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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. 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.
Install Hypernets with pip command:
pip install hypernets
Optional, to run Hypernets in JupyterLab notebooks, install Hypernets and JupyterLab with command:
pip install hypernets[notebook]
Optional, to run Hypernets in distributed Dask cluster, install Hypernets with command:
pip install hypernets[dask]
Optional, to support dataset with simplified Chinese in feature generation, install jieba package before run Hypernets, or install Hypernets with command:
pip install hypernets[zhcn]
Optional, install all Hypernets components and dependencies with one command:
pip install hypernets[all]
Verify installation:
python -m hypernets.examples.smoke_testing
- HyperGBM: A full pipeline AutoML tool integrated various GBM models.
- HyperDT/DeepTables: An AutoDL tool for tabular data.
- HyperKeras: An AutoDL tool for Neural Architecture Search and Hyperparameter Optimization on Tensorflow and Keras.
- Cooka: Lightweight interactive AutoML system.
- Hypernets: A general automated machine learning framework.
Hypernets is an open source project created by DataCanvas.