Metadata-Version: 2.1
Name: rapidflow
Version: 0.1.8
Summary: rapidFlow - A framework to perform micro experimentation fast with easy scaling.
Home-page: https://github.com/gebauerm/rapidFlow
Download-URL: https://github.com/gebauerm/rapidFlow/archive/0.1.8.tar.gz
Author: Michael Gebauer
Author-email: gebauerm23@gmail.com
License: MIT
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: test
License-File: LICENSE

# rapidFlow

This is a project, that tries to accelerate micro research projects by providing a richer functionality for the
already known hpyerparameter optimization library [optuna](https://github.com/optuna/optuna). The code of optuna is not
modified, it is incorporated into rapidFlow to provide richer evaluation and easy parallel processing.

# Getting Started

## Prerequisites

* Python >= 3.7
* [PyTorch](https://pytorch.org/)

## Install

rapidFlow is build upon Pytorch, so make sure you have PyTorch installed.

1. From Pip
Install package with: \
    `pip install rapidflow`

2. With cloned repository
Install package with:
\
    `pip install -e /src`

# TODO:

* move experiment library to another repo
* experiments in docker container with gpu? (or singularity)
* test on multiple gpus
* testing and propper doku
* significance testing

# Acknowledgments
Feel free to contribute. If you use this repository please cite with:

        @misc{rapidFlow_geb,
        author = {Gebauer, Michael},
        title = {rapidFlow},
        year = {2022},
        publisher = {GitHub},
        journal = {GitHub repository},
        howpublished = {\url{https://github.com/gebauerm/model_storage}},
        }


# Author

[elysias](https://github.com/gebauerm)
