A feature-rich, Neural Architecture Search framework based on Artificial Bee Colony optimization

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Getting Started

HiveNAS (preprint) is a modular NAS framework that can find and optimize a neural architecture with state-of-the-art performance.

Installation

PyPi (recommended)

The Python package is hosted on the Python Package Index (PyPI).

The latest published version of HiveNAS can be installed using

pip install HiveNAS

Manual Installation

Simply clone the entire repo and extract the files in the HiveNAS folder, then import them into your project folder.

Or use one of the shorthand methods below

GIT

• cd into your project directory
• Use sparse-checkout to pull the library files only into your project directory

  git init HiveNAS
  cd HiveNAs
  git remote add -f origin https://github.com/ThunderStruct/HiveNAS.git
  git config core.sparseCheckout true
  echo "HiveNAS/*" >> .git/info/sparse-checkout
  git pull --depth=1 origin master

• Import the newly pulled files into your project folder

SVN

• cd into your project directory
• checkout the library files

  svn checkout https://github.com/ThunderStruct/HiveNAS/trunk/HiveNAS

• Import the newly checked out files into your project folder

Documentation

Detailed examples and the full API docs are hosted on Read the Docs.

License

This project is licensed under the MIT License - see the LICENSE file for details