Description

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 and/or YAML. 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/). ## Installation To install `kfp`, run: ```sh pip install kfp ``` ## Getting started The following is an example of a simple pipeline that uses the `kfp` v2 syntax: ```python from kfp import dsl import kfp @dsl.component def add(a: float, b: float) -> float: '''Calculates sum of two arguments''' return a + b @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(a=a, b=4.0) second_add_task = add(a=first_add_task.output, b=b) client = kfp.Client(host='<my-host-url>') client.create_run_from_pipeline_func( add_pipeline, arguments={ 'a': 7.0, 'b': 8.0 }) ```