fix: use canonical v1 link throughout pipelines docs (#3240)

* fix: use canonical v1 links throughout docs

* use master for roadmap

* fix other broken links

* use sdk/release-1.8 instead of commit hash

Author: connor-mccarthy

content/en/docs/components/pipelines/installation/standalone-deployment.md

@@ -162,17 +162,17 @@ and use it as your working directory.
 ### Deploy on GCP with Cloud SQL and Google Cloud Storage
 
 **Note**: This is recommended for production environments. For more details about customizing your environment
-for GCP, see the [Kubeflow Pipelines GCP manifests](https://github.com/kubeflow/pipelines/tree/master/manifests/kustomize/env/gcp).
+for GCP, see the [Kubeflow Pipelines GCP manifests](https://github.com/kubeflow/pipelines/tree/sdk/release-1.8/manifests/kustomize/env/gcp).
 
 ### Change deployment namespace
 
 To deploy Kubeflow Pipelines standalone in namespace `<my-namespace>`:
 
 1. Set the `namespace` field to `<my-namespace>` in
-   [dev/kustomization.yaml](https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/env/dev/kustomization.yaml) or
-   [gcp/kustomization.yaml](https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/env/gcp/kustomization.yaml).
+   [dev/kustomization.yaml](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/manifests/kustomize/env/dev/kustomization.yaml) or
+   [gcp/kustomization.yaml](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/manifests/kustomize/env/gcp/kustomization.yaml).
 
-1. Set the `namespace` field to `<my-namespace>` in [cluster-scoped-resources/kustomization.yaml](https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/cluster-scoped-resources/kustomization.yaml)
+1. Set the `namespace` field to `<my-namespace>` in [cluster-scoped-resources/kustomization.yaml](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/manifests/kustomize/cluster-scoped-resources/kustomization.yaml)
 
 1. Apply the changes to update the Kubeflow Pipelines deployment:
 
@@ -181,7 +181,7 @@ To deploy Kubeflow Pipelines standalone in namespace `<my-namespace>`:
      kubectl apply -k manifests/kustomize/env/dev
      ```
 
-     **Note**: If using GCP Cloud SQL and Google Cloud Storage, set the proper values in [manifests/kustomize/env/gcp/params.env](https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/env/gcp/params.env), then apply with this command:
+     **Note**: If using GCP Cloud SQL and Google Cloud Storage, set the proper values in [manifests/kustomize/env/gcp/params.env](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/manifests/kustomize/env/gcp/params.env), then apply with this command:
 
      ```
      kubectl apply -k manifests/kustomize/cluster-scoped-resources
@@ -192,15 +192,15 @@ To deploy Kubeflow Pipelines standalone in namespace `<my-namespace>`:
 
 By default, the KFP standalone deployment installs an [inverting proxy agent](https://github.com/google/inverting-proxy) that exposes a public URL. If you want to skip the installation of the inverting proxy agent, complete the following:
 
-1. Comment out the proxy components in the base `kustomization.yaml`. For example in [manifests/kustomize/env/dev/kustomization.yaml](https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/env/dev/kustomization.yaml) comment out `inverse-proxy`.
+1. Comment out the proxy components in the base `kustomization.yaml`. For example in [manifests/kustomize/env/dev/kustomization.yaml](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/manifests/kustomize/env/dev/kustomization.yaml) comment out `inverse-proxy`.
 
 1. Apply the changes to update the Kubeflow Pipelines deployment:
 
      ```
      kubectl apply -k manifests/kustomize/env/dev
      ```
 
-     **Note**: If using GCP Cloud SQL and Google Cloud Storage, set the proper values in [manifests/kustomize/env/gcp/params.env](https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/env/gcp/params.env), then apply with this command:
+     **Note**: If using GCP Cloud SQL and Google Cloud Storage, set the proper values in [manifests/kustomize/env/gcp/params.env](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/manifests/kustomize/env/gcp/params.env), then apply with this command:
 
      ```
      kubectl apply -k manifests/kustomize/env/gcp

content/en/docs/components/pipelines/introduction.md

@@ -64,7 +64,7 @@ and [components](/docs/components/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_training_cm).
+[GitHub](https://github.com/kubeflow/pipelines/tree/sdk/release-1.8/samples/core/xgboost_training_cm).
 
