--- title: "hypertunity vs autokeras" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gdikov-hypertunity-vs-keras-team-autokeras" tools: ["gdikov-hypertunity", "keras-team-autokeras"] --- # hypertunity vs autokeras *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 autokeras if autoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. [hypertunity](https://hypertunity.readthedocs.io) reports 137 GitHub stars, 10 forks, and 0 open issues, last pushed Jan 26, 2020. [autokeras](http://autokeras.com/) has 9.3k stars, 1.4k forks, and 161 open issues, last pushed Nov 25, 2025. Figures are from public GitHub metadata via [hypertunity's repository](https://github.com/gdikov/hypertunity) and [autokeras's repository](https://github.com/keras-team/autokeras). | | [hypertunity](/tools/gdikov-hypertunity.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Tagline | A toolset for black-box hyperparameter optimisation | AutoML library for deep learning | | Stars | 137 | 9,328 | | Forks | 10 | 1,393 | | Open issues | 0 | 161 | | Language | Python | Python | | Adopt for | hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization. | AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hypertunity](/tools/gdikov-hypertunity.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 2381d | 251d | | Open issues (now) | 0 | 161 | | Owner type | User | Organization | | Full report | [trust report](/tools/gdikov-hypertunity/trust.md) | [trust report](/tools/keras-team-autokeras/trust.md) | ## Shared compatibility - **Python**: [hypertunity](/tools/gdikov-hypertunity.md) - Python runtime; [autokeras](/tools/keras-team-autokeras.md) - Python runtime ## 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: autokeras - **Adopt for:** AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. ## Choose when ### Choose hypertunity if… - 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, hyperparameter-optimization, slurm. - 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 autokeras if… - Tags unique to autokeras: autodl, automl, deep-learning, keras. - Also covers Developer Tools. - When your project involves deep learning tasks requiring minimal manual intervention in designing models. ## 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 autokeras - When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. - If your project emphasizes transparent, understandable model architecture over automated generation without human oversight. ## Common questions ### What is the difference between hypertunity and autokeras? hypertunity: A toolset for black-box hyperparameter optimisation. autokeras: AutoML library for deep learning. See the comparison table for live GitHub stats and shared categories. ### When should I choose hypertunity over autokeras? Choose hypertunity over autokeras when 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, hyperparameter-optimization, slurm; 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 autokeras over hypertunity? Choose autokeras over hypertunity when Tags unique to autokeras: autodl, automl, deep-learning, keras; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models. ### 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 autokeras? When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. If your project emphasizes transparent, understandable model architecture over automated generation without human oversight. ### Is hypertunity or autokeras more popular on GitHub? autokeras has more GitHub stars (9,328 vs 137). Stars measure visibility, not whether either tool fits your constraints. ### Are hypertunity and autokeras open source? Yes - both are open-source projects on GitHub (hypertunity: Apache-2.0, autokeras: Apache-2.0). ### Where can I find alternatives to hypertunity or autokeras? GraphCanon lists graph-backed alternatives at [hypertunity alternatives](/tools/gdikov-hypertunity/alternatives) and [autokeras alternatives](/tools/keras-team-autokeras/alternatives) ([hypertunity markdown twin](/tools/gdikov-hypertunity/alternatives.md), [autokeras markdown twin](/tools/keras-team-autokeras/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-keras-team-autokeras.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hypertunity or autokeras? hypertunity: Dormant. autokeras: 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 autokeras? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hypertunity trust report](/tools/gdikov-hypertunity/trust); [autokeras trust report](/tools/keras-team-autokeras/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/_