Hypernets
We Are Hiring!
Dear folks, we are opening several precious positions based in Beijing both for professionals and interns avid in AutoML/NAS, please send your resume/cv to [email protected]. (Application deadline: TBD.)
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. 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.
Overview
Conceptual Model
Illustration of the Search Space
Installation
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
Related Links
Hypernets related projects
- 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.
Documents
Neural Architecture Search
DataCanvas
Hypernets is an open source project created by DataCanvas.