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fix(sdk): fix kfp sdk v2 readme (#9668)

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Connor McCarthy 2023-06-20 17:15:40 -07:00 committed by GitHub
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> Note: This is a pre-release and is not yet stable. Please report bugs and provide feedback via [GitHub Issues](https://github.com/kubeflow/pipelines/issues).
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 the `kfp` pre-release, run:
To install `kfp`, run:
```sh
pip install --pre kfp
pip install kfp
```
## Getting started