We introduce the Python Experiment Suite, an open source software tool written in Python, that supports scientists, engineers and others to conduct automated generic software experiments on a larger scale with numerous features: parameter ranges and combinations can be evaluated automatically, where different experiment architectures (e.g. grid search) are available. The suite also takes care of logging results into files, can handle experiment interruption and continuation, for instance after process termination by power failure, supports execution on multiple cores and contains a convenient Python interface to retrieve the stored results. Configuration files ease the setup of complex experiments without modifying code, and various run-time options allow for a variety of use cases.
[1]
Jürgen Schmidhuber,et al.
Active Learning with Adaptive Grids
,
2001,
ICANN.
[2]
Radford M. Neal.
Pattern Recognition and Machine Learning
,
2007,
Technometrics.
[3]
Travis E. Oliphant,et al.
Python for Scientific Computing
,
2007,
Computing in Science & Engineering.
[4]
Hans Petter Langtangen,et al.
Python scripting for computational science
,
2004
.
[5]
David A. Cohn,et al.
Improving generalization with active learning
,
1994,
Machine Learning.
[6]
John K. Ousterhout,et al.
Scripting: Higher-Level Programming for the 21st Century
,
1998,
Computer.
[7]
David M. Beazley,et al.
Building Flexible Large-Scale Scientific Computing Applications with Scripting Languages
,
1997,
PPSC.