A Neural Architecture Search framework based on Artificial Bee Colony optimization
Project description
A feature-rich, Neural Architecture Search framework based on Artificial Bee Colony optimization
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 directorygit 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 directorycheckout
the library filessvn 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
Project details
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