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@@ -19,8 +21,7 @@ The Kubeflow pipelines service has the following goals:
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* Kubeflow Pipelines can be installed as part of the [Kubeflow Platform](https://www.kubeflow.org/docs/started/installing-kubeflow/#kubeflow-platform). Alternatively you can deploy [Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/operator-guides/installation/) as a standalone service.
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* The Docker container runtime has been deprecated on Kubernetes 1.20+. Kubeflow Pipelines has switched to use [Emissary Executor](https://www.kubeflow.org/docs/components/pipelines/legacy-v1/installation/choose-executor/#emissary-executor) by default from Kubeflow Pipelines 1.8. Emissary executor is Container runtime agnostic, meaning you are able to run Kubeflow Pipelines on Kubernetes cluster with any [Container runtimes](https://kubernetes.io/docs/setup/production-environment/container-runtimes/).
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* The Docker container runtime has been deprecated on Kubernetes 1.20+. Kubeflow Pipelines has switched to use [Emissary Executor](https://www.kubeflow.org/docs/components/pipelines/legacy-v1/installation/choose-executor/#emissary-executor) by default from Kubeflow Pipelines 1.8. Emissary executor is Container runtime agnostic, meaning you are able to run Kubeflow Pipelines on Kubernetes cluster with any [Container runtimes](https://kubernetes.io/docs/setup/production-environment/container-runtimes/).
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## Documentation
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@@ -34,19 +35,27 @@ Consult the [Python SDK reference docs](https://kubeflow-pipelines.readthedocs.i
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## Contributing to Kubeflow Pipelines
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Before you start contributing to Kubeflow Pipelines, read the guidelines in [How to Contribute](./CONTRIBUTING.md). To learn how to build and deploy Kubeflow Pipelines from source code, read the [developer guide](./developer_guide.md).
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Before you start contributing to Kubeflow Pipelines, read the guidelines in [How to Contribute](./CONTRIBUTING.md). To learn how to build and deploy Kubeflow Pipelines from source code, reagd the [developer guide](./developer_guide.md).
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## Kubeflow Pipelines Community
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### Community Meeting
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The Kubeflow Pipelines Community Meeting occurs every other Wed 10-11AM (PST).
We also have a slack channel (#kubeflow-pipelines) on the Cloud Native Computing Foundation Slack workspace. You can find more details at [https://www.kubeflow.org/docs/about/community/#kubeflow-slack-channels](https://www.kubeflow.org/docs/about/community/#kubeflow-slack-channels)
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## Architecture
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Details about the KFP Architecture can be found at [Architecture.md](docs/Architecture.md)
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## Blog posts
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*[Part 2: How to deploy Jupyter notebooks as components of a Kubeflow ML pipeline](https://towardsdatascience.com/how-to-deploy-jupyter-notebooks-as-components-of-a-kubeflow-ml-pipeline-part-2-b1df77f4e5b3)
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*[Part 3: How to carry out CI/CD in Machine Learning (“MLOps”) using Kubeflow ML pipelines](https://medium.com/google-cloud/how-to-carry-out-ci-cd-in-machine-learning-mlops-using-kubeflow-ml-pipelines-part-3-bdaf68082112)
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*[Tekton optimizations for Kubeflow Pipelines 2.0](https://developer.ibm.com/blogs/awb-tekton-optimizations-for-kubeflow-pipelines-2-0) (By Tommy Li)
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## Acknowledgments
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Kubeflow pipelines uses [Argo Workflows](https://github.com/argoproj/argo-workflows) by default under the hood to orchestrate Kubernetes resources. The Argo community has been very supportive and we are very grateful. Additionally there is Tekton backend available as well. To access it, please refer to [Kubeflow Pipelines with Tekton repository](https://github.com/kubeflow/kfp-tekton).
The above diagram documents the various components in Kubeflow Pipelines and how they interact. This should be a useful starting point for any developers.
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