Comparison
HPOBench vs awesome-AutoML
Verdict
Pick HPOBench if hPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · HPOBench alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | HPOBench | awesome-AutoML |
|---|---|---|
| Maintenance | Dormant (439d since push) As of 2w · github_public_v1 | Slowing (133d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) 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
- HPOBench
- A collection of hyperparameter optimization benchmark problems
- awesome-AutoML
- Curating AutoML research and resources
Stars
- HPOBench
- 170
- awesome-AutoML
- 941
Forks
- HPOBench
- 36
- awesome-AutoML
- 156
Open issues
- HPOBench
- 34
- awesome-AutoML
- 1
Language
- HPOBench
- Python
- awesome-AutoML
- -
Adopt for
- HPOBench
- HPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- HPOBench
- -
- awesome-AutoML
- -
Runtime
- HPOBench
- -
- awesome-AutoML
- -
License
- HPOBench
- HPOBench is open source under the Apache-2.0 license.
- awesome-AutoML
- GPL-3.0
Last pushed
- HPOBench
- May 21, 2025
- awesome-AutoML
- Mar 24, 2026
Categories
- HPOBench
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- HPOBench
- Dormant (18%)
- awesome-AutoML
- Slowing (36%)
Days since push
- HPOBench
- 439d
- awesome-AutoML
- 133d
Open issues (now)
- HPOBench
- 34
- awesome-AutoML
- 1
Owner type
- HPOBench
- Organization
- awesome-AutoML
- User
OSV dependency advisories
- HPOBench
- Published findings
- awesome-AutoML
- No lockfile (source not queried)
Full report
- HPOBench
- Trust report
- awesome-AutoML
- Trust report
Choose HPOBench if…
- License: HPOBench is Apache-2.0, awesome-AutoML is GPL-3.0.
- Requirements: The installation recommends, but does not strictly require singularity version 3.6, which can be an additional setup step..
- Tags unique to HPOBench: bayesian-optimization, 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.
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, HPOBench is Apache-2.0.
- Tags unique to awesome-AutoML: meta-learning, neural-architecture-search.
- When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
When NOT to use awesome-AutoML
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides.
- When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (windmaple/awesome-AutoML) · observed Aug 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Aug 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: HPOBench 170 · awesome-AutoML 941 (synced Aug 4, 2026).
Common questions
- What is the difference between HPOBench and awesome-AutoML?
- HPOBench: A collection of hyperparameter optimization benchmark problems. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose HPOBench over awesome-AutoML?
- Choose HPOBench over awesome-AutoML when License: HPOBench is Apache-2.0, awesome-AutoML is GPL-3.0; Requirements: The installation recommends, but does not strictly require singularity version 3.6, which can be an additional setup step.; Tags unique to HPOBench: bayesian-optimization, benchmark, python; When you are specifically interested in benchmarking hyperparameter optimization problems that include containerized benchmarks to ensure consistency across environments.
- When should I choose awesome-AutoML over HPOBench?
- Choose awesome-AutoML over HPOBench when License: awesome-AutoML is GPL-3.0, HPOBench is Apache-2.0; Tags unique to awesome-AutoML: meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- 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.
- When should I avoid awesome-AutoML?
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
- Is HPOBench or awesome-AutoML more popular on GitHub?
- awesome-AutoML has more GitHub stars (941 vs 170). Stars measure visibility, not whether either tool fits your constraints.
- Are HPOBench and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (HPOBench: Apache-2.0, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to HPOBench or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at HPOBench alternatives and awesome-AutoML alternatives (HPOBench markdown twin, awesome-AutoML 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, HPOBench or awesome-AutoML?
- HPOBench: Dormant. awesome-AutoML: 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 HPOBench and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: HPOBench trust report; awesome-AutoML trust report.