daskperiment 0.3.0

A lightweight tool to perform reproducible machine learning experiment using Dask.

Overview

daskperiment is a tool to perform reproducible machine learning experiment. It allows users to define and manage the history of trials (given parameters, results and execution environment).

The package is built on Dask, a package for parallel computing with task scheduling. Each experiment trial is internally expressed as Dask computation graph, and can be executed in parallel.

Benefits

• Usable in standard Python/Jupyter environment (and optionally with standard KVS).

  • No need to setting up server applications.

  • No registration to cloud services.

  • Not to be constrained by slightly customized Python shells.

• User-intuitive.

  • Minimizing modifications of existing codes.

  • Performing experiments using Dask compatible API.

  • Easily handle experiments history (with pandas basic operations).

  • Requires less work to manage with Git (no need to make branch per trials).

  • (Experimental) Web dashboard to manage trial history.

• Tracking experiment related information

  • Trial result and its (hyper) parameters.

  • Code context.

  • Environment information.

    • Device information

    • OS information

    • Python version

    • Installed Python packages and its version

    • Git information

• Reproducibility

  • Check function purity (each step should return the same output for the same inputs)

  • Automatic random seeding

• Auto saving and loading previous experiment history.

• Parallel execution of experiment steps.

• Sharing experiments.

  • Redis backend

Future Scope

• More efficient execution.

  • Omit execution if depending parameters are the same

  • Distributed execution