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    "info": {
        "author": "James Santucci",
        "author_email": "james.santucci@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "=======\ntaggedartifacts\n=======\n\n\n.. image:: https://img.shields.io/pypi/v/taggedartifacts.svg\n        :target: https://pypi.python.org/pypi/taggedartifacts\n\n.. image:: https://circleci.com/gh/jisantuc/taggedartifacts.svg?style=svg\n    :target: https://circleci.com/gh/jisantuc/taggedartifacts\n\n.. image:: https://pyup.io/repos/github/jisantuc/taggedartifacts/shield.svg\n     :target: https://pyup.io/repos/github/jisantuc/taggedartifacts/\n     :alt: Updates\n\n\n\nSimple artifact versioning and caching for scientific workflows\n\n\n* Free software: MIT license\n* Documentation: https://taggedartifacts.readthedocs.io.\n\n\nFeatures\n--------\n\n:code:`taggedartifacts` exists to provide a simple interface for versioning functions that produce artifacts.\nAn \"artifact\" could be anything -- maybe you have some sort of ETL pipeline that writes intermediate files,\nor you have a plotting function that writes a bunch of plots to disk, or you have a machine learning\nworkflow that produces a bunch of model files somewhere. The purpose of :code:`taggedartifacts` is to allow you\nto write normally -- give your output its regular name, like :code:`plot.png` -- and automatically attach\ngit commit and configuration information as part of the path.\n\nExample\n-------\n\nThe following example shows how to use :code:`taggedartifacts` to tag an output file with commit and config info:\n\n.. codeblock:: python\n\n    from taggedartifacts import Artifact\n    @Artifact(keyword='outpath', config={}, allow_dirty=True)\n    def save_thing(outpath):\n        with open(outpath, 'w') as outf:\n            outf.write('good job')\n\n\n    save_thing(outpath='foo.txt')\n\nThe resulting file that would be created would be :code:`foo-<commit>-<config-hash>.txt`, without having to\nlitter string formats and fetching git commit info throughout the code.\n\nWhy\n---\n\nIt's really easy, once you start running a lot of experiments, to end up with a ton of output files\nproduced at different times with names like :code:`plot.png`, :code:`plot2.png`, :code:`plot-please-work.png`, etc.\nLater, you'll maybe want to show someone a plot, and they'll try to reproduce it, and you won't be\nable to tell them the state of the code when the plot was produced. That's not great! :code:`taggedartifacts`\noffers one solution to this problem, where you can tell at a glance whether two files were produced\nby the same code and the same configuration.\n\nIsn't this just another workflow library\n----------------------------------------\n\nIt's not! I promise.\n\nThe workflow library ecosystem in python already has a lot of entrants, like Luigi_, Airflow_, \nPinball_, and probably many I haven't heard of. There are also experiment and data/code versioning systems\naround like DVC_, and older solutions to DAGs that understand how not to redo work, like :code:`make`. :code:`taggedartifacts`\nisn't really like any of those. It isn't aware of a DAG of all of your tasks at any point, and it doesn't\nknow anything about data science workflows in general. It only knows about tagging some sort of file-based\noutput with git commit and configuration information so that you can tell whether two artifacts produced\npotentially on different computers should match.\n\nAs a result, you don't have to have a separate daemon running, you don't get anything like task\ndistribution and parallelization for free, and you don't get a special CLI. :code:`taggedartifacts` only attempts to\nsolve one problem.\n\n.. _Luigi: https://github.com/spotify/luigi\n.. _Airflow: https://github.com/apache/airflow\n.. _Pinball: https://github.com/pinterest/pinball\n.. _DVC: https://github.com/iterative/dvc\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n=======\nHistory\n=======\n\n0.1.1 (2019-04-27)\n------------------\n\n* Renamed to avoid pypi name conflict. \n\n0.1.0 (2019-04-27)\n------------------\n\n* First release on PyPI.\n\n\n",
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        "name": "taggedartifacts",
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