Comparison
hypertunity vs archai
Verdict
Pick hypertunity if hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization; pick archai if archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch.
Markdown twin · hypertunity alternatives · archai alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | hypertunity | archai |
|---|---|---|
| Maintenance | Dormant (2381d since push) As of 3w · github_public_v1 | Slowing (252d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization 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
- hypertunity
- A toolset for black-box hyperparameter optimisation
- archai
- Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
Stars
- hypertunity
- 137
- archai
- 485
Forks
- hypertunity
- 10
- archai
- 93
Open issues
- hypertunity
- 0
- archai
- 4
Language
- hypertunity
- Python
- archai
- Python
Adopt for
- hypertunity
- hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization.
- archai
- Archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch.
Persona
- hypertunity
- -
- archai
- -
Runtime
- hypertunity
- -
- archai
- -
License
- hypertunity
- Apache-2.0
- archai
- MIT
Last pushed
- hypertunity
- Jan 26, 2020
- archai
- Nov 24, 2025
Categories
- hypertunity
- Model Training
- archai
- Model Training
Trust and health
Maintenance
- hypertunity
- Dormant (18%)
- archai
- Slowing (36%)
Days since push
- hypertunity
- 2381d
- archai
- 252d
Open issues (now)
- hypertunity
- 0
- archai
- 4
Owner type
- hypertunity
- User
- archai
- Organization
Full report
- hypertunity
- Trust report
- archai
- Trust report
Shared compatibility
- Python · hypertunity: Python runtime · archai: Python runtime
Choose hypertunity if…
- License: hypertunity is Apache-2.0, archai 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, slurm, tensorboard.
- 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.
Choose archai if…
- License: archai is MIT, hypertunity is Apache-2.0.
- Tags unique to archai: automated-machine-learning, automl, darts, deep-learning.
- Need rapid iteration in NAS projects while ensuring reproducibility
When NOT to use archai
- Project requires specific GPU support not aligned with PyTorch 1.7.0+ versions
- Development occurs outside Python 3.8+, limiting the application of Archai tools
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (microsoft/archai) · observed Aug 4, 2026
- GitHub forks (microsoft/archai) · observed Aug 4, 2026
- Last push (microsoft/archai) · observed Nov 24, 2025
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: hypertunity 137 · archai 485 (synced Aug 4, 2026).
Common questions
- What is the difference between hypertunity and archai?
- hypertunity: A toolset for black-box hyperparameter optimisation. archai: Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.. See the comparison table for live GitHub stats and shared categories.
- When should I choose hypertunity over archai?
- Choose hypertunity over archai when License: hypertunity is Apache-2.0, archai 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, slurm, tensorboard; 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 choose archai over hypertunity?
- Choose archai over hypertunity when License: archai is MIT, hypertunity is Apache-2.0; Tags unique to archai: automated-machine-learning, automl, darts, deep-learning; Need rapid iteration in NAS projects while ensuring reproducibility.
- 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.
- When should I avoid archai?
- Project requires specific GPU support not aligned with PyTorch 1.7.0+ versions Development occurs outside Python 3.8+, limiting the application of Archai tools
- Is hypertunity or archai more popular on GitHub?
- archai has more GitHub stars (485 vs 137). Stars measure visibility, not whether either tool fits your constraints.
- Are hypertunity and archai open source?
- Yes - both are open-source projects on GitHub (hypertunity: Apache-2.0, archai: MIT).
- Where can I find alternatives to hypertunity or archai?
- GraphCanon lists graph-backed alternatives at hypertunity alternatives and archai alternatives (hypertunity markdown twin, archai 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, hypertunity or archai?
- hypertunity: Dormant. archai: 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 hypertunity and archai?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: hypertunity trust report; archai trust report.