Comparison
HPOBench vs awesome-automl-papers
Verdict
Pick HPOBench if hPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios; pick awesome-automl-papers if awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search.
Markdown twin · HPOBench alternatives · awesome-automl-papers alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | HPOBench | awesome-automl-papers |
|---|---|---|
| Maintenance | Dormant (439d since push) As of 3w · github_public_v1 | Dormant (784d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 3w · 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-papers
- A curated list of automated machine learning papers and resources.
Stars
- HPOBench
- 170
- awesome-automl-papers
- 4.2k
Forks
- HPOBench
- 36
- awesome-automl-papers
- 678
Open issues
- HPOBench
- 34
- awesome-automl-papers
- 2
Language
- HPOBench
- Python
- awesome-automl-papers
- -
Adopt for
- HPOBench
- HPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios.
- awesome-automl-papers
- awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search.
Persona
- HPOBench
- -
- awesome-automl-papers
- -
Runtime
- HPOBench
- -
- awesome-automl-papers
- -
License
- HPOBench
- HPOBench is open source under the Apache-2.0 license.
- awesome-automl-papers
- Apache-2.0
Last pushed
- HPOBench
- May 21, 2025
- awesome-automl-papers
- Jun 11, 2024
Categories
- HPOBench
- Model Training
- awesome-automl-papers
- Evaluation & Observability, Model Training
Trust and health
Days since push
- HPOBench
- 439d
- awesome-automl-papers
- 784d
Open issues (now)
- HPOBench
- 34
- awesome-automl-papers
- 2
Owner type
- HPOBench
- Organization
- awesome-automl-papers
- User
OSV dependency advisories
- HPOBench
- Published findings
- awesome-automl-papers
- No lockfile (source not queried)
Full report
- HPOBench
- Trust report
- awesome-automl-papers
- Trust report
Choose HPOBench if…
- 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-papers if…
- Tags unique to awesome-automl-papers: feature-engineering, neural-architecture-search.
- Also covers Evaluation & Observability.
- When you need a curated list of academic materials to research or learn about AutoML technologies
When NOT to use awesome-automl-papers
- If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources
- When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers
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 (hibayesian/awesome-automl-papers) · observed Aug 4, 2026
- GitHub forks (hibayesian/awesome-automl-papers) · observed Aug 4, 2026
- Last push (hibayesian/awesome-automl-papers) · observed Jun 11, 2024
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: HPOBench 170 · awesome-automl-papers 4.2k (synced Aug 4, 2026).
Common questions
- What is the difference between HPOBench and awesome-automl-papers?
- HPOBench: A collection of hyperparameter optimization benchmark problems. awesome-automl-papers: A curated list of automated machine learning papers and resources.. See the comparison table for live GitHub stats and shared categories.
- When should I choose HPOBench over awesome-automl-papers?
- Choose HPOBench over awesome-automl-papers when 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-papers over HPOBench?
- Choose awesome-automl-papers over HPOBench when Tags unique to awesome-automl-papers: feature-engineering, neural-architecture-search; Also covers Evaluation & Observability; When you need a curated list of academic materials to research or learn about AutoML technologies.
- 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-papers?
- If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers
- Is HPOBench or awesome-automl-papers more popular on GitHub?
- awesome-automl-papers has more GitHub stars (4,155 vs 170). Stars measure visibility, not whether either tool fits your constraints.
- Are HPOBench and awesome-automl-papers open source?
- Yes - both are open-source projects on GitHub (HPOBench: Apache-2.0, awesome-automl-papers: Apache-2.0).
- Where can I find alternatives to HPOBench or awesome-automl-papers?
- GraphCanon lists graph-backed alternatives at HPOBench alternatives and awesome-automl-papers alternatives (HPOBench markdown twin, awesome-automl-papers 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-papers?
- HPOBench: Dormant. awesome-automl-papers: 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 HPOBench and awesome-automl-papers?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: HPOBench trust report; awesome-automl-papers trust report.