--- title: "HpBandSter vs rembo" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-hpbandster-vs-ziyuw-rembo" tools: ["automl-hpbandster", "ziyuw-rembo"] --- # HpBandSter vs rembo *GraphCanon updated Aug 4, 2026* ## Verdict Pick HpBandSter if hpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities; pick rembo if rembo is a Matlab-based tool for high-dimensional Bayesian optimization using random embedding. [HpBandSter](https://github.com/automl/HpBandSter) reports 632 GitHub stars, 107 forks, and 66 open issues, last pushed Oct 16, 2022. [rembo](https://github.com/ziyuw/rembo) has 117 stars, 25 forks, and 3 open issues, last pushed Aug 4, 2013. Figures are from public GitHub metadata via [HpBandSter's repository](https://github.com/automl/HpBandSter) and [rembo's repository](https://github.com/ziyuw/rembo). | | [HpBandSter](/tools/automl-hpbandster.md) | [rembo](/tools/ziyuw-rembo.md) | | --- | --- | --- | | Tagline | a distributed Hyperband implementation on Steroids | Bayesian optimization in high-dimensions via random embedding. | | Stars | 632 | 117 | | Forks | 107 | 25 | | Open issues | 66 | 3 | | Language | Python | Matlab | | Adopt for | HpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities. | Rembo is a Matlab-based tool for high-dimensional Bayesian optimization using random embedding. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause License - Permits free use but requires preservation of copyright and license notices. Contributors retain the copyrights to their contributions. | - | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [HpBandSter](/tools/automl-hpbandster.md) | [rembo](/tools/ziyuw-rembo.md) | | --- | --- | --- | | Days since push | 1387d | 4747d | | Open issues (now) | 66 | 3 | | Owner type | Organization | User | | Full report | [trust report](/tools/automl-hpbandster/trust.md) | [trust report](/tools/ziyuw-rembo/trust.md) | ## 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. ## Decision facts: rembo - **Adopt for:** Rembo is a Matlab-based tool for high-dimensional Bayesian optimization using random embedding. ## Choose when ### Choose HpBandSter if… - HpBandSter is primarily Python; rembo is Matlab. - 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, automl, 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. ### Choose rembo if… - rembo is primarily Matlab; HpBandSter is Python. - Tags unique to rembo: high-dimensional space, random embedding. - When working with high-dimensional data spaces that require efficient exploration and optimization, making it ideal for problems exceeding typical dimensions ## 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. ## When NOT to use rembo - For low-dimensional spaces where full Bayesian optimization methods would be more efficient and less complex than random embedding - In scenarios requiring open-source or licensed software when Rembo's license status is unknown, potentially limiting its use in certain projects ## Common questions ### What is the difference between HpBandSter and rembo? HpBandSter: a distributed Hyperband implementation on Steroids. rembo: Bayesian optimization in high-dimensions via random embedding.. See the comparison table for live GitHub stats and shared categories. ### When should I choose HpBandSter over rembo? Choose HpBandSter over rembo when HpBandSter is primarily Python; rembo is Matlab; 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, automl, 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 choose rembo over HpBandSter? Choose rembo over HpBandSter when rembo is primarily Matlab; HpBandSter is Python; Tags unique to rembo: high-dimensional space, random embedding; When working with high-dimensional data spaces that require efficient exploration and optimization, making it ideal for problems exceeding typical dimensions. ### 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. ### When should I avoid rembo? For low-dimensional spaces where full Bayesian optimization methods would be more efficient and less complex than random embedding In scenarios requiring open-source or licensed software when Rembo's license status is unknown, potentially limiting its use in certain projects ### Is HpBandSter or rembo more popular on GitHub? HpBandSter has more GitHub stars (632 vs 117). Stars measure visibility, not whether either tool fits your constraints. ### Are HpBandSter and rembo open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to HpBandSter or rembo? GraphCanon lists graph-backed alternatives at [HpBandSter alternatives](/tools/automl-hpbandster/alternatives) and [rembo alternatives](/tools/ziyuw-rembo/alternatives) ([HpBandSter markdown twin](/tools/automl-hpbandster/alternatives.md), [rembo markdown twin](/tools/ziyuw-rembo/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-hpbandster-vs-ziyuw-rembo.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, HpBandSter or rembo? HpBandSter: Dormant. rembo: 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 HpBandSter and rembo? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [HpBandSter trust report](/tools/automl-hpbandster/trust); [rembo trust report](/tools/ziyuw-rembo/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-hpbandster`](/api/graphcanon/graph?tool=automl-hpbandster) - 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/_