update sample link after kfp sample restructure (kubeflow#1057)

content/docs/pipelines/sdk/build-component.md

@@ -110,7 +110,7 @@ def dataproc_train_op(
 ```
 
 The function must return a dsl.ContainerOp from the
-[XGBoost Spark pipeline sample](https://github.com/kubeflow/pipelines/blob/master/samples/xgboost-spark/xgboost-training-cm.py).
+[XGBoost Spark pipeline sample](https://github.com/kubeflow/pipelines/blob/master/samples/core/xgboost_training_cm/xgboost_training_cm.py).
 
 Note:
 
@@ -122,7 +122,7 @@ Note:
   you can still use the parameters inline in the `arguments` by using `%s`
   variable substitution. At run time the argument contains the value of the 
   parameter. For an example of this technique in operation, see the 
-  [taxi cab classification pipeline](https://github.com/kubeflow/pipelines/blob/master/samples/tfx/taxi-cab-classification-pipeline.py). 
+  [taxi cab classification pipeline](https://github.com/kubeflow/pipelines/blob/master/samples/core/tfx_cab_classification/tfx_cab_classification.py). 
 * `file_outputs` is a mapping between labels and local file paths. In the above 
   example, the content of `/output.txt` contains the string output of the 
   component. To reference the output in code:
@@ -172,7 +172,7 @@ Note:
 
 
 See the full code in the
-[XGBoost Spark pipeline sample](https://github.com/kubeflow/pipelines/blob/master/samples/xgboost-spark/xgboost-training-cm.py).
+[XGBoost Spark pipeline sample](https://github.com/kubeflow/pipelines/blob/master/samples/core/xgboost_training_cm/xgboost_training_cm.py).
 
 ## Compile the pipeline
 

content/docs/pipelines/sdk/dsl-recursion.md

@@ -88,4 +88,4 @@ multiple recursive function calls in the function body.
 
 ## Next steps
 
-* See [recursion sample](https://github.com/kubeflow/pipelines/blob/master/samples/basic/recursion.py)
+* See [recursion sample](https://github.com/kubeflow/pipelines/blob/master/samples/core/recursion/recursion.py)

content/docs/pipelines/sdk/lightweight-python-components.md

@@ -54,6 +54,6 @@ There are several requirements for the component function:
 ## Tutorials
 
 See the notebook on 
-[lightweight Python component basics](https://github.com/kubeflow/pipelines/blob/master/samples/notebooks/Lightweight%20Python%20components%20-%20basics.ipynb) 
+[lightweight Python component basics](https://github.com/kubeflow/pipelines/blob/master/samples/core/lightweight_component/Lightweight%20Python%20components%20-%20basics.ipynb) 
 for an example of creating lightweight Python components and using them in a 
 pipeline.

content/docs/pipelines/sdk/sdk-overview.md

@@ -247,7 +247,7 @@ For more detailed instructions, see the guide to [building components and
 pipelines](/docs/pipelines/sdk/build-component/).
 
 For an example, see the
-[`xgboost-training-cm.py`](https://github.com/kubeflow/pipelines/blob/master/samples/xgboost-spark/xgboost-training-cm.py)
+[`xgboost-training-cm.py`](https://github.com/kubeflow/pipelines/blob/master/samples/core/xgboost_training_cm/xgboost_training_cm.py)
 pipeline sample on GitHub. The pipeline creates an XGBoost model using 
 structured data in CSV format.
 {{% /alert %}}
@@ -356,7 +356,7 @@ You can also choose to share your pipeline as follows:
 {{% alert title="More about the above workflow" color="info" %}}
 For an example of the above workflow, see the
 Jupyter notebook titled [KubeFlow Pipeline Using TFX OSS 
-Components](https://github.com/kubeflow/pipelines/blob/master/samples/notebooks/KubeFlow%20Pipeline%20Using%20TFX%20OSS%20Components.ipynb) on GitHub.
+Components](https://github.com/kubeflow/pipelines/blob/master/samples/core/kubeflow_pipeline_using_TFX_OSS_components/KubeFlow%20Pipeline%20Using%20TFX%20OSS%20Components.ipynb) on GitHub.
 {{% /alert %}}
 
 <a id="lightweight-component"></a>
@@ -456,7 +456,7 @@ For more detailed instructions, see the guide to [building lightweight
 components](/docs/pipelines/sdk/lightweight-python-components/).
 
 For an example, see the [Lightweight Python components - 
-basics](https://github.com/kubeflow/pipelines/blob/master/samples/notebooks/Lightweight%20Python%20components%20-%20basics.ipynb)
+basics](https://github.com/kubeflow/pipelines/blob/master/samples/core/lightweight_component/Lightweight%20Python%20components%20-%20basics.ipynb)
 notebook on GitHub.
 {{% /alert %}}
 

content/docs/pipelines/sdk/static-type-checking.md

@@ -153,4 +153,4 @@ type checking would still fail if some I/Os lack the type information and some I
 
 ## Next steps
 
-* See [type checking sample](https://github.com/kubeflow/pipelines/blob/master/samples/notebooks/DSL%20Static%20Type%20Checking.ipynb).
+* See [type checking sample](https://github.com/kubeflow/pipelines/blob/master/samples/core/dsl_static_type_checking/DSL%20Static%20Type%20Checking.ipynb).

content/docs/pipelines/tutorials/build-pipeline.md

@@ -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/notebooks/KubeFlow%20Pipeline%20Using%20TFX%20OSS%20Components.ipynb): 
+* [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): 
   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 
@@ -116,7 +116,7 @@ The following notebooks are available:
   demonstrates how to build a component based on Python 3 inside the notebook, 
   including how to build a Docker container.
 
-* [Lightweight Python components](https://github.com/kubeflow/pipelines/blob/master/samples/notebooks/Lightweight%20Python%20components%20-%20basics.ipynb): 
+* [Lightweight Python components](https://github.com/kubeflow/pipelines/blob/master/samples/core/lightweight_component/Lightweight%20Python%20components%20-%20basics.ipynb): 
   This notebook demonstrates how to build simple Python components based on 
   Python 3 and use them in a pipeline with fast iterations. If you use this
   technique, you don't need to build a Docker container when you build a