Comparison
FEDOT vs Auto-PyTorch
Verdict
Pick FEDOT if fEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction; pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
Markdown twin · FEDOT alternatives · Auto-PyTorch alternatives
GraphCanon updated Sep 30, 2026
13views this month
Trust & integrity
| Signal | FEDOT | Auto-PyTorch |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 30, 2026 · github_public_v1 | Dormant (903d since push) As of Sep 30, 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 Sep 30, 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
- FEDOT
- Automated modeling and machine learning framework FEDOT
- Auto-PyTorch
- Automatic architecture search and hyperparameter optimization for PyTorch
Stars
- FEDOT
- 711
- Auto-PyTorch
- 2.5k
Forks
- FEDOT
- 98
- Auto-PyTorch
- 298
Open issues
- FEDOT
- 83
- Auto-PyTorch
- 75
Language
- FEDOT
- Python
- Auto-PyTorch
- Python
Adopt for
- FEDOT
- FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction.
- Auto-PyTorch
- Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
Persona
- FEDOT
- -
- Auto-PyTorch
- -
Runtime
- FEDOT
- -
- Auto-PyTorch
- -
License
- FEDOT
- BSD-3-Clause
- Auto-PyTorch
- Apache-2.0
Last pushed
- FEDOT
- Sep 29, 2026
- Auto-PyTorch
- Apr 9, 2024
Categories
- FEDOT
- Model Training
- Auto-PyTorch
- Data & Retrieval, Model Training
Trust and health
Maintenance
- FEDOT
- Very active (96%)
- Auto-PyTorch
- Dormant (18%)
Days since push
- FEDOT
- 0d
- Auto-PyTorch
- 903d
Open issues (now)
- FEDOT
- 83
- Auto-PyTorch
- 75
Stars delta
- FEDOT
- +2 (30d)
- Auto-PyTorch
- -1 (30d)
Open issues delta
- FEDOT
- +1 (30d)
- Auto-PyTorch
- 0 (30d)
Full report
- FEDOT
- Trust report
- Auto-PyTorch
- Trust report
Shared compatibility
- Python · FEDOT: Python runtime · Auto-PyTorch: Python runtime
Choose FEDOT if…
- License: FEDOT is BSD-3-Clause, Auto-PyTorch is Apache-2.0.
- Tags unique to FEDOT: evolutionary-algorithms, genetic-programming, hyperparameter-optimization, structural-learning.
- For projects requiring automated generative design of machine-learning pipelines suitable for a wide range of tasks and data types
When NOT to use FEDOT
- In scenarios with strict real-time requirements due to its optimization time
- For environments where only specific ML libraries are acceptable and FEDOT's integrations do not meet those needs
- When the project specifically requires a non-evolutionary approach for pipeline design
Choose Auto-PyTorch if…
- License: Auto-PyTorch is Apache-2.0, FEDOT is BSD-3-Clause.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (aimclub/FEDOT) · observed Sep 30, 2026
- GitHub forks (aimclub/FEDOT) · observed Sep 30, 2026
- Last push (aimclub/FEDOT) · observed Sep 29, 2026
- License file (BSD-3-Clause) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: FEDOT 711 · Auto-PyTorch 2.5k (synced Sep 30, 2026).
Common questions
- What is the difference between FEDOT and Auto-PyTorch?
- FEDOT: Automated modeling and machine learning framework FEDOT. Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. See the comparison table for live GitHub stats and shared categories.
- When should I choose FEDOT over Auto-PyTorch?
- Choose FEDOT over Auto-PyTorch when License: FEDOT is BSD-3-Clause, Auto-PyTorch is Apache-2.0; Tags unique to FEDOT: evolutionary-algorithms, genetic-programming, hyperparameter-optimization, structural-learning; For projects requiring automated generative design of machine-learning pipelines suitable for a wide range of tasks and data types.
- When should I choose Auto-PyTorch over FEDOT?
- Choose Auto-PyTorch over FEDOT when License: Auto-PyTorch is Apache-2.0, FEDOT is BSD-3-Clause; 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 avoid FEDOT?
- In scenarios with strict real-time requirements due to its optimization time For environments where only specific ML libraries are acceptable and FEDOT's integrations do not meet those needs When the project specifically requires a non-evolutionary approach for pipeline design
- 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.
- Is FEDOT or Auto-PyTorch more popular on GitHub?
- Auto-PyTorch has more GitHub stars (2,540 vs 711). Stars measure visibility, not whether either tool fits your constraints.
- Are FEDOT and Auto-PyTorch open source?
- Yes - both are open-source projects on GitHub (FEDOT: BSD-3-Clause, Auto-PyTorch: Apache-2.0).
- Where can I find alternatives to FEDOT or Auto-PyTorch?
- GraphCanon lists graph-backed alternatives at FEDOT alternatives and Auto-PyTorch alternatives (FEDOT markdown twin, Auto-PyTorch 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, FEDOT or Auto-PyTorch?
- FEDOT: Very active. Auto-PyTorch: 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 FEDOT and Auto-PyTorch?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FEDOT trust report; Auto-PyTorch trust report.