--- title: "FEDOT vs Auto-PyTorch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aimclub-fedot-vs-automl-auto-pytorch" tools: ["aimclub-fedot", "automl-auto-pytorch"] --- # FEDOT vs Auto-PyTorch *GraphCanon updated Aug 4, 2026* ## 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. [FEDOT](https://fedot.readthedocs.io) reports 709 GitHub stars, 94 forks, and 82 open issues, last pushed Jul 27, 2026. [Auto-PyTorch](https://github.com/automl/Auto-PyTorch) has 2.5k stars, 303 forks, and 75 open issues, last pushed Apr 9, 2024. Figures are from public GitHub metadata via [FEDOT's repository](https://github.com/aimclub/FEDOT) and [Auto-PyTorch's repository](https://github.com/automl/Auto-PyTorch). | | [FEDOT](/tools/aimclub-fedot.md) | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | | --- | --- | --- | | Tagline | Automated modeling and machine learning framework FEDOT | Automatic architecture search and hyperparameter optimization for PyTorch | | Stars | 709 | 2,541 | | Forks | 94 | 303 | | Open issues | 82 | 75 | | Language | Python | Python | | Adopt for | FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction. | Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | Apache-2.0 | | Categories | Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FEDOT](/tools/aimclub-fedot.md) | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 7d | 846d | | Open issues (now) | 82 | 75 | | Full report | [trust report](/tools/aimclub-fedot/trust.md) | [trust report](/tools/automl-auto-pytorch/trust.md) | ## Shared compatibility - **Python**: [FEDOT](/tools/aimclub-fedot.md) - Python runtime; [Auto-PyTorch](/tools/automl-auto-pytorch.md) - Python runtime ## Decision facts: FEDOT - **Adopt for:** FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction. ## Decision facts: Auto-PyTorch - **Adopt for:** Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. ## Choose when ### 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 ### 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 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 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. ## 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 (2,541 vs 709). 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](/tools/aimclub-fedot/alternatives) and [Auto-PyTorch alternatives](/tools/automl-auto-pytorch/alternatives) ([FEDOT markdown twin](/tools/aimclub-fedot/alternatives.md), [Auto-PyTorch markdown twin](/tools/automl-auto-pytorch/alternatives.md)), 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](/compare/aimclub-fedot-vs-automl-auto-pytorch.md) 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: 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](/tools/aimclub-fedot/trust); [Auto-PyTorch trust report](/tools/automl-auto-pytorch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aimclub-fedot`](/api/graphcanon/graph?tool=aimclub-fedot) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_