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epyc is a library to manage large sets of computational experiments.

Computational science often needs us to perform a lot of repetitions of experiments, for example to reduce variance or to cancel out strange artefacts. Typically one might also want to perform a set of experiments across a parameter space, varying one or more parameters to see their effect. All these tasks generate a lot of repetitions that need to be managed while they’re on-going, and a lot of result data that needs to be kept archived.

These activities are so structured, so repetitive, and so common, that they cry out for automation — but there didn’t seem to be an automation library for doing this sort of thing in Python. So I wrote one.

epyc is being used to manage experiments within my complex networks blog/book, but it can manage any kind of experiment written in Python, and can run experiments in parallel using an IPython compute cluster, or locally taking advantage of a multicore workstation.

Github repo

Install from PyPi

Read the API documentation