Metadata-Version: 1.0
Name: daskperiment
Version: 0.4.0
Summary: A lightweight tool to perform reproducible machine learning experiment using Dask.
Home-page: http://daskperiment.readthedocs.org/en/stable
Author: sinhrks
Author-email: sinhrks@gmail.com
License: BSD
Description: daskperiment
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        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
        
Platform: UNKNOWN
