%global _empty_manifest_terminate_build 0 Name: python-pypmml-spark Version: 0.9.16 Release: 1 Summary: Python PMML scoring library for PySpark as SparkML Transformer License: Apache License 2.0 URL: https://github.com/autodeployai/pypmml-spark Source0: https://mirrors.nju.edu.cn/pypi/web/packages/93/18/a32277826a25ed1f8acc429d80e034d00c763066919118179433c738ea68/pypmml-spark-0.9.16.tar.gz BuildArch: noarch %description [pypmml-spark](https://github.com/autodeployai/pypmml-spark/tree/master) | PySpark >= 3.0.0 [pypmml-spark2](https://github.com/autodeployai/pypmml-spark/tree/spark-2.x) | PySpark >= 2.4.0, < 3.0.0 ## Installation ```bash pip install pypmml-spark ``` Or install the latest version from github: ```bash pip install --upgrade git+https://github.com/autodeployai/pypmml-spark.git ``` After 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: 1. The easiest way is to run the script `link_pmml4s_jars_into_spark.py` that is delivered with `pypmml-spark`: ```bash link_pmml4s_jars_into_spark.py ``` 2. 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. ## Usage 1. Load model from various sources, e.g. filename, string, or array of bytes. ```python from pypmml_spark import ScoreModel # The model is from http://dmg.org/pmml/pmml_examples/KNIME_PMML_4.1_Examples/single_iris_dectree.xml model = ScoreModel.fromFile('single_iris_dectree.xml') ``` 2. Call `transform(dataset)` to run a batch score against an input dataset. ```python # The data is from http://dmg.org/pmml/pmml_examples/Iris.csv df = spark.read.csv('Iris.csv', header='true') score_df = model.transform(df) ``` ## Use PMML in Scala or Java See the [PMML4S](https://github.com/autodeployai/pmml4s) project. _PMML4S_ is a PMML scoring library for Scala. It provides both Scala and Java Evaluator API for PMML. ## Use PMML in Python See the [PyPMML](https://github.com/autodeployai/pypmml) project. _PyPMML_ is a Python PMML scoring library, it really is the Python API for PMML4S. ## Use PMML in Spark See the [PMML4S-Spark](https://github.com/autodeployai/pmml4s-spark) project. _PMML4S-Spark_ is a PMML scoring library for Spark as SparkML Transformer. ## Deploy PMML as REST API See the [AI-Serving](https://github.com/autodeployai/ai-serving) project. _AI-Serving_ is serving AI/ML models in the open standard formats PMML and ONNX with both HTTP (REST API) and gRPC endpoints. ## Deploy and Manage AI/ML models at scale See the [DaaS](https://www.autodeploy.ai/) system that deploys AI/ML models in production at scale on Kubernetes. ## Support If you have any questions about the _PyPMML-Spark_ library, please open issues on this repository. Feedback and contributions to the project, no matter what kind, are always very welcome. ## License _PyPMML-Spark_ is licensed under [APL 2.0](http://www.apache.org/licenses/LICENSE-2.0). %package -n python3-pypmml-spark Summary: Python PMML scoring library for PySpark as SparkML Transformer Provides: python-pypmml-spark BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pypmml-spark [pypmml-spark](https://github.com/autodeployai/pypmml-spark/tree/master) | PySpark >= 3.0.0 [pypmml-spark2](https://github.com/autodeployai/pypmml-spark/tree/spark-2.x) | PySpark >= 2.4.0, < 3.0.0 ## Installation ```bash pip install pypmml-spark ``` Or install the latest version from github: ```bash pip install --upgrade git+https://github.com/autodeployai/pypmml-spark.git ``` After 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: 1. The easiest way is to run the script `link_pmml4s_jars_into_spark.py` that is delivered with `pypmml-spark`: ```bash link_pmml4s_jars_into_spark.py ``` 2. 