Comparison
Auto-PyTorch vs HPOBench
Verdict
Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick HPOBench if hPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios.
Markdown twin · Auto-PyTorch alternatives · HPOBench alternatives
GraphCanon updated Oct 2, 2026
14views this month
vs
Trust & integrity
| Signal | Auto-PyTorch | HPOBench |
|---|---|---|
| Maintenance | Dormant (903d since push) As of Sep 30, 2026 · github_public_v1 | Dormant (498d since push) As of Oct 2, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 2, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · 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
- HPOBench
- A collection of hyperparameter optimization benchmark problems
Stars
- Auto-PyTorch
- 2.5k
- HPOBench
- 173
Forks
- Auto-PyTorch
- 298
- HPOBench
- 36
Open issues
- Auto-PyTorch
- 75
- HPOBench
- 34
Language
- Auto-PyTorch
- Python
- HPOBench
- Python
Adopt for
- Auto-PyTorch
- Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
- HPOBench
- HPOBench is useful for researchers and developers working on hyperparameter optimization techniques in automated machine learning scenarios.
Persona
- Auto-PyTorch
- -
- HPOBench
- -
Runtime
- Auto-PyTorch
- -
- HPOBench
- -
License
- Auto-PyTorch
- Apache-2.0
- HPOBench
- HPOBench is open source under the Apache-2.0 license.
Last pushed
- Auto-PyTorch
- Apr 9, 2024
- HPOBench
- May 21, 2025
Categories
- Auto-PyTorch
- Data & Retrieval, Model Training
- HPOBench
- Model Training
Trust and health
Days since push
- Auto-PyTorch
- 903d
- HPOBench
- 498d
Open issues (now)
- Auto-PyTorch
- 75
- HPOBench
- 34
Stars delta
- Auto-PyTorch
- -1 (30d)
- HPOBench
- +3 (30d)
Full report
- Auto-PyTorch
- Trust report
- HPOBench
- Trust report
Shared compatibility
- Python · Auto-PyTorch: Python runtime · HPOBench: Python runtime
Choose Auto-PyTorch if…
- Tags unique to Auto-PyTorch: deep-learning, pytorch, tabular-data, time-series-forecasting.
- 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 HPOBench if…
- 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.
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 Sep 30, 2026
- GitHub forks (automl/Auto-PyTorch) · observed Sep 30, 2026
- Last push (automl/Auto-PyTorch) · observed Apr 9, 2024
- License file (Apache-2.0) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (automl/HPOBench) · observed Oct 2, 2026
- GitHub forks (automl/HPOBench) · observed Oct 2, 2026
- Last push (automl/HPOBench) · observed May 21, 2025
- License file (Apache-2.0) · observed Oct 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Auto-PyTorch 2.5k · HPOBench 173 (synced Sep 30, 2026).
Common questions
- What is the difference between Auto-PyTorch and HPOBench?
- Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. HPOBench: A collection of hyperparameter optimization benchmark problems. See the comparison table for live GitHub stats and shared categories.
- When should I choose Auto-PyTorch over HPOBench?
- Choose Auto-PyTorch over HPOBench when Tags unique to Auto-PyTorch: deep-learning, pytorch, tabular-data, time-series-forecasting; 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 HPOBench over Auto-PyTorch?
- Choose HPOBench over Auto-PyTorch when 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 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 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.
- Is Auto-PyTorch or HPOBench more popular on GitHub?
- Auto-PyTorch has more GitHub stars (2,540 vs 173). Stars measure visibility, not whether either tool fits your constraints.
- Are Auto-PyTorch and HPOBench open source?
- Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, HPOBench: Apache-2.0).
- Where can I find alternatives to Auto-PyTorch or HPOBench?
- GraphCanon lists graph-backed alternatives at Auto-PyTorch alternatives and HPOBench alternatives (Auto-PyTorch markdown twin, HPOBench 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 HPOBench?
- Auto-PyTorch: Dormant. HPOBench: 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 HPOBench?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Auto-PyTorch trust report; HPOBench trust report.