%global _empty_manifest_terminate_build 0 Name: python-kfp Version: 1.8.20 Release: 1 Summary: KubeFlow Pipelines SDK License: Apache Software License URL: https://github.com/kubeflow/pipelines Source0: https://mirrors.nju.edu.cn/pypi/web/packages/39/4f/a92391bd04ef76f7476b05366411e4c36d83eaa3fca18f1b643c7d5b75a3/kfp-1.8.20.tar.gz BuildArch: noarch %description # `kfp`: Kubeflow Pipelines SDK [![PyPI Package version](https://badge.fury.io/py/kfp.svg)](https://badge.fury.io/py/kfp) [![PyPI Python Version](https://img.shields.io/pypi/pyversions/kfp.svg)](https://pypi.org/project/kfp/) [![PyPI Downloads](https://img.shields.io/pypi/dm/kfp)](https://pypi.org/project/kfp/) [![Documentation Status](https://readthedocs.org/projects/kubeflow-pipelines/badge/?version=latest)](https://kubeflow-pipelines.readthedocs.io/en/stable/?badge=latest) [![Code Style](https://img.shields.io/badge/code%20style-yapf-brightgreen.svg)](https://github.com/google/yapf) Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning workflows based on Docker containers within the [Kubeflow](https://www.kubeflow.org/) project. Use Kubeflow Pipelines to compose a multi-step workflow ([pipeline](https://www.kubeflow.org/docs/components/pipelines/concepts/pipeline/)) as a [graph](https://www.kubeflow.org/docs/components/pipelines/concepts/graph/) of containerized [tasks](https://www.kubeflow.org/docs/components/pipelines/concepts/step/) using [Python code](https://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/#getting-started-with-python-function-based-components) and/or [YAML](https://www.kubeflow.org/docs/components/pipelines/sdk/component-development/#creating-a-component-specification). Then, [run](https://www.kubeflow.org/docs/components/pipelines/concepts/run/) your pipeline with specified pipeline arguments, rerun your pipeline with new arguments or data, [schedule](https://www.kubeflow.org/docs/components/pipelines/concepts/run-trigger/) your pipeline to run on a recurring basis, organize your runs into [experiments](https://www.kubeflow.org/docs/components/pipelines/concepts/experiment/), save machine learning artifacts to compliant [artifact registries](https://www.kubeflow.org/docs/components/pipelines/concepts/metadata/), and visualize it all through the [Kubeflow Dashboard](https://www.kubeflow.org/docs/components/central-dash/overview/). ## Documentation * [Kubeflow Pipelines Overview](https://www.kubeflow.org/docs/components/pipelines/introduction/) * [SDK Overview](https://www.kubeflow.org/docs/components/pipelines/sdk/sdk-overview/) * [SDK API Documentation](https://kubeflow-pipelines.readthedocs.io/en/stable/) ## Installation To install the latest stable release, run: ```sh pip install kfp ``` ## Getting started The following is an example of a simple pipeline with one Python function-based component used in two separate tasks to do basic addition: ```python import kfp from kfp.components import create_component_from_func import kfp.dsl as dsl def add(a: float, b: float) -> float: '''Calculates sum of two arguments''' return a + b # create a component using the add function add_op = create_component_from_func(add) # compose your pipeline using the dsl.pipeline decorator @dsl.pipeline( name='Addition pipeline', description='An example pipeline that performs addition calculations.') def add_pipeline( a: float=1.0, b: float=7.0, ): first_add_task = add_op(a=a, b=4.0) second_add_task = add_op(a=first_add_task.output, b=b) # instantiate a client and submit your pipeline with arguments client = kfp.Client(host='') client.create_run_from_pipeline_func( add_pipeline, arguments={ 'a': 7.0, 'b': 8.0 }) ``` For more information, refer to [Building Python function-based components](https://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/). %package -n python3-kfp Summary: KubeFlow Pipelines SDK Provides: python-kfp BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-kfp # `kfp`: Kubeflow Pipelines SDK [![PyPI Package version](https://badge.fury.io/py/kfp.svg)](https://badge.fury.io/py/kfp) [![PyPI Python Version](https://img.shields.io/pypi/pyversions/kfp.svg)](https://pypi.org/project/kfp/) [![PyPI Downloads](https://img.shields.io/pypi/dm/kfp)](https://pypi.org/project/kfp/) [![Documentation Status](https://readthedocs.org/projects/kubeflow-pipelines/badge/?version=latest)](https://kubeflow-pipelines.readthedocs.io/en/stable/?badge=latest) [![Code Style](https://img.shields.io/badge/code%20style-yapf-brightgreen.svg)](https://github.com/google/yapf) Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning workflows based on Docker containers within the [Kubeflow](https://www.kubeflow.org/) project. Use Kubeflow Pipelines to compose a multi-step workflow ([pipeline](https://www.kubeflow.org/docs/components/pipelines/concepts/pipeline/)) as a [graph](https://www.kubeflow.org/docs/components/pipelines/concepts/graph/) of containerized [tasks](https://www.kubeflow.org/docs/components/pipelines/concepts/step/) using [Python code](https://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/#getting-started-with-python-function-based-components) and/or [YAML](https://www.kubeflow.org/docs/components/pipelines/sdk/component-development/#creating-a-component-specification). Then, [run](https://www.kubeflow.org/docs/components/pipelines/concepts/run/) your pipeline with specified pipeline arguments, rerun your pipeline with new arguments or data, [schedule](https://www.kubeflow.org/docs/components/pipelines/concepts/run-trigger/) your pipeline to run on a recurring