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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. As an abstraction computing layer, tabular toolbox, has successfully implemented in various tabular data types: pandas, dask, cudf, etc.
Install Hypernets with conda
from the channel conda-forge:
conda install -c conda-forge hypernets
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
jieba
package before running Hypernets. - OR install Hypernets with command:
- Install
pip install hypernets[zhcn]
- Install all above with one command:
pip install hypernets[all]
Verify installation:
python -m hypernets.examples.smoke_testing
- Hypernets: A general automated machine learning (AutoML) framework.
- HyperGBM: A full pipeline AutoML tool integrated various GBM models.
- HyperDT/DeepTables: An AutoDL tool for tabular data.
- HyperTS: A full pipeline AutoML&AutoDL tool for time series datasets.
- HyperKeras: An AutoDL tool for Neural Architecture Search and Hyperparameter Optimization on Tensorflow and Keras.
- HyperBoard: A visualization tool for Hypernets.
- Cooka: Lightweight interactive AutoML system.
Hypernets is an open source project created by DataCanvas.