Comparison
HPOBench vs Awesome-AutoDL
Verdict
Pick HPOBench if hPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios; pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
Markdown twin · HPOBench alternatives · Awesome-AutoDL alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | HPOBench | Awesome-AutoDL |
|---|---|---|
| Maintenance | Dormant (439d 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 | 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-AutoDL
- Curated list of automated deep learning resources covering AutoDL, NAS, HPO
Stars
- HPOBench
- 170
- Awesome-AutoDL
- 2.3k
Forks
- HPOBench
- 36
- Awesome-AutoDL
- 319
Open issues
- HPOBench
- 34
- Awesome-AutoDL
- 2
Language
- HPOBench
- Python
- Awesome-AutoDL
- Python
Adopt for
- HPOBench
- HPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios.
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
Persona
- HPOBench
- -
- Awesome-AutoDL
- -
Runtime
- HPOBench
- -
- Awesome-AutoDL
- -
License
- HPOBench
- HPOBench is open source under the Apache-2.0 license.
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
Last pushed
- HPOBench
- May 21, 2025
- Awesome-AutoDL
- Sep 26, 2022
Categories
- HPOBench
- Model Training
- Awesome-AutoDL
- Developer Tools, Model Training
Trust and health
Days since push
- HPOBench
- 439d
- Awesome-AutoDL
- 1408d
Open issues (now)
- HPOBench
- 34
- Awesome-AutoDL
- 2
Owner type
- HPOBench
- Organization
- Awesome-AutoDL
- User
OSV dependency advisories
- HPOBench
- Published findings
- Awesome-AutoDL
- No lockfile (source not queried)
Full report
- HPOBench
- Trust report
- Awesome-AutoDL
- Trust report
Choose HPOBench if…
- License: HPOBench is Apache-2.0, Awesome-AutoDL is MIT.
- 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, hyperparameter-optimization, 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-AutoDL if…
- License: Awesome-AutoDL is MIT, HPOBench is Apache-2.0.
- 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/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 (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: HPOBench 170 · Awesome-AutoDL 2.3k (synced Aug 4, 2026).
Common questions
- What is the difference between HPOBench and Awesome-AutoDL?
- HPOBench: A collection of hyperparameter optimization benchmark problems. 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 HPOBench over Awesome-AutoDL?
- Choose HPOBench over Awesome-AutoDL when License: HPOBench is Apache-2.0, Awesome-AutoDL is MIT; 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, hyperparameter-optimization, 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-AutoDL over HPOBench?
- Choose Awesome-AutoDL over HPOBench when License: Awesome-AutoDL is MIT, HPOBench is Apache-2.0; 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 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-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 HPOBench or Awesome-AutoDL more popular on GitHub?
- Awesome-AutoDL has more GitHub stars (2,339 vs 170). Stars measure visibility, not whether either tool fits your constraints.
- Are HPOBench and Awesome-AutoDL open source?
- Yes - both are open-source projects on GitHub (HPOBench: Apache-2.0, Awesome-AutoDL: MIT).
- Where can I find alternatives to HPOBench or Awesome-AutoDL?
- GraphCanon lists graph-backed alternatives at HPOBench alternatives and Awesome-AutoDL alternatives (HPOBench 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, HPOBench or Awesome-AutoDL?
- HPOBench: 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 HPOBench and Awesome-AutoDL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: HPOBench trust report; Awesome-AutoDL trust report.