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            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "# PyPMML-Spark\n\n_PyPMML-Spark_ is a Python PMML scoring library for PySpark as SparkML Transformer, it really is the Python API for [PMML4S-Spark](https://github.com/autodeployai/pmml4s-spark).\n\n## Prerequisites\n - Java >= 1.8\n - Python 2.7 or >= 3.5\n\n## Dependencies\n  - PySpark >= 2.4.0\n  \n## Installation\n\n```bash\npip install pypmml-spark\n```\n\nOr install the latest version from github:\n\n```bash\npip install --upgrade git+https://github.com/autodeployai/pypmml-spark.git\n```\n\nAfter that, you need to do more to use it in Spark that must know those jars in the package `pypmml_spark.jars`. There are several ways to do that:\n\n1. The easiest way is to run the script `link_pmml4s_jars_into_spark.py` that is delivered with `pypmml-spark`:\n\n    ```bash\n    link_pmml4s_jars_into_spark.py\n    ```\n    \n2. Use those config options to specify dependent jars properly. e.g. `--jars`, or `spark.executor.extraClassPath` and `spark.executor.extraClassPath`. See [Spark](http://spark.apache.org/docs/latest/configuration.html) for details about those parameters.\n\n## Usage\n\n1. Load model from various sources, e.g. filename, string, or array of bytes.\n\n    ```python\n    from pypmml_spark import ScoreModel\n    \n    # The model is from http://dmg.org/pmml/pmml_examples/KNIME_PMML_4.1_Examples/single_iris_dectree.xml\n    model = ScoreModel.fromFile('single_iris_dectree.xml')\n    ```\n\n2. Call `transform(dataset)` to run a batch score against an input dataset.\n\n    ```python\n    # The data is from http://dmg.org/pmml/pmml_examples/Iris.csv\n    df = spark.read.csv('Iris.csv', header='true')\n    score_df = model.transform(df)\n    ```\n\n## Use PMML in Scala or Java\nSee the [PMML4S](https://github.com/autodeployai/pmml4s) project. _PMML4S_ a PMML scoring library for Scala. It provides both Scala and Java Evaluator API for PMML.\n\n## Use PMML in Python\nSee the [PyPMML](https://github.com/autodeployai/pypmml) project. _PyPMML_ is a Python PMML scoring library, it really is the Python API for PMML4S.\n\n## Use PMML in Spark\nSee the [PMML4S-Spark](https://github.com/autodeployai/pmml4s-spark) project. _PMML4S-Spark_ is a PMML scoring library for Spark as SparkML Transformer.\n\n## Deploy PMML as REST API\nSee the [DaaS](https://www.autodeploy.ai/) system that deploys AI & ML models in production at scale on Kubernetes.\n\n## Support\nIf you have any questions about the _PyPMML-Spark_ library, please open issues on this repository.\n\nFeedback and contributions to the project, no matter what kind, are always very welcome. \n\n## License\n_PyPMML-Spark_ is licensed under [APL 2.0](http://www.apache.org/licenses/LICENSE-2.0).",
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