diff --git a/content/docs/pipelines/overview/pipelines-overview.md b/content/docs/pipelines/overview/pipelines-overview.md index 46d556f83f..491ba4c53f 100644 --- a/content/docs/pipelines/overview/pipelines-overview.md +++ b/content/docs/pipelines/overview/pipelines-overview.md @@ -59,8 +59,8 @@ and [components](/docs/pipelines/concepts/component/). The screenshots and code below show the `xgboost-training-cm.py` pipeline, which creates an XGBoost model using structured data in CSV format. You can see the -source code and other information about the pipeline on -[GitHub](https://github.com/kubeflow/pipelines/tree/master/samples/core/xgboost-spark). +source code and other information about the pipeline on +[GitHub](https://github.com/kubeflow/pipelines/tree/master/samples/core/xgboost_training_cm). ### The runtime execution graph of the pipeline diff --git a/content/docs/pipelines/sdk/build-component.md b/content/docs/pipelines/sdk/build-component.md index 77e5bda719..6ab7c6fe0d 100644 --- a/content/docs/pipelines/sdk/build-component.md +++ b/content/docs/pipelines/sdk/build-component.md @@ -32,8 +32,8 @@ Set up your environment: * Install [Docker](https://www.docker.com/get-docker). * Install the [Kubeflow Pipelines SDK](/docs/pipelines/sdk/install-sdk/). -The examples on this page come from the -[XGBoost Spark pipeline sample](https://github.com/kubeflow/pipelines/tree/master/samples/xgboost-spark) +The examples on this page come from the +[XGBoost Spark pipeline sample](https://github.com/kubeflow/pipelines/tree/master/samples/core/xgboost_training_cm) in the Kubeflow Pipelines sample repository. ## Create a container image for each component diff --git a/content/docs/pipelines/sdk/manipulate-resources.md b/content/docs/pipelines/sdk/manipulate-resources.md index f36054fd99..16b6680ded 100644 --- a/content/docs/pipelines/sdk/manipulate-resources.md +++ b/content/docs/pipelines/sdk/manipulate-resources.md @@ -71,7 +71,7 @@ specification. ### Samples For better understanding, please refer to the following samples: -[1](https://github.com/kubeflow/pipelines/blob/master/samples/core/resource_ops/resourceop_basic.py) +[1](https://github.com/kubeflow/pipelines/blob/master/samples/core/resource_ops/resource_ops.py) --- @@ -256,7 +256,7 @@ The user may otherwise use the `step.outputs["name"]` as `data_source`. ### Samples For better understanding, please refer to the following samples: -[1](https://github.com/kubeflow/pipelines/blob/master/samples/core/volume_snapshot_ops/volume_snapshot_op.py), +[1](https://github.com/kubeflow/pipelines/blob/master/samples/core/volume_snapshot_ops/volume_snapshot_ops.py), [2](https://github.com/kubeflow/pipelines/blob/master/samples/contrib/volume_snapshot_ops/volume_snapshotop_rokurl.py) ## Next steps diff --git a/content/docs/pipelines/sdk/pipelines-metrics.md b/content/docs/pipelines/sdk/pipelines-metrics.md index 36bff22a04..a1624937ac 100644 --- a/content/docs/pipelines/sdk/pipelines-metrics.md +++ b/content/docs/pipelines/sdk/pipelines-metrics.md @@ -61,23 +61,6 @@ The following example shows the **accuracy-score** and alt="Metrics from a pipeline run" class="mt-3 mb-3 border border-info rounded"> -The above example comes from the *taxi tip prediction* sample that is -pre-installed when you deploy Kubeflow. You can run the sample by selecting **[Sample] -ML - TFX - Taxi Tip Prediction Model Trainer** from the Kubeflow Pipelines UI. -For help getting started with the UI, follow the -[Kubeflow Pipelines quickstart](/docs/pipelines/pipelines-quickstart/). - -The sample code is available in the [Kubeflow Pipelines samples -repo](https://github.com/kubeflow/pipelines/tree/master/samples/tfx). The -pipeline uses a number of prebuilt components, including: - -* The [Confusion Matrix - component](https://github.com/kubeflow/pipelines/blob/master/components/local/confusion_matrix/src/confusion_matrix.py) - which writes out the **accuracy-score** metric. -* The [ROC - component](https://github.com/kubeflow/pipelines/blob/master/components/local/roc/src/roc.py) - which writes out the **roc-auc-score** metric. - ## Next step Visualize the output of your component by [writing out metadata for an output diff --git a/content/docs/pipelines/tutorials/build-pipeline.md b/content/docs/pipelines/tutorials/build-pipeline.md index a12deef6db..b7d1f0f951 100644 --- a/content/docs/pipelines/tutorials/build-pipeline.md +++ b/content/docs/pipelines/tutorials/build-pipeline.md @@ -68,7 +68,7 @@ guide to [getting started with the UI](/docs/pipelines/pipelines-quickstart). ## Building a pipeline in a Jupyter notebook You can choose to build your pipeline in a Jupyter notebook. The -[sample notebooks](https://github.com/kubeflow/pipelines/tree/master/samples/notebooks) +[sample notebooks](https://github.com/kubeflow/pipelines/tree/master/samples/core) walk you through the process. It's easiest to use the Jupyter services that are installed in the same cluster as @@ -92,10 +92,10 @@ Follow these steps to start a notebook: 1. Follow the [Kubeflow notebooks setup guide](/docs/notebooks/setup/) to create a Jupyter notebook server and open the Jupyter UI. -1. Download the sample notebooks from - https://github.com/kubeflow/pipelines/tree/master/samples/notebooks. - -1. Upload these notebooks from the Jupyter UI: In Jupyter, go to the tree view +1. Download the sample notebooks from + https://github.com/kubeflow/pipelines/tree/master/samples/core. + +1. Upload these notebooks from the Jupyter UI: In Jupyter, go to the tree view and find the **upload** button in the top right-hand area of the screen. 1. Open one of the uploaded notebooks. @@ -107,7 +107,7 @@ Follow these steps to start a notebook: The following notebooks are available: -* [KubeFlow pipeline using TFX OSS components](https://github.com/kubeflow/pipelines/blob/master/samples/core/kubeflow_pipeline_using_TFX_OSS_components/KubeFlow%20Pipeline%20Using%20TFX%20OSS%20Components.ipynb): +* [KubeFlow pipeline using TFX OSS components](https://github.com/kubeflow/pipelines/blob/master/samples/core/tfx-oss/TFX%20Example.ipynb): This notebook demonstrates how to build a machine learning pipeline based on [TensorFlow Extended (TFX)](https://www.tensorflow.org/tfx/) components. The pipeline includes a TFDV step to infer the schema, a TFT preprocessor, a