Hypernets
A General Automated Machine Learning framework for building domain-specific AutoML toolkits.
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Decision brief
Hypernets is an AutoML framework supporting multiple ML frameworks for end-to-end AutoML solutions in specific domains.
Good fit when
- If your project requires integration with TensorFlow, Keras, PyTorch, Scikit-Learn, LightGBM or XGBoost within a single AutoML pipeline
- When you need advanced optimization algorithms and support for multi-objective problems such as MOEA/D and NSGA-II
Avoid when
- 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
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
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- Security (OSV)
- 14 low (14 low)
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Install
pip install HypernetsSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Hypernets is an automated machine learning framework that simplifies the development of end-to-end AutoML solutions across various domains by supporting hyperparameter optimization and neuro-architecture search among other advanced optimization techniques. It supports multiple ML frameworks including TensorFlow, Keras, PyTorch, SciKit-Learn, LightGBM, and XGBoost.
Capability facts
- Languages
- python
Source: github.language · Aug 4, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 4, 2026)
python -m hypernets.examples.smoke_testingSource link
Tags
README
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Hypernets: A General Automated Machine Learning Framework
Hypernets is a general AutoML framework, based on which it can implement automatic optimization tools for various machine learning frameworks and libraries, including deep learning frameworks such as tensorflow, keras, pytorch, and machine learning libraries like sklearn, lightgbm, xgboost, etc. It also adopted various state-of-the-art optimization algorithms, including but not limited to evolution algorithm, monte carlo tree search for single objective optimization and multi-objective optimization algorithms such as MOEA/D,NSGA-II,R-NSGA-II. We introduced an abstract search space representation, taking into account the requirements of hyperparameter optimization and neural architecture search(NAS), making Hypernets a general framework that can adapt to various automated machine learning needs. As an abstraction computing layer, tabular toolbox, has successfully implemented in various tabular data types: pandas, dask, cudf, etc.
Overview
Conceptual Model
Illustration of the Search Space
What's NEW !
- New feature: Multi-objectives optimization support
- New feature: Performance and model complexity measurement metrics
- New feature: Distributed computing and GPU acceleration base on computational abstraction layer
Installation
Conda
Install Hypernets with conda from the channel conda-forge:
conda install -c conda-forge hypernets
Pip
Install Hypernets with different options:
- Typical installation:
pip install hypernets
- To run Hypernets in JupyterLab/Jupyter notebook, install with command:
pip install hypernets[notebook]
- To run Hypernets in distributed Dask cluster, install with command:
pip install hypernets[dask]
- To support dataset with simplified Chinese in feature generation,
- Install
jiebapackage before running Hypernets. - OR install Hypernets with command:
- Install
pip install hypernets[zhcn]
- Install all above with one command:
pip install hypernets[all]
To Verify your installation:
python -m hypernets.examples.smoke_testing
Related Links
Documents
Neural Architecture Search
- Define A DNN Search Space
- [Define A
For agents
This page has a .md twin and JSON over the API.