Comparison
HpBandSter vs Awesome-AutoDL
Verdict
Pick HpBandSter if hpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities; pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
Markdown twin · HpBandSter alternatives · Awesome-AutoDL alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | HpBandSter | Awesome-AutoDL |
|---|---|---|
| Maintenance | Dormant (1387d since push) As of 2w · github_public_v1 | Dormant (1408d 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 | 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-AutoDL
- Curated list of automated deep learning resources covering AutoDL, NAS, HPO
Stars
- HpBandSter
- 632
- Awesome-AutoDL
- 2.3k
Forks
- HpBandSter
- 107
- Awesome-AutoDL
- 319
Open issues
- HpBandSter
- 66
- Awesome-AutoDL
- 2
Language
- HpBandSter
- Python
- Awesome-AutoDL
- Python
Adopt for
- HpBandSter
- HpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities.
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
Persona
- HpBandSter
- -
- Awesome-AutoDL
- -
Runtime
- HpBandSter
- -
- Awesome-AutoDL
- -
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-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
Last pushed
- HpBandSter
- Oct 16, 2022
- Awesome-AutoDL
- Sep 26, 2022
Categories
- HpBandSter
- Model Training
- Awesome-AutoDL
- Developer Tools, Model Training
Trust and health
Days since push
- HpBandSter
- 1387d
- Awesome-AutoDL
- 1408d
Open issues (now)
- HpBandSter
- 66
- Awesome-AutoDL
- 2
Owner type
- HpBandSter
- Organization
- Awesome-AutoDL
- User
Full report
- HpBandSter
- Trust report
- Awesome-AutoDL
- Trust report
Choose HpBandSter if…
- License: HpBandSter is BSD-3-Clause, Awesome-AutoDL is MIT.
- 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 Awesome-AutoDL if…
- License: Awesome-AutoDL is MIT, HpBandSter is BSD-3-Clause.
- Tags unique to Awesome-AutoDL: autodl, awesome, deep-learning, hyper-parameter-optimization.
- Also covers Developer Tools.
- Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS).
When NOT to use Awesome-AutoDL
- Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned.
- Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide.
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 (D-X-Y/Awesome-AutoDL) · observed Aug 4, 2026
- GitHub forks (D-X-Y/Awesome-AutoDL) · observed Aug 4, 2026
- Last push (D-X-Y/Awesome-AutoDL) · observed Sep 26, 2022
- License file (MIT) · 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 · Awesome-AutoDL 2.3k (synced Aug 4, 2026).
Common questions
- What is the difference between HpBandSter and Awesome-AutoDL?
- HpBandSter: a distributed Hyperband implementation on Steroids. Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. See the comparison table for live GitHub stats and shared categories.
- When should I choose HpBandSter over Awesome-AutoDL?
- Choose HpBandSter over Awesome-AutoDL when License: HpBandSter is BSD-3-Clause, Awesome-AutoDL is MIT; 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 Awesome-AutoDL over HpBandSter?
- Choose Awesome-AutoDL over HpBandSter when License: Awesome-AutoDL is MIT, HpBandSter is BSD-3-Clause; Tags unique to Awesome-AutoDL: autodl, awesome, deep-learning, hyper-parameter-optimization; Also covers Developer Tools; Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS).
- 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-AutoDL?
- Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide.
- Is HpBandSter or Awesome-AutoDL more popular on GitHub?
- Awesome-AutoDL has more GitHub stars (2,339 vs 632). Stars measure visibility, not whether either tool fits your constraints.
- Are HpBandSter and Awesome-AutoDL open source?
- Yes - both are open-source projects on GitHub (HpBandSter: BSD-3-Clause, Awesome-AutoDL: MIT).
- Where can I find alternatives to HpBandSter or Awesome-AutoDL?
- GraphCanon lists graph-backed alternatives at HpBandSter alternatives and Awesome-AutoDL alternatives (HpBandSter markdown twin, Awesome-AutoDL 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-AutoDL?
- HpBandSter: Dormant. Awesome-AutoDL: 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 HpBandSter and Awesome-AutoDL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: HpBandSter trust report; Awesome-AutoDL trust report.