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        "author": "Max Plack Institute for Software Systems, Data-ken Research",
        "author_email": "jeff@data-ken.org",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows :: Windows 10",
            "Operating System :: POSIX",
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        "description": "===============\nData Workspaces\n===============\nData Workspaces is an open source framework for maintaining the\nstate of a data science project, including data sets, intermediate\ndata, results, and code. It supports reproducability through snapshotting\nand lineage models and collaboration through a push/pull model\ninspired by source control systems like Git.\n\nData Workspaces is installed as a Python 3 package and provides a\nGit-like command line interface and programming APIs. Specific data\nscience tools and workflows are supported through extensions\ncalled *kits*. The goal is to provide the reproducibility and collaboration\nbenefits with minimal changes to your current projects and processes.\n\nData Workspaces runs on\nUnix-like systems, including Linux, MacOS, and on Windows via the\nWindows Subsystem for Linux.\n\n.. image:: https://travis-ci.org/data-workspaces/data-workspaces-core.svg?branch=master\n\nQuick Start\n===========\nHere is a quick example to give you a flavor of the project, using\n`scikit-learn <https://scikit-learn.org>`_\nand the famous digits dataset running in a Jupyter Notebook.\n\nFirst, install the libary::\n\n  pip install dataworkspaces\n\nNow, we will create a workspace::\n\n  mkdir quickstart\n  cd ./quickstart\n  dws init --create-resources code,results\n\nThis created our *workspace* (which is a git repository under the covers)\nand initialized it with two subdirectories,\none for the source code, and one for the results. These are special\nsubdirectories, in that they are *resources* which can be tracked and versioned\nindependently.\n\nNow, we are going to add our source data to the workspace. This resides in an\nexternal, third-party git repository. It is simple to add::\n\n  git clone https://github.com/jfischer/sklearn-digits-dataset.git\n  dws add git --role=source-data --read-only ./sklearn-digits-dataset\n\nThe first line (``git clone ...``) makes a local copy of the Git repository for the\nDigits dataset. The second line (``dws add git ...``) adds the repository to the workspace\nas a resource to be tracked as part of our project.  The ``--role`` option tells Data\nWorkspaces how we will use the resource (as source data), and the ``--read-only``\noption indicates that we should treat the repository as read-only and never try to\npush it to its ``origin`` (as you do not have write permissions to the ``origin``\ncopy of this repository).\n\nNow, we can create a Jupyter notebook for running our experiments::\n\n  cd ./code\n  jupyter notebook\n\nThis will bring up the Jupyter app in your brower. Click on the *New*\ndropdown (on the right side) and select \"Python 3\". Once in the notebook,\nclick on the current title (\"Untitled\", at the top, next to \"Jupyter\")\nand change the title to ``digits-svc``.\n\nNow, type the following Python code in the first cell::\n\n  import numpy as np\n  from os.path import join\n  from sklearn.svm import SVC\n  from dataworkspaces.kits.scikit_learn import load_dataset_from_resource,\\\n                                               train_and_predict_with_cv\n\n  RESULTS_DIR='../results'\n\n  dataset = load_dataset_from_resource('sklearn-digits-dataset')\n  train_and_predict_with_cv(SVC, {'gamma':[0.01, 0.001, 0.0001]}, dataset,\n                            RESULTS_DIR, random_state=42)\n\nNow, run the cell. It will take a few seconds to train and test the\nmodel. You should then see::\n\n  Best params were: {'gamma': 0.001}\n  accuracy: 0.99\n  classification report:\n                precision    recall  f1-score   support\n\n           0.0       1.00      1.00      1.00        33\n           1.0       1.00      1.00      1.00        28\n           2.0       1.00      1.00      1.00        33\n           3.0       1.00      0.97      0.99        34\n           4.0       1.00      1.00      1.00        46\n           5.0       0.98      0.98      0.98        47\n           6.0       0.97      1.00      0.99        35\n           7.0       0.97      0.97      0.97        34\n           8.0       1.00      1.00      1.00        30\n           9.0       0.97      0.97      0.97        40\n\n     micro avg       0.99      0.99      0.99       360\n     macro avg       0.99      0.99      0.99       360\n  weighted avg       0.99      0.99      0.99       360\n\n  Wrote results to results:results.json\n\nNow, you can save and shut down your notebook. If you look at the\ndirectory ``quickstart/results``, you should see a ``results.json``\nfile with information about your run.\n\nNext, let us take a *snapshot*, which will record the state of\nthe workspace and save the data lineage along with our results::\n\n  dws snapshot -m \"first run with SVC\" SVC-1\n\n``SVC-1`` is the *tag* of our snapshot.\nIf you look in ``quickstart/results``, you will see that the results\n(currently just ``results.json``) have been moved to the subdirectory\n``snapshots/HOSTNAME-SVC-1``, where ``HOSTNAME`` is the hostname for your\nlocal machine). A file, ``lineage.json``, containing a full\ndata lineage graph for our experiment has also been\ncreated in that directory.\n\nSome things you can do from here:\n\n* Run more experiments and save their results by snapshotting the workspace.\n  If, at some point, we want to go back to our first experiment, we can run:\n  ``dws restore SVC-1``. This will restore the state of the source data and\n  code subdirectories, but leave the full history of the results.\n* Upload your workspace on GitHub or an any other Git hosting application.\n  This can be to have a backup copy or to share with others.\n  Others can download it via ``dws clone``.\n* More complex scenarios involving multi-step data pipelines can easily\n  be automated. See the documentation for details.\n\nDocumentation\n=============\nThe documentation is available here: https://data-workspaces-core.readthedocs.io/en/latest/. The source for the documentation is under ``docs``. To build it locally, install\n`Sphinx <https://www.sphinx-doc.org/en/master/>`_ and run the following::\n\n  cd docs\n  pip install -r requirements.txt # extras needed to build the docs\n  make html\n\nTo view the local documentation, open the file ``docs/_build/html/index.html`` in your\nbrowser.\n\nLicense\n=======\nThis code is copyright 2018, 2019 by the Max Planck Institute for Software Systems and Data-ken\nResearch. It is licensed under the Apache 2.0 license. See the file LICENSE.txt for details.\n\n\n",
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