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updating broken pipelines references (#1199)

* updating broken pipelines references

This is global update, its meaning to fix links to kubeflow/pipelines
repository

1. `pipeline-metrics.md` - taxy pipeline and notebook link (tfx)
2. `manipulate-reposerces.md` - renamed resources
    see kubeflow/pipelines@fe0bc67
3. `build-pipeline.md` link to tfx notebook + general link to
    samples/core, as notebooks now located in multiple directories.
4. `build-pipeline.md` - other pipeline repo changes.

* Additional fix for new found broken resources

Added two more fixes for broken pipelines, not covered before.

* Updating `pipelines metrics`
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butuzov authored and k8s-ci-robot committed Oct 9, 2019
1 parent 92a4542 commit e2040afbe6aa170c16bc4dfed791d5eb8131a94b
@@ -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

@@ -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
@@ -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
@@ -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
@@ -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

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