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PyExperimenter: Easily Distribute Experiments and Track Results

MCML Authors

Abstract

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.<br>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.<br>PyExperimenter adresses theses challenges 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.

article

The Journal of Open Source Software

8.86. Apr. 2023.

Authors

T. Tornede • A. Tornede • L. Fehring • L. Gehring • H. Graf • J. Hanselle • F. Mohr • M. Wever

Links

DOI

Research Area

 A3 | Computational Models

BibTeXKey: TTF+23

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