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        "description": "daskperiment\n============\n\n.. image:: https://img.shields.io/pypi/v/daskperiment.svg\n  :target: https://pypi.python.org/pypi/daskperiment/\n.. image:: https://readthedocs.org/projects/daskperiment/badge/?version=latest\n  :target: http://daskperiment.readthedocs.org/en/latest/\n  :alt: Latest Docs\n.. image:: https://travis-ci.org/sinhrks/daskperiment.svg?branch=master\n  :target: https://travis-ci.org/sinhrks/daskperiment\n.. image:: https://codecov.io/gh/sinhrks/daskperiment/branch/master/graph/badge.svg\n  :target: https://codecov.io/gh/sinhrks/daskperiment\n\nOverview\n~~~~~~~~\n\n`daskperiment` is a tool to perform reproducible machine learning experiment.\nIt allows users to define and manage the history of trials\n(given parameters, results and execution environment).\n\nThe package is built on `Dask`, a package for parallel computing with task\nscheduling. Each experiment trial is internally expressed as `Dask` computation\ngraph, and can be executed in parallel.\n\nBenefits\n~~~~~~~~\n\n- Compatibility with standard Python/Jupyter environment (and optionally with standard KVS).\n\n  - No need to set up server applications\n  - No need to registrate on any cloud services\n  - Run on standard / customized Python shells\n\n- Intuitive user interface\n\n  - Few modifications on existing codes are needed\n  - Trial histories are logged automatically (no need to write additional codes for logging)\n  - `Dask` compatible API\n  - Easily accessible experiments history (with `pandas` basic operations)\n  - Less managiment works on Git (no need to make branch per trials)\n  - (Experimental) Web dashboard to manage trial history\n\n- Traceability of experiment related information\n\n  - Trial result and its (hyper) parameters.\n  - Code contexts\n  - Environment information\n\n    - Device information\n    - OS information\n    - Python version\n    - Installed Python packages and its version\n    - Git information\n\n- Reproducibility\n\n  - Check function purity (each step should return the same output for the same inputs)\n  - Automatic random seeding\n\n- Auto saving and loading of previous experiment history\n- Parallel execution of experiment steps\n- Experiment sharing\n\n  - Redis backend\n  - MongoDB backend\n\nFuture Scope\n~~~~~~~~~~~~\n\n- More efficient execution.\n\n  - Omit execution if depending parameters are the same\n  - Distributed execution\n\n\n",
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