (82%)
- nni
- Archived (8%)
Days since push
- auto-sklearn
- 14d
- nni
- 820d
Archived on GitHub
- auto-sklearn
- No
- nni
- Yes
Open issues (now)
- auto-sklearn
- 209
- nni
- 415
Stars delta
- auto-sklearn
- +7 (30d)
- nni
- +3 (30d)
OSV dependency advisories
- auto-sklearn
- Published findings
- nni
- No lockfile (source not queried)
Full report
- auto-sklearn
- Trust report
- nni
- Trust report
Shared compatibility
- Python · auto-sklearn: Python runtime · nni: Python runtime
Choose auto-sklearn if…
- License: auto-sklearn is BSD-3-Clause, nni is MIT.
- Tags unique to auto-sklearn: hyperparameter-optimization, hyperparameter-search, hyperparameter-tuning, meta-learning.
- When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically.
When NOT to use auto-sklearn
- If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers.
- In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library.
Choose nni if…
- License: nni is MIT, auto-sklearn is BSD-3-Clause.
- Tags unique to nni: data-science, deep-learning, deep-neural-network, distributed.
- You need to automate extensive parts of your machine learning lifecycle from preprocessing to deployment.
When NOT to use nni
- You require real-time automated tuning capabilities, as NNI focuses on batch processing and model training scenarios.
- If your project demands direct integration with specific deep learning frameworks beyond PyTorch and TensorFlow, NNI support is limited to these two environments.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (automl/auto-sklearn) · observed Sep 30, 2026
- GitHub forks (automl/auto-sklearn) · observed Sep 30, 2026
- Last push (automl/auto-sklearn) · observed Sep 15, 2026
- License file (BSD-3-Clause) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (microsoft/nni) · observed Oct 2, 2026
- GitHub forks (microsoft/nni) · observed Oct 2, 2026
- Last push (microsoft/nni) · observed Jul 3, 2024
- License file (MIT) · observed Oct 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: auto-sklearn 8.1k · nni 14k (synced Sep 30, 2026).
Common questions
- What is the difference between auto-sklearn and nni?
- auto-sklearn: Automated Machine Learning with scikit-learn. nni: An open source AutoML toolkit for automating machine learning lifecycle. See the comparison table for live GitHub stats and shared categories.
- When should I choose auto-sklearn over nni?
- Choose auto-sklearn over nni when License: auto-sklearn is BSD-3-Clause, nni is MIT; Tags unique to auto-sklearn: hyperparameter-optimization, hyperparameter-search, hyperparameter-tuning, meta-learning; When you need a drop-in replacement estimator for your existing scikit-learn pipeline that can handle the complexity of hyperparameter tuning automatically.
- When should I choose nni over auto-sklearn?
- Choose nni over auto-sklearn when License: nni is MIT, auto-sklearn is BSD-3-Clause; Tags unique to nni: data-science, deep-learning, deep-neural-network, distributed; You need to automate extensive parts of your machine learning lifecycle from preprocessing to deployment.
- When should I avoid auto-sklearn?
- If extensive customization or control over individual machine learning components is required beyond what auto-sklearn's automation offers. In cases requiring non-scikit-learn model ensembles, as the toolkit primarily supports models that are part of the scikit-earn library.
- When should I avoid nni?
- You require real-time automated tuning capabilities, as NNI focuses on batch processing and model training scenarios. If your project demands direct integration with specific deep learning frameworks beyond PyTorch and TensorFlow, NNI support is limited to these two environments.
- Is auto-sklearn or nni more popular on GitHub?
- nni has more GitHub stars (14,366 vs 8,134). Stars measure visibility, not whether either tool fits your constraints.
- Are auto-sklearn and nni open source?
- Yes - both are open-source projects on GitHub (auto-sklearn: BSD-3-Clause, nni: MIT).
- Where can I find alternatives to auto-sklearn or nni?
- GraphCanon lists graph-backed alternatives at auto-sklearn alternatives and nni alternatives (auto-sklearn markdown twin, nni markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, auto-sklearn or nni?
- auto-sklearn: Active. nni: Archived. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for auto-sklearn and nni?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: auto-sklearn trust report; nni trust report.