Metadata-Version: 1.0
Name: boml
Version: 0.1.0
Summary: A Bilevel Optimizer Library in Python for Meta Learning
Home-page: https://github.com/dut-media-lab/BOML
Author: Yaohua Liu, Risheng Liu
Author-email: liuyaohua@mail.dlut.edu.cn
License: MIT
Description: .. BOML documentation master file, created by
           sphinx-quickstart on Mon Sep  7 09:30:26 2020.
           You can adapt this file completely to your liking, but it should at least
           contain the root `toctree` directive.
        
        Welcome to BOML's documentation!
        ================================
        **Configuration & Status**
        
        .. image:: https://travis-ci.com/dut-media-lab/BOML.svg?branch=master
           :target: https://github.com/dut-media-lab/BOML
           :alt: build status
        
        .. image:: https://codecov.io/gh/dut-media-lab/BOML/branch/master/graph/badge.svg
           :target: https://github.com/dut-media-lab/BOML
           :alt: codecov
        	
        .. image:: https://readthedocs.org/projects/pybml/badge/?version=latest
           :target: https://github.com/dut-media-lab/BOML
           :alt: Documentation Status
        	
        .. image:: https://img.shields.io/badge/license-MIT-000000.svg
           :target: https://github.com/dut-media-lab/BOML
           :alt: License
        	
        .. image:: https://img.shields.io/github/languages/top/dut-media-lab/BOML
           :target: https://github.com/dut-media-lab/BOML
           :alt: Language
        	
        .. image:: https://img.shields.io/badge/code%20style-black-000000.svg
           :target: https://github.com/dut-media-lab/BOML
           :alt: Code style: black
        
        BOML is a modularized optimization library that unifies several ML algorithms into a common bilevel optimization framework. It provides interfaces to implement popular bilevel optimization algorithms, so that you could quickly build your own meta learning neural network and test its performance.
        
        **Key features of BOML**
        
        - **Unified bilevel optimization framework** to address different categories of existing meta-learning paradigms. 
        - **Modularized algorithmic structure** to integrate a variety of optimization techniques and popular methods.
        - **Unit tests with Travis CI and Codecov** to reach 99% coverage, and following **PEP8 naming convention** to guarantee the code quality. 
        - **Comprehensive documentations** using sphinx and **flexible functional interfaces** similar to conventional optimizers to help researchers quickly get familiar with the procedures.
        
        **Optimization Routine**
        
        The figure below illustrates the general optimization routine by organized modules in BOML.
        
        .. image:: https://github.com/dut-media-lab/BOML/blob/master/figures/p2.png
        	:alt: Bilevel Optimization Routine
        	:align: center
        
        
        **Related Links**
        
        * `Go to the project home page <https://github.com/dut-media-lab/BOML>`_
        * `Download the latest code bundle <https://codeload.github.com/dut-media-lab/BOML/zip/master>`_
        
Platform: UNKNOWN
