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HpBandSter [![Build Status](https://travis-ci.org/automl/HpBandSter.svg?branch=master)](https://travis-ci.org/automl/HpBandSter)  [![codecov](https://codecov.io/gh/automl/HpBandSter/branch/master/graph/badge.svg)](https://codecov.io/gh/automl/HpBandSter)\na distributed Hyperband implementation on Steroids\n\n## News: Not Maintained Anymore!\n\nPlease note that we don't maintain this repository anymore. We also cannot ensure that we can reply to issues in the issue tracker or look into PRs. \n\nWe offer two successor  packages which showed in our [HPOBench paper](https://arxiv.org/abs/2109.06716) superior performance:\n\n1. [SMAC3](https://github.com/automl/SMAC3): is a versatile HPO package with different HPO strategies. It also implements the main idea of BOHB, but uses a RF (or GP) as a predictive model instead of a KDE.\n2. [DEHB](https://github.com/automl/dehb): is a HPO package using a combination of differential evolution and hyperband. \n\nIn particular, SMAC3 has an active group of developers working on it and maintaining it. So, we strongly recommend using one of these two packages instead of HPBandSter.\n\n## Overview\n\nThis python 3 package is a framework for distributed hyperparameter optimization.\nIt started out as a simple implementation of [Hyperband (Li et al. 2017)](http://jmlr.org/papers/v18/16-558.html), and contains\nan implementation of [BOHB (Falkner et al. 2018)](http://proceedings.mlr.press/v80/falkner18a.html)\n\n## How to install\n\nWe try to keep the package on PyPI up to date. So you should be able to install it via:\n```\npip install hpbandster\n```\nIf you want to develop on the code you could install it via:\n\n```\npython3 setup.py develop --user\n```\n\n## Documentation\n\nThe documentation is hosted on github pages: [https://automl.github.io/HpBandSter/](https://automl.github.io/HpBandSter/)\nIt contains a quickstart guide with worked out examples to get you started in different circumstances.\nCheck it out if you are interest in applying one of the implemented optimizers to your problem.\n\nWe have also written a [blogpost](https://www.automl.org/blog_bohb/) showcasing the results from our ICML paper.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/automl.github.io%2FHpBandSter%2F","html_url":"https://awesome.ecosyste.ms/projects/automl.github.io%2FHpBandSter%2F","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/automl.github.io%2FHpBandSter%2F/lists"}