From eab9c56af77beeb64fd6011c76dd3e259103b37e Mon Sep 17 00:00:00 2001 From: Sarah Maddox Date: Fri, 28 Jun 2019 04:33:21 +1000 Subject: [PATCH] Added an overview of Pipelines interfaces (#829) * Added an overview of Pipelines interfaces. * Updated for review comments. --- content/docs/pipelines/overview/interfaces.md | 60 +++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 content/docs/pipelines/overview/interfaces.md diff --git a/content/docs/pipelines/overview/interfaces.md b/content/docs/pipelines/overview/interfaces.md new file mode 100644 index 00000000..5c9e92bc --- /dev/null +++ b/content/docs/pipelines/overview/interfaces.md @@ -0,0 +1,60 @@ ++++ +title = "Introduction to the Pipelines Interfaces" +description = "The ways you can interact with the Kubeflow Pipelines system" +weight = 20 ++++ + +This page introduces the interfaces that you can use to build and run +machine learning (ML) workflows with Kubeflow Pipelines. + +## User interface (UI) + +You can access the Kubeflow Pipelines UI by clicking **Pipeline Dashboard** on +the Kubeflow UI. The Kubeflow Pipelines UI looks like this: + Pipelines UI + +From the Kubeflow Pipelines UI you can perform the following tasks: + +* Run one or more of the preloaded samples to try out pipelines quickly. +* Upload a pipeline as a compressed file. The pipeline can be one that you + have built (see how to [build a + pipeline](/docs/pipelines/sdk/build-component/#compile-the-pipeline)) or one + that someone has shared with you. +* Create an *experiment* to group one or more of your pipeline runs. + See the [definition of an + experiment](/docs/pipelines/overview/concepts/experiment/). +* Create and start a *run* within the experiment. A run is a single execution + of a pipeline. See the [definition of a + run](/docs/pipelines/overview/concepts/run/). +* Explore the configuration, graph, and output of your pipeline run. +* Compare the results of one or more runs within an experiment. +* Schedule runs by creating a recurring run. + +See the [quickstart guide](/docs/pipelines/pipelines-quickstart/) for more +information about accessing the Kubeflow Pipelines UI and running the samples. + +When building a pipeline component, you can write out information for display +in the UI. See the guides to [exporting +metrics](/docs/pipelines/sdk/pipelines-metrics/) and [visualizing results in +the UI](/docs/pipelines/sdk/output-viewer/). + +## Python SDK + +The Kubeflow Pipelines SDK provides a set of Python packages that you can use to +specify and run your ML workflows. + +See the [introduction to the Kubeflow Pipelines +SDK](/docs/pipelines/sdk/sdk-overview/) for an overview of the ways you can +use the SDK to build pipeline components and pipelines. + +## REST API + +The Kubeflow Pipelines API is useful for continuous integration/deployment +systems, for example, where you want to incorporate your pipeline executions +into shell scripts or other systems. +For example, you may want to trigger a pipeline run when new data comes in. + +See the [Kubeflow Pipelines API reference +documentation](/docs/pipelines/reference/api/kubeflow-pipeline-api-spec/). -- GitLab