--- title: "hypertunity vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gdikov-hypertunity-vs-windmaple-awesome-automl" tools: ["gdikov-hypertunity", "windmaple-awesome-automl"] --- # hypertunity vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## Verdict Pick hypertunity if hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. [hypertunity](https://hypertunity.readthedocs.io) reports 137 GitHub stars, 10 forks, and 0 open issues, last pushed Jan 26, 2020. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) has 941 stars, 156 forks, and 1 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [hypertunity's repository](https://github.com/gdikov/hypertunity) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [hypertunity](/tools/gdikov-hypertunity.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | A toolset for black-box hyperparameter optimisation | Curating AutoML research and resources | | Stars | 137 | 941 | | Forks | 10 | 156 | | Open issues | 0 | 1 | | Language | Python | - | | Adopt for | hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | GPL-3.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hypertunity](/tools/gdikov-hypertunity.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 2381d | 133d | | Open issues (now) | 0 | 1 | | Full report | [trust report](/tools/gdikov-hypertunity/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: hypertunity - **Requirements:** Min 2 GB RAM; Support for SLURM is indicated in the topics, useful for HPC cluster management but not a hard requirement. - **Adopt for:** hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization. ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### Choose hypertunity if… - License: hypertunity is Apache-2.0, awesome-AutoML is GPL-3.0. - Requirements: Min 2 GB RAM; Support for SLURM is indicated in the topics, useful for HPC cluster management but not a hard requirement.. - Tags unique to hypertunity: bayesian-optimization, gpyopt, slurm, tensorboard. - When you are working with complex objective functions that are expensive to evaluate, and you need an automated way to optimize your model parameters. ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, hypertunity is Apache-2.0. - Tags unique to awesome-AutoML: automl, meta-learning, neural-architecture-search. - When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ## When NOT to use hypertunity - When the objective function evaluation is inexpensive or fast because hypertunity shines in scenarios where evaluations are costly, offering less benefit if evaluations can be easily repeated. - If your project does not require advanced techniques such as Bayesian Optimization and you seek a simpler method with fewer dependencies. ## When NOT to use awesome-AutoML - If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. - When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool. ## Common questions ### What is the difference between hypertunity and awesome-AutoML? hypertunity: A toolset for black-box hyperparameter optimisation. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose hypertunity over awesome-AutoML? Choose hypertunity over awesome-AutoML when License: hypertunity is Apache-2.0, awesome-AutoML is GPL-3.0; Requirements: Min 2 GB RAM; Support for SLURM is indicated in the topics, useful for HPC cluster management but not a hard requirement.; Tags unique to hypertunity: bayesian-optimization, gpyopt, slurm, tensorboard; When you are working with complex objective functions that are expensive to evaluate, and you need an automated way to optimize your model parameters. ### When should I choose awesome-AutoML over hypertunity? Choose awesome-AutoML over hypertunity when License: awesome-AutoML is GPL-3.0, hypertunity is Apache-2.0; Tags unique to awesome-AutoML: automl, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ### When should I avoid hypertunity? When the objective function evaluation is inexpensive or fast because hypertunity shines in scenarios where evaluations are costly, offering less benefit if evaluations can be easily repeated. If your project does not require advanced techniques such as Bayesian Optimization and you seek a simpler method with fewer dependencies. ### When should I avoid awesome-AutoML? If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool. ### Is hypertunity or awesome-AutoML more popular on GitHub? awesome-AutoML has more GitHub stars (941 vs 137). Stars measure visibility, not whether either tool fits your constraints. ### Are hypertunity and awesome-AutoML open source? Yes - both are open-source projects on GitHub (hypertunity: Apache-2.0, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to hypertunity or awesome-AutoML? GraphCanon lists graph-backed alternatives at [hypertunity alternatives](/tools/gdikov-hypertunity/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([hypertunity markdown twin](/tools/gdikov-hypertunity/alternatives.md), [awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/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/gdikov-hypertunity-vs-windmaple-awesome-automl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hypertunity or awesome-AutoML? hypertunity: Dormant. awesome-AutoML: Slowing. 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 hypertunity and awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hypertunity trust report](/tools/gdikov-hypertunity/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gdikov-hypertunity`](/api/graphcanon/graph?tool=gdikov-hypertunity) - 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/_