basis, organize your runs into [experiments](https://www.kubeflow.org/docs/components/pipelines/concepts/experiment/), save machine learning artifacts to compliant [artifact registries](https://www.kubeflow.org/docs/components/pipelines/concepts/metadata/), and visualize it all through the [Kubeflow Dashboard](https://www.kubeflow.org/docs/components/central-dash/overview/). ## Documentation * [Kubeflow Pipelines Overview](https://www.kubeflow.org/docs/components/pipelines/introduction/) * [SDK Overview](https://www.kubeflow.org/docs/components/pipelines/sdk/sdk-overview/) * [SDK API Documentation](https://kubeflow-pipelines.readthedocs.io/en/stable/) ## Installation To install the latest stable release, run: ```sh pip install kfp ``` ## Getting started The following is an example of a simple pipeline with one Python function-based component used in two separate tasks to do basic addition: ```python import kfp from kfp.components import create_component_from_func import kfp.dsl as dsl def add(a: float, b: float) -> float: '''Calculates sum of two arguments''' return a + b # create a component using the add function add_op = create_component_from_func(add) # compose your pipeline using the dsl.pipeline decorator @dsl.pipeline( name='Addition pipeline', description='An example pipeline that performs addition calculations.') def add_pipeline( a: float=1.0, b: float=7.0, ): first_add_task = add_op(a=a, b=4.0) second_add_task = add_op(a=first_add_task.output, b=b) # instantiate a client and submit your pipeline with arguments client = kfp.Client(host='') client.create_run_from_pipeline_func( add_pipeline, arguments={ 'a': 7.0, 'b': 8.0 }) ``` For more information, refer to [Building Python function-based components](https://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/). %package help Summary: Development documents and examples for kfp Provides: python3-kfp-doc %description help # `kfp`: Kubeflow Pipelines SDK [![PyPI Package version](https://badge.fury.io/py/kfp.svg)](https://badge.fury.io/py/kfp) [![PyPI Python Version](https://img.shields.io/pypi/pyversions/kfp.svg)](https://pypi.org/project/kfp/) [![PyPI Downloads](https://img.shields.io/pypi/dm/kfp)](https://pypi.org/project/kfp/) [![Documentation Status](https://readthedocs.org/projects/kubeflow-pipelines/badge/?version=latest)](https://kubeflow-pipelines.readthedocs.io/en/stable/?badge=latest) [![Code Style](https://img.shields.io/badge/code%20style-yapf-brightgreen.svg)](https://github.com/google/yapf) Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning workflows based on Docker containers within the [Kubeflow](https://www.kubeflow.org/) project. Use Kubeflow Pipelines to compose a multi-step workflow ([pipeline](https://www.kubeflow.org/docs/components/pipelines/concepts/pipeline/)) as a [graph](https://www.kubeflow.org/docs/components/pipelines/concepts/graph/) of containerized [tasks](https://www.kubeflow.org/docs/components/pipelines/concepts/step/) using [Python code](https://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/#getting-started-with-python-function-based-components) and/or [YAML](https://www.kubeflow.org/docs/components/pipelines/sdk/component-development/#creating-a-component-specification). Then, [run](https://www.kubeflow.org/docs/components/pipelines/concepts/run/) your pipeline with specified pipeline arguments, rerun your pipeline with new arguments or data, [schedule](https://www.kubeflow.org/docs/components/pipelines/concepts/run-trigger/) your pipeline to run on a recurring basis, organize your runs into [experiments](https://www.kubeflow.org/docs/components/pipelines/concepts/experiment/), save machine learning artifacts to compliant [artifact registries](https://www.kubeflow.org/docs/components/pipelines/concepts/metadata/), and visualize it all through the [Kubeflow Dashboard](https://www.kubeflow.org/docs/components/central-dash/overview/). ## Documentation * [Kubeflow Pipelines Overview](https://www.kubeflow.org/docs/components/pipelines/introduction/) * [SDK Overview](https://www.kubeflow.org/docs/components/pipelines/sdk/sdk-overview/) * [SDK API Documentation](https://kubeflow-pipelines.readthedocs.io/en/stable/) ## Installation To install the latest stable release, run: ```sh pip install kfp ``` ## Getting started The following is an example of a simple pipeline with one Python function-based component used in two separate tasks to do basic addition: ```python import kfp from kfp.components import create_component_from_func import kfp.dsl as dsl def add(a: float, b: float) -> float: '''Calculates sum of two arguments''' return a + b # create a component using the add function add_op = create_component_from_func(add) # compose your pipeline using the dsl.pipeline decorator @dsl.pipeline( name='Addition pipeline', description='An example pipeline that performs addition calculations.') def add_pipeline( a: float=1.0, b: float=7.0, ): first_add_task = add_op(a=a, b=4.0) second_add_task = add_op(a=first_add_task.output, b=b) # instantiate a client and submit your pipeline with arguments client = kfp.Client(host='') client.create_run_from_pipeline_func( add_pipeline, arguments={ 'a': 7.0, 'b': 8.0 }) ``` For more information, refer to [Building Python function-based components](https://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/). %prep %autosetup -n kfp-1.8.20 %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-kfp -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon Apr 10 2023 Python_Bot - 1.8.20-1 - Package Spec generated