{
    "info": {
        "author": "Antoine Redier",
        "author_email": "antoine.redier2@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "========\nchariots\n========\n\n\n.. image:: https://img.shields.io/pypi/v/chariots.svg\n        :target: https://pypi.python.org/pypi/chariots\n\n.. image:: https://img.shields.io/travis/aredier/chariots.svg\n        :target: https://travis-ci.org/aredier/chariots\n\n.. image:: https://readthedocs.org/projects/chariots/badge/?version=latest\n        :target: https://chariots.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n.. image:: https://img.shields.io/github/license/aredier/chariots?color=green\n        :target: https://github.com/aredier/chariots/blob/master/LICENSE\n\n\n\n\nchariots aims to be a complete framework to build and deploy versioned machine learning pipelines.\n\n* Documentation: https://chariots.readthedocs.io.\n\nGetting Started: 30 seconds to Chariots:\n----------------------------------------\nYou can check the `chariots docutemtation`_ for a complete tutorial on getting started with\nchariots, but here are the essentials:\n\nyou can create operations to execute steps in your pipeline:\n\n    >>> from chariots.sklearn import SKUnsupervisedOp, SKSupervisedOp\n    >>> from chariots.versioning import VersionType, VersionedFieldDict, VersionedField\n    >>> from sklearn.decomposition import PCA\n    >>> from sklearn.linear_model import LogisticRegression\n    ...\n    ...\n    >>> class PCAOp(SKUnsupervisedOp):\n    ...     training_update_version = VersionType.MAJOR\n    ...     model_parameters = VersionedFieldDict(VersionType.MAJOR, {\"n_components\": 2})\n    ...     model_class = VersionedField(PCA, VersionType.MAJOR)\n    ...\n    >>> class LogisticOp(SKSupervisedOp):\n    ...     training_update_version = VersionType.PATCH\n    ...     model_class = LogisticRegression\n\nOnce your ops are created, you can create your various training and prediction pipelines:\n\n\n    >>> from chariots import Pipeline, MLMode\n    >>> from chariots.nodes import Node\n    ...\n    ...\n    >>> train = Pipeline([\n    ...     Node(IrisFullDataSet(), output_nodes=[\"x\", \"y\"]),\n    ...     Node(PCAOp(MLMode.FIT_PREDICT), input_nodes=[\"x\"], output_nodes=\"x_transformed\"),\n    ...     Node(LogisticOp(MLMode.FIT), input_nodes=[\"x_transformed\", \"y\"])\n    ... ], 'train')\n    ...\n    >>> pred = Pipeline([\n    ...     Node(PCAOp(MLMode.PREDICT), input_nodes=[\"__pipeline_input__\"], output_nodes=\"x_transformed\"),\n    ...     Node(LogisticOp(MLMode.PREDICT), input_nodes=[\"x_transformed\"], output_nodes=['__pipeline_output__'])\n    ... ], 'pred')\n\nOnce all your pipelines have been created, deploying them is as easy as creating a creating a `Chariots` object:\n\n    >>> from chariots import Chariots\n    ...\n    ...\n    >>> app = Chariots([train, pred], app_path, import_name='iris_app')\n\n\nThe `Chariots` class inherits from the `Flask` class so you can deploy this the same way you would any\n`flask application`_\n\n\nOnce this the server is started, you can use the chariots client to query your machine learning micro-service from\npython:\n\n    >>> from chariots import Client\n    ...\n    ...\n    >>> client = Client()\n\nwith this client we will be\n\n- training the models\n- saving them and reloading the prediction pipeline (so that it uses the latest/trained version of our models)\n- query some prediction\n\n    >>> client.call_pipeline(train)\n    >>> client.save_pipeline(train)\n    >>> client.load_pipeline(pred)\n    >>> client.call_pipeline(pred, [[1, 2, 3, 4]])\n    [1]\n\nFeatures\n--------\n\n* versionable individual op\n* easy pipeline building\n* easy pipelines deployment\n* ML utils (implementation of ops for most popular ML libraries with adequate `Versionedfield`) for sklearn and keras at first\n* A CookieCutter template to properly structure your Chariots project\n\nComming Soon\n------------\n\nSome key features of Chariot are still in development and should be coming soon:\n\n* Cloud integration (integration with cloud services to fetch and load models from)\n* Graphql API to store and load information on different ops and pipelines (performance monitoring, ...)\n* ABTesting\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n`audreyr/cookiecutter-pypackage`_'s project is also the basis of the Chariiots project template\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypac\n.. _chariots docutemtation: https://chariots.readthedocs.io\n.. _flask application: https://github.com/pallets/flask\n\n=======\nHistory\n=======\n\n0.1.0 (2019-06-15)\n------------------\n\n* First release on PyPI.\n\n0.2.0 (2019-06-15)\n------------------\n\n* sci-kit learn and keras integration\n* multiple outputs per nodes\n* project template\n* tutorials\n\n\n",
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