Metadata-Version: 2.4
Name: L2LforHPC
Version: 2.0.0b0
Summary: L2L (Learning-to-learn) contains implementations of various gradient free optimization algorithms. It also makes it easy to optimize (hyper-)parameters for any task.
Author: Anand Subramoney, Arjun Rao, Sandra Diaz-Pier, Franz Scherr, Darjan Salaj, Jakob Jordan, Nikolaus Kopp, Daniel Hackhofer, Sinisa Stekovic, Thomas Bohnstingl, Alper Yegenoglu, Michiel van der Vlag, Aaron Perez Martin, Hanna Mohr, Daniel Todt, Johannes Wilhelm, Aitor Morales-Gregorio, Cristian Jimenez-Romero, Thorsten Hater, Wouter Klijn, Pier Stanislao Paolucci, Elena Pastorelli
License: GPL-3.0-only
Project-URL: Homepage, https://github.com/Meta-optimization/L2L
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/x-rst
License-File: LICENSE
License-File: AUTHORS
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pyyaml
Requires-Dist: sphinx
Requires-Dist: sphinxjp.themes.basicstrap
Requires-Dist: sphinx-rtd-theme
Requires-Dist: jinja2
Requires-Dist: gitpython
Requires-Dist: scikit-learn
Requires-Dist: flake8
Requires-Dist: deap
Dynamic: license-file

L2L Gradient-free Optimization Framework
++++++++++++++++++++++++++++++++++++++++

.. image:: https://travis-ci.org/Meta-optimization/L2L.svg?branch=master
    :target: https://travis-ci.org/Meta-optimization/L2L

.. image:: https://app.circleci.com/pipelines/github/Meta-optimization/badge.svg
    :target: https://app.circleci.com/pipelines/github/Meta-optimization

.. image:: https://circleci.com/gh/Meta-optimization/L2L.svg?style=svg
    :target: https://circleci.com/gh/Meta-optimization/L2L

.. image:: https://coveralls.io/repos/github/Meta-optimization/L2L/badge.svg?branch=master
    :target: https://coveralls.io/github/Meta-optimization/L2L?branch=master

    

About
*****

The L2L (Learning-to-learn) gradient-free optimization framework contains well documented and tested implementations of various gradient free optimization algorithms. It also defines an API that makes it easy to optimize (hyper-)parameters for any task (optimizee). All the implementations in this package are parallel and can run across different cores and nodes (but equally well on a single core).

NOTE: The L2L framework is currently in **BETA**

Getting Started
***************


If you are developing a new Optimizee or want to try out a new Optimizee with the Optimizers in the L2L package, install
L2L as a python package. See section `Installing the L2L Package`_ for details on how to install the package (this
automatically installs all requirements).

Documentation is available at `<https://meta-optimizationl2l.readthedocs.io/en/latest/>`_.


Installing the L2L Package
**************************

From the Top-Level directory of the directory, run the following command:

     python -m pip install -e .

or alternatively (if you do not use a virtual environment)

    python -m pip install -e . --user

The `--user` flag is to be used if you wish to install in the user path as 
opposed to the root path. However, we **recommend to use a virtual enviornment**, 
such as the standard Python env or conda env.

The above will install the package by creating symlinks to the code files in the
relevant directory containing python modules. This means that you can change any
of the code files and see the changes reflected in the package immediately (i.e.
without requiring a reinstall). In order to uninstall one may run the following:

    pip uninstall L2L 

*Note that if the setup was done using sudo access, then the uninstall must also
be done using sudo access*

Having installed this package, we now have access to the top level `l2l` module
which contains all the relevant modules relevant for using the l2l package.

This should also install the `sphinx` package which should now enable you to build
the documentation as specified below.


Building Documentation
**********************
Run the following command from the `doc` directory

    make html

And open the documentation with

   firefox _build/html/index.html

All further (and extensive) documentation is in the html documentation!


Invoking the tests
******************

To run the tests go to the folder `l2l/tests` and execute: 

    python test_all.py
    
If the package nosetests is installed run on the top folder (L2L):

     nosetests -v --with-coverage --cover-package=l2l/

Contributing to L2L
*******************

By contributing to this code you confirm that you understand that L2L follows an open development approach and is distributed under a GPL-3.0 license. This means you have also made sure that all your contributed code meets the requirements detailed in the contributing guidelines and does not breach the license of any other library or source of code.
