View on GitHub

BOML

Bilevel Optimization Library in Python for Multi-Task and Meta Learning

Welcome to the documentation for BOML

Contents

  1. Introduction
  2. Installation and Requirements
  3. Quickly Build Your Bilevel Meta-Learning Model
  4. Modification and Extension
  5. Authors and Liscense

Introduction

BOML is a bilevel optimization library in Python for meta learning. Before reading the documentation, you could refer to View on GitHub for a brief introduction about meta learning and BOML.
Here we provide detailed instruction to quickly get down to your research and test performance of popular algorithms and new ideas.

Installation and requirements

BOML implements various meta learning approaches based on TensorFlow, which is one of the most popular macheine learning platform. Besides, Numpy and basical image processing modules are required for installation.
We also provide requirements.txt as reference for version control. BOML requires Python 3.5+ and TensorFlow 1.13+.

  1. Install from GitHub page:

  git clone https://github.com/liuyaohua918/boml.git

  python setup.py install 
  
  or
  
  pip install -r requirements.txt

  2. use pip instruction

  pip install boml
  
  or
  
  pip install --upgrade boml

Quickly build your bilevel meta-learning model

Modification and extension

Authors and license

MIT License

Copyright (c) 2020 Yaohua Liu

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.