From e5fe981c1af88b02122eeb1a46fead3a26993aeb Mon Sep 17 00:00:00 2001 From: Connor McCarthy Date: Tue, 20 Jun 2023 17:15:40 -0700 Subject: [PATCH] fix(sdk): fix kfp sdk v2 readme (#9668) --- sdk/python/README.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/sdk/python/README.md b/sdk/python/README.md index aa8ab1f198..82fc6bb176 100644 --- a/sdk/python/README.md +++ b/sdk/python/README.md @@ -1,16 +1,14 @@ -> 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