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|
%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 <Python_Bot@openeuler.org> - 0.9.16-1
- Package Spec generated
|
