Comparison
auto-sklearn vs HPOBench
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 HPOBench if hPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios.
Markdown twin · auto-sklearn alternatives · HPOBench alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | auto-sklearn | HPOBench |
|---|---|---|
| Maintenance | Steady (35d since push) As of 3w · github_public_v1 | Dormant (439d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 3w · 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
- HPOBench
- A collection of hyperparameter optimization benchmark problems
Stars
- auto-sklearn
- 8.1k
- HPOBench
- 170
Forks
- auto-sklearn
- 1.3k
- HPOBench
- 36
Open issues
- auto-sklearn
- 209
- HPOBench
- 34
Language
- auto-sklearn
- Python
- HPOBench
- 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.
- HPOBench
- HPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios.
Persona
- auto-sklearn
- -
- HPOBench
- -
Runtime
- auto-sklearn
- -
- HPOBench
- -
License
- auto-sklearn
- BSD-3-Clause
- HPOBench
- HPOBench is open source under the Apache-2.0 license.
Last pushed
- auto-sklearn
- Jun 29, 2026
- HPOBench
- May 21, 2025
Categories
- auto-sklearn
- Model Training
- HPOBench
- Model Training
Trust and health
Maintenance
- auto-sklearn
- Steady (60%)
- HPOBench
- Dormant (18%)
Days since push
- auto-sklearn
- 35d
- HPOBench
- 439d
Open issues (now)
- auto-sklearn
- 209
- HPOBench
- 34
Full report
- auto-sklearn
- Trust report
- HPOBench
- Trust report
Shared compatibility
- Python · auto-sklearn: Python runtime · HPOBench: Python runtime
Choose auto-sklearn if…
- License: auto-sklearn is BSD-3-Clause, HPOBench is Apache-2.0.
- Tags unique to auto-sklearn: automated-machine-learning, hyperparameter-search, hyperparameter-tuning, meta-learning.
- 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 HPOBench if…
- License: HPOBench is Apache-2.0, auto-sklearn is BSD-3-Clause.
- Requirements: The installation recommends, but does not strictly require singularity version 3.6, which can be an additional setup step..
- Tags unique to HPOBench: benchmark, python.
- When you are specifically interested in benchmarking hyperparameter optimization problems that include containerized benchmarks to ensure consistency across environments.
When NOT to use HPOBench
- Avoid HPOBench if your project does not require Python or you are looking for a platform that exclusively focuses on the automation of model selection without hyperparameter optimization.
- If you prefer tools with built-in support for multiple programming languages, rather than focusing solely on Python as is the case with HPOBench.
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 (automl/HPOBench) · observed Aug 4, 2026
- GitHub forks (automl/HPOBench) · observed Aug 4, 2026
- Last push (automl/HPOBench) · observed May 21, 2025
- 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 · HPOBench 170 (synced Aug 4, 2026).
Common questions
- What is the difference between auto-sklearn and HPOBench?
- auto-sklearn: Automated Machine Learning with scikit-learn. HPOBench: A collection of hyperparameter optimization benchmark problems. See the comparison table for live GitHub stats and shared categories.
- When should I choose auto-sklearn over HPOBench?
- Choose auto-sklearn over HPOBench when License: auto-sklearn is BSD-3-Clause, HPOBench is Apache-2.0; Tags unique to auto-sklearn: automated-machine-learning, hyperparameter-search, hyperparameter-tuning, meta-learning; 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 HPOBench over auto-sklearn?
- Choose HPOBench over auto-sklearn when License: HPOBench is Apache-2.0, auto-sklearn is BSD-3-Clause; Requirements: The installation recommends, but does not strictly require singularity version 3.6, which can be an additional setup step.; Tags unique to HPOBench: benchmark, python; When you are specifically interested in benchmarking hyperparameter optimization problems that include containerized benchmarks to ensure consistency across environments.
- 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 HPOBench?
- Avoid HPOBench if your project does not require Python or you are looking for a platform that exclusively focuses on the automation of model selection without hyperparameter optimization. If you prefer tools with built-in support for multiple programming languages, rather than focusing solely on Python as is the case with HPOBench.
- Is auto-sklearn or HPOBench more popular on GitHub?
- auto-sklearn has more GitHub stars (8,127 vs 170). Stars measure visibility, not whether either tool fits your constraints.
- Are auto-sklearn and HPOBench open source?
- Yes - both are open-source projects on GitHub (auto-sklearn: BSD-3-Clause, HPOBench: Apache-2.0).
- Where can I find alternatives to auto-sklearn or HPOBench?
- GraphCanon lists graph-backed alternatives at auto-sklearn alternatives and HPOBench alternatives (auto-sklearn markdown twin, HPOBench 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 HPOBench?
- auto-sklearn: Steady. HPOBench: Dormant. 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 HPOBench?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: auto-sklearn trust report; HPOBench trust report.