rapidflow 0.1.8rc0

# rapidFlow

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. 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

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

Collaboration

Branche	Purpose
main	production state
feature	a new feautre
hotfix	hotfix as there are no bugfixes as everything is created from master

The desired workflow is github flow. Meaning that: * we can deploy from master at any time * nothing gets deployed without a PR and its review * we have no releases or release branches

This way we maintain: * fast responses to features or bugs and continouus delivery * easy workflow * fast developer feedback

more to come!

Pipelines

PR:

• deplyos into develop and performs integration tests
  • 2 PRs are created at the same time --> their tests and deployment is scheduled over Runners in github or jenkins
  • 1 PR is merged --> if there is a dependency a merge conflict arises, which gets reslved by a new commit in the 2nd PR --> triggers pipeline

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