--- title: "Auto-PyTorch vs hyperband" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-pytorch-vs-zygmuntz-hyperband" tools: ["automl-auto-pytorch", "zygmuntz-hyperband"] --- # Auto-PyTorch vs hyperband *GraphCanon updated Aug 4, 2026* ## Verdict Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick hyperband if hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn. [Auto-PyTorch](https://github.com/automl/Auto-PyTorch) reports 2.5k GitHub stars, 303 forks, and 75 open issues, last pushed Apr 9, 2024. [hyperband](http://fastml.com/tuning-hyperparams-fast-with-hyperband/) has 599 stars, 73 forks, and 9 open issues, last pushed Aug 15, 2018. Figures are from public GitHub metadata via [Auto-PyTorch's repository](https://github.com/automl/Auto-PyTorch) and [hyperband's repository](https://github.com/zygmuntz/hyperband). | | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | [hyperband](/tools/zygmuntz-hyperband.md) | | --- | --- | --- | | Tagline | Automatic architecture search and hyperparameter optimization for PyTorch | Tuning hyperparams fast with Hyperband | | Stars | 2,541 | 599 | | Forks | 303 | 73 | | Open issues | 75 | 9 | | Language | Python | Python | | Adopt for | Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. | Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | Data & Retrieval, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | [hyperband](/tools/zygmuntz-hyperband.md) | | --- | --- | --- | | Days since push | 846d | 2910d | | Open issues (now) | 75 | 9 | | Owner type | Organization | User | | Full report | [trust report](/tools/automl-auto-pytorch/trust.md) | [trust report](/tools/zygmuntz-hyperband/trust.md) | ## Shared compatibility - **Python**: [Auto-PyTorch](/tools/automl-auto-pytorch.md) - Python runtime; [hyperband](/tools/zygmuntz-hyperband.md) - Python runtime ## Decision facts: Auto-PyTorch - **Adopt for:** Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. ## Decision facts: hyperband - **Adopt for:** Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn. ## Choose when ### Choose Auto-PyTorch if… - License: Auto-PyTorch is Apache-2.0, hyperband is Other. - Tags unique to Auto-PyTorch: automl, deep-learning, pytorch, tabular-data. - 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. ### Choose hyperband if… - License: hyperband is Other, Auto-PyTorch is Apache-2.0. - Tags unique to hyperband: classification, gradient-boosting, hyperparameter-optimization, machine-learning. - Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn. ## 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. ## When NOT to use hyperband - Avoid Hyperband if you require custom data formats that differ significantly from scikit-learn conventions, as this will necessitate extensive customization of the load_data modules. - Do not use Hyperband when the models you need for hyperparameter tuning are not among the eight pre-supported models; additional support is required outside what comes built-in. ## Common questions ### What is the difference between Auto-PyTorch and hyperband? Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. hyperband: Tuning hyperparams fast with Hyperband. See the comparison table for live GitHub stats and shared categories. ### When should I choose Auto-PyTorch over hyperband? Choose Auto-PyTorch over hyperband when License: Auto-PyTorch is Apache-2.0, hyperband is Other; Tags unique to Auto-PyTorch: automl, deep-learning, pytorch, tabular-data; 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 hyperband over Auto-PyTorch? Choose hyperband over Auto-PyTorch when License: hyperband is Other, Auto-PyTorch is Apache-2.0; Tags unique to hyperband: classification, gradient-boosting, hyperparameter-optimization, machine-learning; Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn. ### 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 hyperband? Avoid Hyperband if you require custom data formats that differ significantly from scikit-learn conventions, as this will necessitate extensive customization of the load_data modules. Do not use Hyperband when the models you need for hyperparameter tuning are not among the eight pre-supported models; additional support is required outside what comes built-in. ### Is Auto-PyTorch or hyperband more popular on GitHub? Auto-PyTorch has more GitHub stars (2,541 vs 599). Stars measure visibility, not whether either tool fits your constraints. ### Are Auto-PyTorch and hyperband open source? Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, hyperband: Other). ### Where can I find alternatives to Auto-PyTorch or hyperband? GraphCanon lists graph-backed alternatives at [Auto-PyTorch alternatives](/tools/automl-auto-pytorch/alternatives) and [hyperband alternatives](/tools/zygmuntz-hyperband/alternatives) ([Auto-PyTorch markdown twin](/tools/automl-auto-pytorch/alternatives.md), [hyperband markdown twin](/tools/zygmuntz-hyperband/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/automl-auto-pytorch-vs-zygmuntz-hyperband.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Auto-PyTorch or hyperband? Auto-PyTorch: Dormant. hyperband: 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 hyperband? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Auto-PyTorch trust report](/tools/automl-auto-pytorch/trust); [hyperband trust report](/tools/zygmuntz-hyperband/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-auto-pytorch`](/api/graphcanon/graph?tool=automl-auto-pytorch) - 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/_