Comparison
HpBandSter vs autokeras
Verdict
Pick HpBandSter if hpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities; pick autokeras if autoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+.
Markdown twin · HpBandSter alternatives · autokeras alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | HpBandSter | autokeras |
|---|---|---|
| Maintenance | Dormant (1387d since push) As of 2w · github_public_v1 | Slowing (251d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · 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
- autokeras
- AutoML library for deep learning
Stars
- HpBandSter
- 632
- autokeras
- 9.3k
Forks
- HpBandSter
- 107
- autokeras
- 1.4k
Open issues
- HpBandSter
- 66
- autokeras
- 161
Language
- HpBandSter
- Python
- autokeras
- Python
Adopt for
- HpBandSter
- HpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities.
- autokeras
- AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+.
Persona
- HpBandSter
- -
- autokeras
- -
Runtime
- HpBandSter
- -
- autokeras
- -
License
- HpBandSter
- BSD-3-Clause License - Permits free use but requires preservation of copyright and license notices. Contributors retain the copyrights to their contributions.
- autokeras
- Apache-2.0
Last pushed
- HpBandSter
- Oct 16, 2022
- autokeras
- Nov 25, 2025
Categories
- HpBandSter
- Model Training
- autokeras
- Developer Tools, Model Training
Trust and health
Maintenance
- HpBandSter
- Dormant (18%)
- autokeras
- Slowing (36%)
Days since push
- HpBandSter
- 1387d
- autokeras
- 251d
Open issues (now)
- HpBandSter
- 66
- autokeras
- 161
Full report
- HpBandSter
- Trust report
- autokeras
- Trust report
Shared compatibility
- Python · HpBandSter: Python runtime · autokeras: Python runtime
Choose HpBandSter if…
- License: HpBandSter is BSD-3-Clause, autokeras is Apache-2.0.
- 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, hyperparameter-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 autokeras if…
- License: autokeras is Apache-2.0, HpBandSter is BSD-3-Clause.
- Tags unique to autokeras: autodl, deep-learning, keras, machine-learning.
- Also covers Developer Tools.
- When your project involves deep learning tasks requiring minimal manual intervention in designing models.
When NOT to use autokeras
- When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible.
- If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.
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 (keras-team/autokeras) · observed Aug 4, 2026
- GitHub forks (keras-team/autokeras) · observed Aug 4, 2026
- Last push (keras-team/autokeras) · observed Nov 25, 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: HpBandSter 632 · autokeras 9.3k (synced Aug 4, 2026).
Common questions
- What is the difference between HpBandSter and autokeras?
- HpBandSter: a distributed Hyperband implementation on Steroids. autokeras: AutoML library for deep learning. See the comparison table for live GitHub stats and shared categories.
- When should I choose HpBandSter over autokeras?
- Choose HpBandSter over autokeras when License: HpBandSter is BSD-3-Clause, autokeras is Apache-2.0; 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, hyperparameter-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 autokeras over HpBandSter?
- Choose autokeras over HpBandSter when License: autokeras is Apache-2.0, HpBandSter is BSD-3-Clause; Tags unique to autokeras: autodl, deep-learning, keras, machine-learning; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models.
- 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 autokeras?
- When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.
- Is HpBandSter or autokeras more popular on GitHub?
- autokeras has more GitHub stars (9,328 vs 632). Stars measure visibility, not whether either tool fits your constraints.
- Are HpBandSter and autokeras open source?
- Yes - both are open-source projects on GitHub (HpBandSter: BSD-3-Clause, autokeras: Apache-2.0).
- Where can I find alternatives to HpBandSter or autokeras?
- GraphCanon lists graph-backed alternatives at HpBandSter alternatives and autokeras alternatives (HpBandSter markdown twin, autokeras 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 autokeras?
- HpBandSter: Dormant. autokeras: 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 autokeras?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: HpBandSter trust report; autokeras trust report.