PyExperimenter

PyExperimenter: Easily distribute experiments and track results - Published in JOSS (2023)

https://github.com/tornede/py_experimenter

Science Score: 100.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 9 DOI reference(s) in README and JOSS metadata
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Academic publication links
Links to: joss.theoj.org
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Committers with academic emails
3 of 9 committers (33.3%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
Published in Journal of Open Source Software

Keywords

database executor experiments python

Last synced: 4 months ago · JSON representation ·

Repository

The PyExperimenter is a tool for the automatic execution of experiments, e.g. for machine learning (ML), capturing corresponding results in a unified manner in a database.

Basic Info

Host: GitHub
Owner: tornede
License: mit
Language: Python
Default Branch: develop
Homepage: https://tornede.github.io/py_experimenter/
Size: 7.6 MB

Statistics

Stars: 39
Watchers: 4
Forks: 3
Open Issues: 22
Releases: 11

Topics

database executor experiments python

Created over 4 years ago · Last pushed 5 months ago

Metadata Files

Readme Changelog License Citation Authors

PyExperimenter

PyExperimenter is a tool to facilitate the setup, documentation, execution, and subsequent evaluation of results from an empirical study of algorithms and in particular is designed to reduce the involved manual effort significantly. It is intended to be used by researchers in the field of artificial intelligence, but is not limited to those.

The empirical analysis of algorithms is often accompanied by the execution of algorithms for different inputs and variants of the algorithms (specified via parameters) and the measurement of non-functional properties. Since the individual evaluations are usually independent, the evaluation can be performed in a distributed manner on an HPC system. However, setting up, documenting, and evaluating the results of such a study is often file-based. Usually, this requires extensive manual work to create configuration files for the inputs or to read and aggregate measured results from a report file. In addition, monitoring and restarting individual executions is tedious and time-consuming.

These challenges are addressed by PyExperimenter by means of a single well defined configuration file and a central database for managing massively parallel evaluations, as well as collecting and aggregating their results. Thereby, PyExperimenter alleviates the aforementioned overhead and allows experiment executions to be defined and monitored with ease.

General schema of `PyExperimenter`.

For more details check out the PyExperimenter documentation:

Cite PyExperimenter

If you use PyExperimenter in a scientific publication, we would appreciate a citation in one of the following ways.

Citation String

Tornede et al., (2023). PyExperimenter: Easily distribute experiments and track results. Journal of Open Source Software, 8(84), 5149, https://doi.org/10.21105/joss.05149

BibTex

@article{Tornede2023, title = {{PyExperimenter}: Easily distribute experiments and track results}, author = {Tanja Tornede and Alexander Tornede and Lukas Fehring and Lukas Gehring and Helena Graf and Jonas Hanselle and Felix Mohr and Marcel Wever}, journal = {Journal of Open Source Software}, publisher = {The Open Journal}, year = {2023}, volume = {8}, number = {84}, pages = {5149}, doi = {10.21105/joss.05149}, url = {https://doi.org/10.21105/joss.05149} }

Owner

Login: tornede
Kind: user

Repositories: 2
Profile: https://github.com/tornede

JOSS Publication

PyExperimenter: Easily distribute experiments and track results

Published

April 20, 2023

DOI

10.21105/joss.05149

Volume 8, Issue 84, Page 5149