Comparison
Auto-PyTorch vs nni
Verdict
Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick nni if nNI is an AutoML toolkit that supports feature engineering, neural architecture search, model compression, and hyperparameter tuning with the flexibility of Python programming.
Markdown twin · Auto-PyTorch alternatives · nni alternatives
GraphCanon updated Oct 2, 2026
11views this month
Trust & integrity
| Signal | Auto-PyTorch | nni |
|---|---|---|
| Maintenance | Dormant (903d since push) As of Sep 30, 2026 · github_public_v1 | Archived (820d 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 | No lockfile (source not queried) 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
- nni
- An open source AutoML toolkit for automating machine learning lifecycle
Stars
- Auto-PyTorch
- 2.5k
- nni
- 14k
Forks
- Auto-PyTorch
- 298
- nni
- 1.9k
Open issues
- Auto-PyTorch
- 75
- nni
- 415
Language
- Auto-PyTorch
- Python
- nni
- Python
Adopt for
- Auto-PyTorch
- Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
- nni
- NNI is an AutoML toolkit that supports feature engineering, neural architecture search, model compression, and hyperparameter tuning with the flexibility of Python programming.
Persona
- Auto-PyTorch
- -
- nni
- -
Runtime
- Auto-PyTorch
- -
- nni
- -
License
- Auto-PyTorch
- Apache-2.0
- nni
- MIT
Last pushed
- Auto-PyTorch
- Apr 9, 2024
- nni
- Jul 3, 2024
Categories
- Auto-PyTorch
- Data & Retrieval, Model Training
- nni
- Model Training
Trust and health
Maintenance
- Auto-PyTorch
- Dormant (18%)
- nni
- Archived (8%)
Days since push
- Auto-PyTorch
- 903d
- nni
- 820d
Archived on GitHub
- Auto-PyTorch
- No
- nni
- Yes
Open issues (now)
- Auto-PyTorch
- 75
- nni
- 415
Stars delta
- Auto-PyTorch
- -1 (30d)
- nni
- +3 (30d)
OSV dependency advisories
- Auto-PyTorch
- Published findings
- nni
- No lockfile (source not queried)
Full report
- Auto-PyTorch
- Trust report
- nni
- Trust report
Shared compatibility
- Python · Auto-PyTorch: Python runtime · nni: Python runtime
Choose Auto-PyTorch if…
- License: Auto-PyTorch is Apache-2.0, nni is MIT.
- Tags unique to Auto-PyTorch: pytorch, tabular-data, time-series-forecasting.
- Also covers Data & Retrieval.
- 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 nni if…
- License: nni is MIT, Auto-PyTorch is Apache-2.0.
- Tags unique to nni: automated-machine-learning, bayesian-optimization, data-science, deep-neural-network.
- You need to automate extensive parts of your machine learning lifecycle from preprocessing to deployment.
When NOT to use nni
- You require real-time automated tuning capabilities, as NNI focuses on batch processing and model training scenarios.
- If your project demands direct integration with specific deep learning frameworks beyond PyTorch and TensorFlow, NNI support is limited to these two environments.
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 (microsoft/nni) · observed Oct 2, 2026
- GitHub forks (microsoft/nni) · observed Oct 2, 2026
- Last push (microsoft/nni) · observed Jul 3, 2024
- License file (MIT) · 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 · nni 14k (synced Sep 30, 2026).
Common questions
- What is the difference between Auto-PyTorch and nni?
- Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. nni: An open source AutoML toolkit for automating machine learning lifecycle. See the comparison table for live GitHub stats and shared categories.
- When should I choose Auto-PyTorch over nni?
- Choose Auto-PyTorch over nni when License: Auto-PyTorch is Apache-2.0, nni is MIT; Tags unique to Auto-PyTorch: pytorch, tabular-data, time-series-forecasting; Also covers Data & Retrieval; Use when you need to automate both architectural searches and hyperparameter tuning specifically for PyTorch-based deep learning models.
- When should I choose nni over Auto-PyTorch?
- Choose nni over Auto-PyTorch when License: nni is MIT, Auto-PyTorch is Apache-2.0; Tags unique to nni: automated-machine-learning, bayesian-optimization, data-science, deep-neural-network; You need to automate extensive parts of your machine learning lifecycle from preprocessing to deployment.
- 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 nni?
- You require real-time automated tuning capabilities, as NNI focuses on batch processing and model training scenarios. If your project demands direct integration with specific deep learning frameworks beyond PyTorch and TensorFlow, NNI support is limited to these two environments.
- Is Auto-PyTorch or nni more popular on GitHub?
- nni has more GitHub stars (14,366 vs 2,540). Stars measure visibility, not whether either tool fits your constraints.
- Are Auto-PyTorch and nni open source?
- Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, nni: MIT).
- Where can I find alternatives to Auto-PyTorch or nni?
- GraphCanon lists graph-backed alternatives at Auto-PyTorch alternatives and nni alternatives (Auto-PyTorch markdown twin, nni 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 nni?
- Auto-PyTorch: Dormant. nni: Archived. 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 nni?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Auto-PyTorch trust report; nni trust report.