Metadata-Version: 2.1
Name: kfp
Version: 1.8.15
Summary: KubeFlow Pipelines SDK
Home-page: https://github.com/kubeflow/pipelines
Author: The Kubeflow Authors
License: UNKNOWN
Project-URL: Documentation, https://kubeflow-pipelines.readthedocs.io/en/stable/
Project-URL: Bug Tracker, https://github.com/kubeflow/pipelines/issues
Project-URL: Source, https://github.com/kubeflow/pipelines/tree/master/sdk
Project-URL: Changelog, https://github.com/kubeflow/pipelines/blob/master/sdk/RELEASE.md
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='<my-host-url>')
        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/).
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6.1
Description-Content-Type: text/markdown
Provides-Extra: all