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. ## Usage 1. Load model from various sources, e.g. filename, string, or array of bytes. ```python from pypmml_spark import ScoreModel # The model is from http://dmg.org/pmml/pmml_examples/KNIME_PMML_4.1_Examples/single_iris_dectree.xml model = ScoreModel.fromFile('single_iris_dectree.xml') ``` 2. Call `transform(dataset)` to run a batch score against an input dataset. ```python # The data is from http://dmg.org/pmml/pmml_examples/Iris.csv df = spark.read.csv('Iris.csv', header='true') score_df = model.transform(df) ``` ## Use PMML in Scala or Java See the [PMML4S](https://github.com/autodeployai/pmml4s) project. _PMML4S_ is a PMML scoring library for Scala. It provides both Scala and Java Evaluator API for PMML. ## Use PMML in Python See the [PyPMML](https://github.com/autodeployai/pypmml) project. _PyPMML_ is a Python PMML scoring library, it really is the Python API for PMML4S. ## Use PMML in Spark See the [PMML4S-Spark](https://github.com/autodeployai/pmml4s-spark) project. _PMML4S-Spark_ is a PMML scoring library for Spark as SparkML Transformer. ## Deploy PMML as REST API See the [AI-Serving](https://github.com/autodeployai/ai-serving) project. _AI-Serving_ is serving AI/ML models in the open standard formats PMML and ONNX with both HTTP (REST API) and gRPC endpoints. ## Deploy and Manage AI/ML models at scale See the [DaaS](https://www.autodeploy.ai/) system that deploys AI/ML models in production at scale on Kubernetes. ## Support If you have any questions about the _PyPMML-Spark_ library, please open issues on this repository. Feedback and contributions to the project, no matter what kind, are always very welcome. ## License _PyPMML-Spark_ is licensed under [APL 2.0](http://www.apache.org/licenses/LICENSE-2.0). %package help Summary: Development documents and examples for pypmml-spark Provides: python3-pypmml-spark-doc %description help [pypmml-spark](https://github.com/autodeployai/pypmml-spark/tree/master) | PySpark >= 3.0.0 [pypmml-spark2](https://github.com/autodeployai/pypmml-spark/tree/spark-2.x) | PySpark >= 2.4.0, < 3.0.0 ## Installation ```bash pip install pypmml-spark ``` Or install the latest version from github: ```bash pip install --upgrade git+https://github.com/autodeployai/pypmml-spark.git ``` After 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: 1. The easiest way is to run the script `link_pmml4s_jars_into_spark.py` that is delivered with `pypmml-spark`: ```bash link_pmml4s_jars_into_spark.py ``` 2. 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. ## Usage 1. Load model from various sources, e.g. filename, string, or array of bytes. ```python from pypmml_spark import ScoreModel # The model is from http://dmg.org/pmml/pmml_examples/KNIME_PMML_4.1_Examples/single_iris_dectree.xml model = ScoreModel.fromFile('single_iris_dectree.xml') ``` 2. Call `transform(dataset)` to run a batch score against an input dataset. ```python # The data is from http://dmg.org/pmml/pmml_examples/Iris.csv df = spark.read.csv('Iris.csv', header='true') score_df = model.transform(df) ``` ## Use PMML in Scala or Java See the [PMML4S](https://github.com/autodeployai/pmml4s) project. _PMML4S_ is a PMML scoring library for Scala. It provides both Scala and Java Evaluator API for PMML. ## Use PMML in Python See the [PyPMML](https://github.com/autodeployai/pypmml) project. _PyPMML_ is a Python PMML scoring library, it really is the Python API for PMML4S. ## Use PMML in Spark See the [PMML4S-Spark](https://github.com/autodeployai/pmml4s-spark) project. _PMML4S-Spark_ is a PMML scoring library for Spark as SparkML Transformer. ## Deploy PMML as REST API See the [AI-Serving](https://github.com/autodeployai/ai-serving) project. _AI-Serving_ is serving AI/ML models in the open standard formats PMML and ONNX with both HTTP (REST API) and gRPC endpoints. ## Deploy and Manage AI/ML models at scale See the [DaaS](https://www.autodeploy.ai/) system that deploys AI/ML models in production at scale on Kubernetes. ## Support If you have any questions about the _PyPMML-Spark_ library, please open issues on this repository. Feedback and contributions to the project, no matter what kind, are always very welcome. ## License _PyPMML-Spark_ is licensed under [APL 2.0](http://www.apache.org/licenses/LICENSE-2.0). %prep %autosetup -n pypmml-spark-0.9.16 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-pypmml-spark -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 0.9.16-1 - Package Spec generated