--- title: "Hypernets vs autokeras" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datacanvasio-hypernets-vs-keras-team-autokeras" tools: ["datacanvasio-hypernets", "keras-team-autokeras"] --- # Hypernets vs autokeras *GraphCanon updated Aug 4, 2026* ## Verdict Pick Hypernets if hypernets is an AutoML framework supporting multiple ML frameworks for end-to-end AutoML solutions in specific domains; 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+. [Hypernets](https://hypernets.readthedocs.io/) reports 265 GitHub stars, 39 forks, and 0 open issues, last pushed Apr 20, 2026. [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 [Hypernets's repository](https://github.com/DataCanvasIO/Hypernets) and [autokeras's repository](https://github.com/keras-team/autokeras). | | [Hypernets](/tools/datacanvasio-hypernets.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Tagline | A General Automated Machine Learning framework for building domain-specific AutoML toolkits. | AutoML library for deep learning | | Stars | 265 | 9,328 | | Forks | 39 | 1,393 | | Open issues | 0 | 161 | | Language | Python | Python | | Adopt for | Hypernets is an AutoML framework supporting multiple ML frameworks for end-to-end AutoML solutions in specific domains. | 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 | Licensed under the Apache-2.0 license, allowing free use and distribution as long as copyright and licensing notices are preserved. | Apache-2.0 | | Categories | Developer Tools, Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Hypernets](/tools/datacanvasio-hypernets.md) | [autokeras](/tools/keras-team-autokeras.md) | | --- | --- | --- | | Days since push | 106d | 251d | | Open issues (now) | 0 | 161 | | Full report | [trust report](/tools/datacanvasio-hypernets/trust.md) | [trust report](/tools/keras-team-autokeras/trust.md) | ## Shared compatibility - **Python**: [Hypernets](/tools/datacanvasio-hypernets.md) - Python runtime; [autokeras](/tools/keras-team-autokeras.md) - Python runtime ## Decision facts: Hypernets - **Adopt for:** Hypernets is an AutoML framework supporting multiple ML frameworks for end-to-end AutoML solutions in specific domains. - **License detail:** Licensed under the Apache-2.0 license, allowing free use and distribution as long as copyright and licensing notices are preserved. ## 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 Hypernets if… - Tags unique to Hypernets: hyperparameter-optimization, lightgbm, pytorch, sklearn. - If your project requires integration with TensorFlow, Keras, PyTorch, Scikit-Learn, LightGBM or XGBoost within a single AutoML pipeline - More recently updated (last pushed Apr 20, 2026). ### Choose autokeras if… - Tags unique to autokeras: autodl, deep-learning, machine-learning. - When your project involves deep learning tasks requiring minimal manual intervention in designing models. - More GitHub stars (9.3k vs 265) - visibility, not fit. ## When NOT to use Hypernets - If the project is limited to only traditional machine learning libraries without deep-learning needs, consider more specialized tools with narrower focus - Avoid if your team has strict time constraints; Hypernets' setup for domain-specific AutoML might require initial investment in understanding its abstraction layer ## 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 Hypernets and autokeras? Hypernets: A General Automated Machine Learning framework for building domain-specific AutoML toolkits.. autokeras: AutoML library for deep learning. See the comparison table for live GitHub stats and shared categories. ### When should I choose Hypernets over autokeras? Choose Hypernets over autokeras when Tags unique to Hypernets: hyperparameter-optimization, lightgbm, pytorch, sklearn; If your project requires integration with TensorFlow, Keras, PyTorch, Scikit-Learn, LightGBM or XGBoost within a single AutoML pipeline; More recently updated (last pushed Apr 20, 2026). ### When should I choose autokeras over Hypernets? Choose autokeras over Hypernets when Tags unique to autokeras: autodl, deep-learning, machine-learning; When your project involves deep learning tasks requiring minimal manual intervention in designing models; More GitHub stars (9.3k vs 265) - visibility, not fit. ### When should I avoid Hypernets? If the project is limited to only traditional machine learning libraries without deep-learning needs, consider more specialized tools with narrower focus Avoid if your team has strict time constraints; Hypernets' setup for domain-specific AutoML might require initial investment in understanding its abstraction layer ### 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 Hypernets or autokeras more popular on GitHub? autokeras has more GitHub stars (9,328 vs 265). Stars measure visibility, not whether either tool fits your constraints. ### Are Hypernets and autokeras open source? Yes - both are open-source projects on GitHub (Hypernets: Apache-2.0, autokeras: Apache-2.0). ### Where can I find alternatives to Hypernets or autokeras? GraphCanon lists graph-backed alternatives at [Hypernets alternatives](/tools/datacanvasio-hypernets/alternatives) and [autokeras alternatives](/tools/keras-team-autokeras/alternatives) ([Hypernets markdown twin](/tools/datacanvasio-hypernets/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/datacanvasio-hypernets-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, Hypernets or autokeras? Hypernets: Slowing. 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 Hypernets and autokeras? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Hypernets trust report](/tools/datacanvasio-hypernets/trust); [autokeras trust report](/tools/keras-team-autokeras/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datacanvasio-hypernets`](/api/graphcanon/graph?tool=datacanvasio-hypernets) - 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/_