--- title: "Auto-PyTorch vs HpBandSter" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-pytorch-vs-automl-hpbandster" tools: ["automl-auto-pytorch", "automl-hpbandster"] --- # Auto-PyTorch vs HpBandSter *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 HpBandSter if hpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities. [Auto-PyTorch](https://github.com/automl/Auto-PyTorch) reports 2.5k GitHub stars, 303 forks, and 75 open issues, last pushed Apr 9, 2024. [HpBandSter](https://github.com/automl/HpBandSter) has 632 stars, 107 forks, and 66 open issues, last pushed Oct 16, 2022. Figures are from public GitHub metadata via [Auto-PyTorch's repository](https://github.com/automl/Auto-PyTorch) and [HpBandSter's repository](https://github.com/automl/HpBandSter). | | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | [HpBandSter](/tools/automl-hpbandster.md) | | --- | --- | --- | | Tagline | Automatic architecture search and hyperparameter optimization for PyTorch | a distributed Hyperband implementation on Steroids | | Stars | 2,541 | 632 | | Forks | 303 | 107 | | Open issues | 75 | 66 | | Language | Python | Python | | Adopt for | Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. | HpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | BSD-3-Clause License - Permits free use but requires preservation of copyright and license notices. Contributors retain the copyrights to their contributions. | | 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) | [HpBandSter](/tools/automl-hpbandster.md) | | --- | --- | --- | | Days since push | 846d | 1387d | | Open issues (now) | 75 | 66 | | Full report | [trust report](/tools/automl-auto-pytorch/trust.md) | [trust report](/tools/automl-hpbandster/trust.md) | ## Shared compatibility - **Python**: [Auto-PyTorch](/tools/automl-auto-pytorch.md) - Python runtime; [HpBandSter](/tools/automl-hpbandster.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: HpBandSter - **Pricing:** freemium - HpBandSter is open-source software under a permissive BSD-3-Clause License, allowing unrestricted usage for personal or commercial purposes without any direct costs. - **Requirements:** Min 4 GB RAM; Requires Python environment. No Docker required. - **Adopt for:** HpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities. - **License detail:** BSD-3-Clause License - Permits free use but requires preservation of copyright and license notices. Contributors retain the copyrights to their contributions. ## Choose when ### Choose Auto-PyTorch if… - License: Auto-PyTorch is Apache-2.0, HpBandSter 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. ### Choose HpBandSter if… - License: HpBandSter is BSD-3-Clause, Auto-PyTorch is Apache-2.0. - Pricing: HpBandSter is open-source software under a permissive BSD-3-Clause License, allowing unrestricted usage for personal or commercial purposes without any direct costs.. - Requirements: Min 4 GB RAM; Requires Python environment. No Docker required.. - Tags unique to HpBandSter: automated-machine-learning, bayesian-optimization, hyperparameter-optimization, neural-architecture-search. - HpBandSter is best used when conducting large-scale experiments on multiple machines that require efficient resource management across different environments. ## 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 HpBandSter - If your project involves smaller datasets or less complex models where individual hyperparameter tuning can be done manually, HpBandSter might be an overkill due to its advanced distributed settings. - Avoid using HpBandSter if you need a tool that heavily relies on Bayesian optimization techniques, as it specializes more in Hyperband methodology. ## Common questions ### What is the difference between Auto-PyTorch and HpBandSter? Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. HpBandSter: a distributed Hyperband implementation on Steroids. See the comparison table for live GitHub stats and shared categories. ### When should I choose Auto-PyTorch over HpBandSter? Choose Auto-PyTorch over HpBandSter when License: Auto-PyTorch is Apache-2.0, HpBandSter 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 choose HpBandSter over Auto-PyTorch? Choose HpBandSter over Auto-PyTorch when License: HpBandSter is BSD-3-Clause, Auto-PyTorch is Apache-2.0; Pricing: HpBandSter is open-source software under a permissive BSD-3-Clause License, allowing unrestricted usage for personal or commercial purposes without any direct costs.; Requirements: Min 4 GB RAM; Requires Python environment. No Docker required.; Tags unique to HpBandSter: automated-machine-learning, bayesian-optimization, hyperparameter-optimization, neural-architecture-search; HpBandSter is best used when conducting large-scale experiments on multiple machines that require efficient resource management across different environments. ### 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 HpBandSter? If your project involves smaller datasets or less complex models where individual hyperparameter tuning can be done manually, HpBandSter might be an overkill due to its advanced distributed settings. Avoid using HpBandSter if you need a tool that heavily relies on Bayesian optimization techniques, as it specializes more in Hyperband methodology. ### Is Auto-PyTorch or HpBandSter more popular on GitHub? Auto-PyTorch has more GitHub stars (2,541 vs 632). Stars measure visibility, not whether either tool fits your constraints. ### Are Auto-PyTorch and HpBandSter open source? Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, HpBandSter: BSD-3-Clause). ### Where can I find alternatives to Auto-PyTorch or HpBandSter? GraphCanon lists graph-backed alternatives at [Auto-PyTorch alternatives](/tools/automl-auto-pytorch/alternatives) and [HpBandSter alternatives](/tools/automl-hpbandster/alternatives) ([Auto-PyTorch markdown twin](/tools/automl-auto-pytorch/alternatives.md), [HpBandSter markdown twin](/tools/automl-hpbandster/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-automl-hpbandster.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Auto-PyTorch or HpBandSter? Auto-PyTorch: Dormant. HpBandSter: 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 HpBandSter? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Auto-PyTorch trust report](/tools/automl-auto-pytorch/trust); [HpBandSter trust report](/tools/automl-hpbandster/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/_