diff --git a/content/docs/pipelines/sdk/build-component.md b/content/docs/pipelines/sdk/build-component.md index 4b6f0ee3ee..c94196a310 100644 --- a/content/docs/pipelines/sdk/build-component.md +++ b/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 diff --git a/content/docs/pipelines/sdk/dsl-recursion.md b/content/docs/pipelines/sdk/dsl-recursion.md index d74e26cbdb..f168471193 100644 --- a/content/docs/pipelines/sdk/dsl-recursion.md +++ b/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) \ No newline at end of file +* See [recursion sample](https://github.com/kubeflow/pipelines/blob/master/samples/core/recursion/recursion.py) \ No newline at end of file diff --git a/content/docs/pipelines/sdk/lightweight-python-components.md b/content/docs/pipelines/sdk/lightweight-python-components.md index 1d77be1858..38b15f0938 100644 --- a/content/docs/pipelines/sdk/lightweight-python-components.md +++ b/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. \ No newline at end of file diff --git a/content/docs/pipelines/sdk/sdk-overview.md b/content/docs/pipelines/sdk/sdk-overview.md index 67703ea0b8..d07ad8f6f6 100644 --- a/content/docs/pipelines/sdk/sdk-overview.md +++ b/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 %}} @@ -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 %}} diff --git a/content/docs/pipelines/sdk/static-type-checking.md b/content/docs/pipelines/sdk/static-type-checking.md index 3a9bfe344a..853cbcb571 100644 --- a/content/docs/pipelines/sdk/static-type-checking.md +++ b/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). diff --git a/content/docs/pipelines/tutorials/build-pipeline.md b/content/docs/pipelines/tutorials/build-pipeline.md index 28468caf4d..4c1701538e 100644 --- a/content/docs/pipelines/tutorials/build-pipeline.md +++ b/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