Metadata-Version: 2.1
Name: hypernets
Version: 0.2.3.2
Summary: An General Automated Machine Learning Framework
Home-page: UNKNOWN
Author: DataCanvas Community
Author-email: yangjian@zetyun.com
License: Apache License 2.0
Description: # Hypernets
        
        [![Python Versions](https://img.shields.io/pypi/pyversions/hypernets.svg)](https://pypi.org/project/hypernets)
        [![Downloads](https://pepy.tech/badge/hypernets)](https://pepy.tech/project/hypernets)
        [![PyPI Version](https://img.shields.io/pypi/v/hypernets.svg)](https://pypi.org/project/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 yangjian@zetyun.com. (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
        <p align="center">
        <img src="docs/source/images/hypernets_conceptual_model.png" width="100%"/>
        </p>
        
        ### Illustration of the Search Space 
        <p align="center">
        <img src="docs/source/images/hypernets_search_space.png" width="100%"/>
        </p>
        
        
        ## Installation
        
        Install Hypernets with `pip` command:
         
        ```bash
        pip install hypernets
        ```
        
        Optional, to run Hypernets in JupyterLab notebooks, install Hypernets and JupyterLab with command:
        ```bash
        pip install hypernets[notebook]
        ```
        
        Optional, to run Hypernets in distributed Dask cluster, install Hypernets with command:
        ```bash
        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:
        ```bash
        pip install hypernets[zhcn]
        ```
        
        Optional, install all Hypernets components and dependencies with one command:
        ```bash
        pip install hypernets[all]
        ```
        
        
        ***Verify installation***:
        ```bash
        python -m hypernets.examples.smoke_testing
        ```
        
        ## Related Links
        
        * [A Brief Tutorial for Developing AutoML Tools with Hypernets](https://github.com/BochenLv/knn_toy_model/blob/main/hypertoy/intro.md)
        
        ## Hypernets related projects
        
        * [HyperGBM](https://github.com/DataCanvasIO/HyperGBM): A full pipeline AutoML tool integrated various GBM models.
        * [HyperDT/DeepTables](https://github.com/DataCanvasIO/DeepTables): An AutoDL tool for tabular data.
        * [HyperKeras](https://github.com/DataCanvasIO/HyperKeras): An AutoDL tool for Neural Architecture Search and Hyperparameter Optimization on Tensorflow and Keras.
        * [Cooka](https://github.com/DataCanvasIO/Cooka): Lightweight interactive AutoML system.
        * [Hypernets](https://github.com/DataCanvasIO/Hypernets): A general automated machine learning framework.
        
        ![DataCanvas AutoML Toolkit](docs/source/images/datacanvas_automl_toolkit.png)
        
        ## Documents
        * [Overview](https://hypernets.readthedocs.io/en/latest/overview.html)
        * [QuickStart](https://hypernets.readthedocs.io/en/latest/quick_start.html)
        * [Search Space](https://hypernets.readthedocs.io/en/latest/search_space.html)
        * [Searcher](https://hypernets.readthedocs.io/en/latest/searchers.html)
        * [HyperModel](https://hypernets.readthedocs.io/en/latest/hypermodels.html)
        * [Experiment](https://hypernets.readthedocs.io/en/latest/experiment.html)
        ## Neural Architecture Search
        * [Define A DNN Search Space](https://hypernets.readthedocs.io/en/latest/nas.html#define-a-dnn-search-space)
        * [Define A CNN Search Space](https://hypernets.readthedocs.io/en/latest/nas.html#define-a-cnn-search-space)
        * [Define An ENAS Micro Search Space](https://hypernets.readthedocs.io/en/latest/nas.html#define-an-enas-micro-search-space)
        
        
        ## DataCanvas
        Hypernets is an open source project created by [DataCanvas](https://www.datacanvas.com/). 
        
Platform: UNKNOWN
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6.*
Description-Content-Type: text/markdown
Provides-Extra: notebook
Provides-Extra: zhcn
Provides-Extra: dask
Provides-Extra: tests
Provides-Extra: extra
Provides-Extra: all
