Comparison
Auto-PyTorch vs hypertunity
Verdict
Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick hypertunity if hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization.
Markdown twin · Auto-PyTorch alternatives · hypertunity alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Auto-PyTorch | hypertunity |
|---|---|---|
| Maintenance | Dormant (846d since push) As of 3w · github_public_v1 | Dormant (2381d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · 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
- Auto-PyTorch
- Automatic architecture search and hyperparameter optimization for PyTorch
- hypertunity
- A toolset for black-box hyperparameter optimisation
Stars
- Auto-PyTorch
- 2.5k
- hypertunity
- 137
Forks
- Auto-PyTorch
- 303
- hypertunity
- 10
Open issues
- Auto-PyTorch
- 75
- hypertunity
- 0
Language
- Auto-PyTorch
- Python
- hypertunity
- Python
Adopt for
- Auto-PyTorch
- Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
- hypertunity
- hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization.
Persona
- Auto-PyTorch
- -
- hypertunity
- -
Runtime
- Auto-PyTorch
- -
- hypertunity
- -
License
- Auto-PyTorch
- Apache-2.0
- hypertunity
- Apache-2.0
Last pushed
- Auto-PyTorch
- Apr 9, 2024
- hypertunity
- Jan 26, 2020
Categories
- Auto-PyTorch
- Data & Retrieval, Model Training
- hypertunity
- Model Training
Trust and health
Days since push
- Auto-PyTorch
- 846d
- hypertunity
- 2381d
Open issues (now)
- Auto-PyTorch
- 75
- hypertunity
- 0
Owner type
- Auto-PyTorch
- Organization
- hypertunity
- User
OSV dependency advisories
- Auto-PyTorch
- Published findings
- hypertunity
- No lockfile (source not queried)
Full report
- Auto-PyTorch
- Trust report
- hypertunity
- Trust report
Shared compatibility
- Python · Auto-PyTorch: Python runtime · hypertunity: Python runtime
Choose Auto-PyTorch if…
- Tags unique to Auto-PyTorch: automl, deep-learning, pytorch, tabular-data.
- Also covers Data & Retrieval.
- Auto-PyTorch ships Docker support for self-hosted deployment.
- Use when you need to automate both architectural searches and hyperparameter tuning specifically for PyTorch-based deep learning models.
When NOT to use Auto-PyTorch
- Avoid using it if your AI development focuses on frameworks other than PyTorch.
- Do not use when the requirements do not involve deep learning models or you are not interested in automating architecture search and hyperparameter tuning.
Choose hypertunity if…
- 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 (automl/Auto-PyTorch) · observed Aug 4, 2026
- GitHub forks (automl/Auto-PyTorch) · observed Aug 4, 2026
- Last push (automl/Auto-PyTorch) · observed Apr 9, 2024
- 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 (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: Auto-PyTorch 2.5k · hypertunity 137 (synced Aug 4, 2026).
Common questions
- What is the difference between Auto-PyTorch and hypertunity?
- Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. hypertunity: A toolset for black-box hyperparameter optimisation. See the comparison table for live GitHub stats and shared categories.
- When should I choose Auto-PyTorch over hypertunity?
- Choose Auto-PyTorch over hypertunity when Tags unique to Auto-PyTorch: automl, deep-learning, pytorch, tabular-data; Also covers Data & Retrieval; Auto-PyTorch ships Docker support for self-hosted deployment; Use when you need to automate both architectural searches and hyperparameter tuning specifically for PyTorch-based deep learning models.
- When should I choose hypertunity over Auto-PyTorch?
- Choose hypertunity over Auto-PyTorch when 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 Auto-PyTorch?
- Avoid using it if your AI development focuses on frameworks other than PyTorch. Do not use when the requirements do not involve deep learning models or you are not interested in automating architecture search and hyperparameter tuning.
- 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 Auto-PyTorch or hypertunity more popular on GitHub?
- Auto-PyTorch has more GitHub stars (2,541 vs 137). Stars measure visibility, not whether either tool fits your constraints.
- Are Auto-PyTorch and hypertunity open source?
- Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, hypertunity: Apache-2.0).
- Where can I find alternatives to Auto-PyTorch or hypertunity?
- GraphCanon lists graph-backed alternatives at Auto-PyTorch alternatives and hypertunity alternatives (Auto-PyTorch 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, Auto-PyTorch or hypertunity?
- Auto-PyTorch: 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 Auto-PyTorch and hypertunity?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Auto-PyTorch trust report; hypertunity trust report.