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
})
```
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