Overview

decu stands for “Decu is a Experimental Computation Utility”. decu is a suite of command line tools to automate the menial tasks involved in the development of experimental computation projects.

We define a “experimental computation” script as a script that reads some data, performs an experiment (run an algorithm, train a model, plot a figure, etc), saves some results to disk, and then quits. Some tasks that are usually involved in this are:

1. file I/O: choosing file names for results and plots
2. multiprocessing: running many experiments/scripts in parallel
3. timekeeping: how long algorithms take to run
4. cloud service integration
5. logging

decu was born from the realization that none of these tasks have anything to do with (and in fact get in the way of) the actual experimentation being done. Furthermore, experimental computation scripts tend to mix together code dedicated to these tasks with code intended to run the actual algorithms of interest, thus making said algorithms harder to maintain and debug.

The main goal of decu is to provide standardization and automation of these tasks, with end code that clearly separates experimental computation from bookkeeping and other auxiliary code.

decu is built with Best Practices for Data Science, by Greg Wilson et al, in mind. See 1 and 2.

Documentation

• Quick Start
  • Installation
  • Start a new project
  • Development
  • Inspection
  • We're done!
• Tutorial
  • Example Project
    • Project organization: decu init
    • Project bookkeeping: decu exec
      • Simple run
      • Multiple runs
      • Figures
    • Project debugging: decu inspect
• Customizing decu
  • The configuration file
  • How the config file is read
  • Configuration options
• Reference
  • core.py
  • io.py
  • logging.py
  • config.py