Comparison
auto-sklearn vs Hypernets
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 Hypernets if hypernets is an AutoML framework supporting multiple ML frameworks for end-to-end AutoML solutions in specific domains.
Markdown twin · auto-sklearn alternatives · Hypernets alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | auto-sklearn | Hypernets |
|---|---|---|
| Maintenance | Steady (35d since push) As of 3w · github_public_v1 | Slowing (106d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | Published findings As of 1mo · 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
- Hypernets
- A General Automated Machine Learning framework for building domain-specific AutoML toolkits.
Stars
- auto-sklearn
- 8.1k
- Hypernets
- 265
Forks
- auto-sklearn
- 1.3k
- Hypernets
- 39
Open issues
- auto-sklearn
- 209
- Hypernets
- 0
Language
- auto-sklearn
- Python
- Hypernets
- 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.
- Hypernets
- Hypernets is an AutoML framework supporting multiple ML frameworks for end-to-end AutoML solutions in specific domains.
Persona
- auto-sklearn
- -
- Hypernets
- -
Runtime
- auto-sklearn
- -
- Hypernets
- -
License
- auto-sklearn
- BSD-3-Clause
- Hypernets
- Licensed under the Apache-2.0 license, allowing free use and distribution as long as copyright and licensing notices are preserved.
Last pushed
- auto-sklearn
- Jun 29, 2026
- Hypernets
- Apr 20, 2026
Categories
- auto-sklearn
- Model Training
- Hypernets
- Developer Tools, Model Training
Trust and health
Maintenance
- auto-sklearn
- Steady (60%)
- Hypernets
- Slowing (36%)
Days since push
- auto-sklearn
- 35d
- Hypernets
- 106d
Open issues (now)
- auto-sklearn
- 209
- Hypernets
- 0
Full report
- auto-sklearn
- Trust report
- Hypernets
- Trust report
Shared compatibility
- Python · auto-sklearn: Python runtime · Hypernets: Python runtime
Choose auto-sklearn if…
- License: auto-sklearn is BSD-3-Clause, Hypernets is Apache-2.0.
- Tags unique to auto-sklearn: automated-machine-learning, bayesian-optimization, hyperparameter-search, hyperparameter-tuning.
- auto-sklearn ships Docker support for self-hosted deployment.
- 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 Hypernets if…
- License: Hypernets is Apache-2.0, auto-sklearn is BSD-3-Clause.
- Tags unique to Hypernets: keras, lightgbm, neural-architecture-search, pytorch.
- Also covers Developer Tools.
- If your project requires integration with TensorFlow, Keras, PyTorch, Scikit-Learn, LightGBM or XGBoost within a single AutoML pipeline
When NOT to use Hypernets
- If the project is limited to only traditional machine learning libraries without deep-learning needs, consider more specialized tools with narrower focus
- Avoid if your team has strict time constraints; Hypernets' setup for domain-specific AutoML might require initial investment in understanding its abstraction layer
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 Aug 4, 2026
- GitHub forks (automl/auto-sklearn) · observed Aug 4, 2026
- Last push (automl/auto-sklearn) · observed Jun 29, 2026
- License file (BSD-3-Clause) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (DataCanvasIO/Hypernets) · observed Aug 4, 2026
- GitHub forks (DataCanvasIO/Hypernets) · observed Aug 4, 2026
- Last push (DataCanvasIO/Hypernets) · observed Apr 20, 2026
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: auto-sklearn 8.1k · Hypernets 265 (synced Aug 4, 2026).
Common questions
- What is the difference between auto-sklearn and Hypernets?
- auto-sklearn: Automated Machine Learning with scikit-learn. Hypernets: A General Automated Machine Learning framework for building domain-specific AutoML toolkits.. See the comparison table for live GitHub stats and shared categories.
- When should I choose auto-sklearn over Hypernets?
- Choose auto-sklearn over Hypernets when License: auto-sklearn is BSD-3-Clause, Hypernets is Apache-2.0; Tags unique to auto-sklearn: automated-machine-learning, bayesian-optimization, hyperparameter-search, hyperparameter-tuning; auto-sklearn ships Docker support for self-hosted deployment; 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 Hypernets over auto-sklearn?
- Choose Hypernets over auto-sklearn when License: Hypernets is Apache-2.0, auto-sklearn is BSD-3-Clause; Tags unique to Hypernets: keras, lightgbm, neural-architecture-search, pytorch; Also covers Developer Tools; If your project requires integration with TensorFlow, Keras, PyTorch, Scikit-Learn, LightGBM or XGBoost within a single AutoML pipeline.
- 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 Hypernets?
- If the project is limited to only traditional machine learning libraries without deep-learning needs, consider more specialized tools with narrower focus Avoid if your team has strict time constraints; Hypernets' setup for domain-specific AutoML might require initial investment in understanding its abstraction layer
- Is auto-sklearn or Hypernets more popular on GitHub?
- auto-sklearn has more GitHub stars (8,127 vs 265). Stars measure visibility, not whether either tool fits your constraints.
- Are auto-sklearn and Hypernets open source?
- Yes - both are open-source projects on GitHub (auto-sklearn: BSD-3-Clause, Hypernets: Apache-2.0).
- Where can I find alternatives to auto-sklearn or Hypernets?
- GraphCanon lists graph-backed alternatives at auto-sklearn alternatives and Hypernets alternatives (auto-sklearn markdown twin, Hypernets 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 Hypernets?
- auto-sklearn: Steady. Hypernets: Slowing. 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 Hypernets?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: auto-sklearn trust report; Hypernets trust report.