automl/HpBandSter automl/HpBandSter

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    609
  • Rank 73,614 (Top 2 %)
  • Language
    Python
  • License
    BSD 3-Clause "New...
  • Created almost 7 years ago
  • Updated about 2 years ago
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automl/HpBandSter
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Repository Details

a distributed Hyperband implementation on Steroids

HpBandSter Build Status codecov

a distributed Hyperband implementation on Steroids

News: Not Maintained Anymore!

Please 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.

We offer two successor packages which showed in our HPOBench paper superior performance:

  1. 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.
  2. DEHB: is a HPO package using a combination of differential evolution and hyperband.

In 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.

Overview

This python 3 package is a framework for distributed hyperparameter optimization. It started out as a simple implementation of Hyperband (Li et al. 2017), and contains an implementation of BOHB (Falkner et al. 2018)

How to install

We try to keep the package on PyPI up to date. So you should be able to install it via:

pip install hpbandster

If you want to develop on the code you could install it via:

python3 setup.py develop --user

Documentation

The documentation is hosted on github pages: https://automl.github.io/HpBandSter/ It contains a quickstart guide with worked out examples to get you started in different circumstances. Check it out if you are interest in applying one of the implemented optimizers to your problem.

We have also written a blogpost showcasing the results from our ICML paper.

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