--- title: "evalml vs Auto-PyTorch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alteryx-evalml-vs-automl-auto-pytorch" tools: ["alteryx-evalml", "automl-auto-pytorch"] --- # evalml vs Auto-PyTorch *GraphCanon updated Aug 4, 2026* ## Verdict Pick evalml if evalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license; pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. [evalml](https://evalml.alteryx.com) reports 852 GitHub stars, 93 forks, and 324 open issues, last pushed Jan 14, 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 [evalml's repository](https://github.com/alteryx/evalml) and [Auto-PyTorch's repository](https://github.com/automl/Auto-PyTorch). | | [evalml](/tools/alteryx-evalml.md) | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | | --- | --- | --- | | Tagline | An AutoML library written in Python | Automatic architecture search and hyperparameter optimization for PyTorch | | Stars | 852 | 2,541 | | Forks | 93 | 303 | | Open issues | 324 | 75 | | Language | Python | Python | | Adopt for | EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license. | Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. | | Persona | - | - | | Runtime | - | - | | License | EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices. | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [evalml](/tools/alteryx-evalml.md) | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 201d | 846d | | Open issues (now) | 324 | 75 | | Full report | [trust report](/tools/alteryx-evalml/trust.md) | [trust report](/tools/automl-auto-pytorch/trust.md) | ## Shared compatibility - **Python**: [evalml](/tools/alteryx-evalml.md) - Python runtime; [Auto-PyTorch](/tools/automl-auto-pytorch.md) - Python runtime ## Decision facts: evalml - **Pricing:** freemium - Access to features comes at no cost due to its open-source nature; however, premium support can be purchased. - **Requirements:** Min 2 GB RAM - **Adopt for:** EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license. - **License detail:** EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices. ## 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 evalml if… - License: evalml is BSD-3-Clause, Auto-PyTorch is Apache-2.0. - Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased.. - Requirements: Min 2 GB RAM. - Tags unique to evalml: data-science, feature-engineering, feature-selection, hyperparameter-tuning. - Also covers Evaluation & Observability. - You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical. ### Choose Auto-PyTorch if… - License: Auto-PyTorch is Apache-2.0, evalml 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 evalml - You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box. - Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities. ## 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 evalml and Auto-PyTorch? evalml: An AutoML library written in Python. 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 evalml over Auto-PyTorch? Choose evalml over Auto-PyTorch when License: evalml is BSD-3-Clause, Auto-PyTorch is Apache-2.0; Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased.; Requirements: Min 2 GB RAM; Tags unique to evalml: data-science, feature-engineering, feature-selection, hyperparameter-tuning; Also covers Evaluation & Observability; You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical. ### When should I choose Auto-PyTorch over evalml? Choose Auto-PyTorch over evalml when License: Auto-PyTorch is Apache-2.0, evalml 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 evalml? You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box. Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities. ### 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 evalml or Auto-PyTorch more popular on GitHub? Auto-PyTorch has more GitHub stars (2,541 vs 852). Stars measure visibility, not whether either tool fits your constraints. ### Are evalml and Auto-PyTorch open source? Yes - both are open-source projects on GitHub (evalml: BSD-3-Clause, Auto-PyTorch: Apache-2.0). ### Where can I find alternatives to evalml or Auto-PyTorch? GraphCanon lists graph-backed alternatives at [evalml alternatives](/tools/alteryx-evalml/alternatives) and [Auto-PyTorch alternatives](/tools/automl-auto-pytorch/alternatives) ([evalml markdown twin](/tools/alteryx-evalml/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/alteryx-evalml-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, evalml or Auto-PyTorch? evalml: Slowing. 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 evalml and Auto-PyTorch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [evalml trust report](/tools/alteryx-evalml/trust); [Auto-PyTorch trust report](/tools/automl-auto-pytorch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alteryx-evalml`](/api/graphcanon/graph?tool=alteryx-evalml) - 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/_