Comparison
HpBandSter vs awesome-AutoML
Verdict
Pick HpBandSter if hpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · HpBandSter alternatives · awesome-AutoML alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | HpBandSter | awesome-AutoML |
|---|---|---|
| Maintenance | Dormant (1387d since push) As of 3w · github_public_v1 | Slowing (133d 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 | No lockfile (source not queried) 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
- HpBandSter
- a distributed Hyperband implementation on Steroids
- awesome-AutoML
- Curating AutoML research and resources
Stars
- HpBandSter
- 632
- awesome-AutoML
- 941
Forks
- HpBandSter
- 107
- awesome-AutoML
- 156
Open issues
- HpBandSter
- 66
- awesome-AutoML
- 1
Language
- HpBandSter
- Python
- awesome-AutoML
- -
Adopt for
- HpBandSter
- HpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- HpBandSter
- -
- awesome-AutoML
- -
Runtime
- HpBandSter
- -
- awesome-AutoML
- -
License
- HpBandSter
- BSD-3-Clause License - Permits free use but requires preservation of copyright and license notices. Contributors retain the copyrights to their contributions.
- awesome-AutoML
- GPL-3.0
Last pushed
- HpBandSter
- Oct 16, 2022
- awesome-AutoML
- Mar 24, 2026
Categories
- HpBandSter
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- HpBandSter
- Dormant (18%)
- awesome-AutoML
- Slowing (36%)
Days since push
- HpBandSter
- 1387d
- awesome-AutoML
- 133d
Open issues (now)
- HpBandSter
- 66
- awesome-AutoML
- 1
Owner type
- HpBandSter
- Organization
- awesome-AutoML
- User
Full report
- HpBandSter
- Trust report
- awesome-AutoML
- Trust report
Choose HpBandSter if…
- License: HpBandSter is BSD-3-Clause, awesome-AutoML is GPL-3.0.
- Pricing: HpBandSter is open-source software under a permissive BSD-3-Clause License, allowing unrestricted usage for personal or commercial purposes without any direct costs..
- Requirements: Min 4 GB RAM; Requires Python environment. No Docker required..
- Tags unique to HpBandSter: automated-machine-learning, bayesian-optimization.
- HpBandSter is best used when conducting large-scale experiments on multiple machines that require efficient resource management across different environments.
When NOT to use HpBandSter
- If your project involves smaller datasets or less complex models where individual hyperparameter tuning can be done manually, HpBandSter might be an overkill due to its advanced distributed settings.
- Avoid using HpBandSter if you need a tool that heavily relies on Bayesian optimization techniques, as it specializes more in Hyperband methodology.
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, HpBandSter is BSD-3-Clause.
- Tags unique to awesome-AutoML: meta-learning.
- 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/HpBandSter) · observed Aug 4, 2026
- GitHub forks (automl/HpBandSter) · observed Aug 4, 2026
- Last push (automl/HpBandSter) · observed Oct 16, 2022
- 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 (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: HpBandSter 632 · awesome-AutoML 941 (synced Aug 4, 2026).
Common questions
- What is the difference between HpBandSter and awesome-AutoML?
- HpBandSter: a distributed Hyperband implementation on Steroids. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose HpBandSter over awesome-AutoML?
- Choose HpBandSter over awesome-AutoML when License: HpBandSter is BSD-3-Clause, awesome-AutoML is GPL-3.0; Pricing: HpBandSter is open-source software under a permissive BSD-3-Clause License, allowing unrestricted usage for personal or commercial purposes without any direct costs.; Requirements: Min 4 GB RAM; Requires Python environment. No Docker required.; Tags unique to HpBandSter: automated-machine-learning, bayesian-optimization; HpBandSter is best used when conducting large-scale experiments on multiple machines that require efficient resource management across different environments.
- When should I choose awesome-AutoML over HpBandSter?
- Choose awesome-AutoML over HpBandSter when License: awesome-AutoML is GPL-3.0, HpBandSter is BSD-3-Clause; Tags unique to awesome-AutoML: meta-learning; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- When should I avoid HpBandSter?
- If your project involves smaller datasets or less complex models where individual hyperparameter tuning can be done manually, HpBandSter might be an overkill due to its advanced distributed settings. Avoid using HpBandSter if you need a tool that heavily relies on Bayesian optimization techniques, as it specializes more in Hyperband methodology.
- 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 HpBandSter or awesome-AutoML more popular on GitHub?
- awesome-AutoML has more GitHub stars (941 vs 632). Stars measure visibility, not whether either tool fits your constraints.
- Are HpBandSter and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (HpBandSter: BSD-3-Clause, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to HpBandSter or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at HpBandSter alternatives and awesome-AutoML alternatives (HpBandSter 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, HpBandSter or awesome-AutoML?
- HpBandSter: 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 HpBandSter and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: HpBandSter trust report; awesome-AutoML trust report.