Comparison
Awesome-AutoDL vs hypertunity
Verdict
Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick hypertunity if hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization.
Markdown twin · Awesome-AutoDL alternatives · hypertunity alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-AutoDL | hypertunity |
|---|---|---|
| Maintenance | Dormant (1408d since push) As of 2w · github_public_v1 | Dormant (2381d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal 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
- Awesome-AutoDL
- Curated list of automated deep learning resources covering AutoDL, NAS, HPO
- hypertunity
- A toolset for black-box hyperparameter optimisation
Stars
- Awesome-AutoDL
- 2.3k
- hypertunity
- 137
Forks
- Awesome-AutoDL
- 319
- hypertunity
- 10
Open issues
- Awesome-AutoDL
- 2
- hypertunity
- 0
Language
- Awesome-AutoDL
- Python
- hypertunity
- Python
Adopt for
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
- hypertunity
- hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization.
Persona
- Awesome-AutoDL
- -
- hypertunity
- -
Runtime
- Awesome-AutoDL
- -
- hypertunity
- -
License
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
- hypertunity
- Apache-2.0
Last pushed
- Awesome-AutoDL
- Sep 26, 2022
- hypertunity
- Jan 26, 2020
Categories
- Awesome-AutoDL
- Developer Tools, Model Training
- hypertunity
- Model Training
Trust and health
Days since push
- Awesome-AutoDL
- 1408d
- hypertunity
- 2381d
Open issues (now)
- Awesome-AutoDL
- 2
- hypertunity
- 0
Full report
- Awesome-AutoDL
- Trust report
- hypertunity
- Trust report
Choose Awesome-AutoDL if…
- License: Awesome-AutoDL is MIT, hypertunity is Apache-2.0.
- Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning.
- 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.
Choose hypertunity if…
- License: hypertunity is Apache-2.0, Awesome-AutoDL is MIT.
- Requirements: Min 2 GB RAM; Support for SLURM is indicated in the topics, useful for HPC cluster management but not a hard requirement..
- Tags unique to hypertunity: bayesian-optimization, gpyopt, hyperparameter-optimization, slurm.
- When you are working with complex objective functions that are expensive to evaluate, and you need an automated way to optimize your model parameters.
When NOT to use hypertunity
- When the objective function evaluation is inexpensive or fast because hypertunity shines in scenarios where evaluations are costly, offering less benefit if evaluations can be easily repeated.
- If your project does not require advanced techniques such as Bayesian Optimization and you seek a simpler method with fewer dependencies.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (gdikov/hypertunity) · observed Aug 4, 2026
- GitHub forks (gdikov/hypertunity) · observed Aug 4, 2026
- Last push (gdikov/hypertunity) · observed Jan 26, 2020
- 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: Awesome-AutoDL 2.3k · hypertunity 137 (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-AutoDL and hypertunity?
- Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. hypertunity: A toolset for black-box hyperparameter optimisation. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-AutoDL over hypertunity?
- Choose Awesome-AutoDL over hypertunity when License: Awesome-AutoDL is MIT, hypertunity is Apache-2.0; Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning; 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 choose hypertunity over Awesome-AutoDL?
- Choose hypertunity over Awesome-AutoDL when License: hypertunity is Apache-2.0, Awesome-AutoDL is MIT; Requirements: Min 2 GB RAM; Support for SLURM is indicated in the topics, useful for HPC cluster management but not a hard requirement.; Tags unique to hypertunity: bayesian-optimization, gpyopt, hyperparameter-optimization, slurm; When you are working with complex objective functions that are expensive to evaluate, and you need an automated way to optimize your model parameters.
- 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.
- When should I avoid hypertunity?
- When the objective function evaluation is inexpensive or fast because hypertunity shines in scenarios where evaluations are costly, offering less benefit if evaluations can be easily repeated. If your project does not require advanced techniques such as Bayesian Optimization and you seek a simpler method with fewer dependencies.
- Is Awesome-AutoDL or hypertunity more popular on GitHub?
- Awesome-AutoDL has more GitHub stars (2,339 vs 137). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-AutoDL and hypertunity open source?
- Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, hypertunity: Apache-2.0).
- Where can I find alternatives to Awesome-AutoDL or hypertunity?
- GraphCanon lists graph-backed alternatives at Awesome-AutoDL alternatives and hypertunity alternatives (Awesome-AutoDL markdown twin, hypertunity 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, Awesome-AutoDL or hypertunity?
- Awesome-AutoDL: Dormant. hypertunity: 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 Awesome-AutoDL and hypertunity?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-AutoDL trust report; hypertunity trust report.