The Datumbox framework is written in Java for developing machine learning and statistical applications.
Open source
24 alternativesHypernets
Hypernets offers a general automated machine learning framework for developing end to end AutoML toolkits in specific domains.
About Hypernets
Hypernets is an open-source framework for developing end-to-end AutoML tools for specific domains. It provides an abstract search space representation for hyperparameter optimization and neural architecture search, and can be used to build optimization tools for TensorFlow, Keras, PyTorch, scikit-learn, LightGBM, and XGBoost.
It includes single-objective optimizers such as evolution algorithms and Monte Carlo tree search, and multi-objective optimizers such as MOEA/D, NSGA-II, and R-NSGA-II. It also supports tabular data types including pandas, dask, and cudf, along with distributed computing and GPU acceleration.
Key features
- Supports hyperparameter optimization and neural architecture search
- Works with TensorFlow, Keras, PyTorch, sklearn, LightGBM, and XGBoost
- Includes evolution and Monte Carlo tree search optimizers
- Supports multi-objective algorithms like MOEA/D, NSGA-II, R-NSGA-II
- Handles tabular data types pandas, dask, and cudf
- Supports distributed computing and GPU acceleration
Best for: Developers building domain-specific AutoML tools on existing machine learning frameworks.
Good for
- Open source framework for building domain-specific AutoML tools
- Compatible with multiple deep learning and machine learning libraries
- Related tools available, including HyperGBM, HyperTS, and HyperKeras
Hypernets at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Good for
- Students
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Hypernets FAQ
How can Hypernets be installed?
With conda using 'conda install -c conda-forge hypernets', or with pip using 'pip install hypernets' with optional extras for notebook, dask, zhcn, or all.
Which machine learning libraries can it work with?
Deep learning frameworks such as TensorFlow, Keras, and PyTorch, and machine learning libraries such as sklearn, LightGBM, and XGBoost.
What related projects are built with Hypernets?
HyperGBM for GBM models, HyperDT/DeepTables for tabular data, HyperTS for time series, HyperKeras for TensorFlow and Keras, plus HyperBoard and Cooka.
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