 ### The runtime execution graph of the pipeline
 
@@ -79,7 +79,7 @@ Kubeflow Pipelines UI:
 
 Below is an extract from the Python code that defines the 
 `xgboost-training-cm.py` pipeline. You can see the full code on 
-[GitHub](https://github.com/kubeflow/pipelines/tree/master/samples/core/xgboost_training_cm).
+[GitHub](https://github.com/kubeflow/pipelines/tree/sdk/release-1.8/samples/core/xgboost_training_cm).
 
 ```python
 @dsl.pipeline(
@@ -229,9 +229,9 @@ At a high level, the execution of a pipeline proceeds as follows:
 
 * **Python SDK**: You create components or specify a pipeline using the Kubeflow
   Pipelines domain-specific language 
-  ([DSL](https://github.com/kubeflow/pipelines/tree/master/sdk/python/kfp/dsl)).
+  ([DSL](https://github.com/kubeflow/pipelines/tree/sdk/release-1.8/sdk/python/kfp/dsl)).
 * **DSL compiler**: The
-  [DSL compiler](https://github.com/kubeflow/pipelines/tree/master/sdk/python/kfp/compiler)
+  [DSL compiler](https://github.com/kubeflow/pipelines/tree/sdk/release-1.8/sdk/python/kfp/compiler)
   transforms your pipeline's Python code into a static configuration (YAML).
 * **Pipeline Service**: You call the Pipeline Service to create a
   pipeline run from the static configuration. 

content/en/docs/components/pipelines/overview/quickstart.md

@@ -56,7 +56,7 @@ workload:
     alt="Run results on the pipelines UI"
     class="mt-3 mb-3 border border-info rounded">
 
-You can find the [source code for the **Data passing in python components** tutorial](https://github.com/kubeflow/pipelines/tree/master/samples/tutorials/Data%20passing%20in%20python%20components) in the Kubeflow Pipelines repo.
+You can find the [source code for the **Data passing in python components** tutorial](https://github.com/kubeflow/pipelines/tree/sdk/release-1.8/samples/tutorials/Data%20passing%20in%20python%20components) in the Kubeflow Pipelines repo.
 
 ## Run an ML pipeline
 
@@ -89,7 +89,7 @@ Follow these steps to run the sample:
       alt="XGBoost results on the pipelines UI"
       class="mt-3 mb-3 border border-info rounded">
 
-You can find the [source code for the **XGBoost - Iterative model training** demo](https://github.com/kubeflow/pipelines/tree/master/samples/core/xgboost_training_cm) in the Kubeflow Pipelines repo.
+You can find the [source code for the **XGBoost - Iterative model training** demo](https://github.com/kubeflow/pipelines/tree/sdk/release-1.8/samples/core/xgboost_training_cm) in the Kubeflow Pipelines repo.
 
 ## Next steps
 

content/en/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec.html

@@ -284,7 +284,7 @@ <h3 id="tag-PipelineService" class="swagger-summary-tag">Tag: PipelineService</h
         changes to the pipeline&#39;s most recent pipeline version. If there are no
         remaining pipeline versions, the pipeline will have no default version.
         Examines the run_service_api.ipynb notebook to learn more about creating a
-        run using a pipeline version (<a href="https://github.com/kubeflow/pipelines/blob/master/tools/benchmarks/run_service_api.ipynb">https://github.com/kubeflow/pipelines/blob/master/tools/benchmarks/run_service_api.ipynb</a>).</p>
+        run using a pipeline version (<a href="https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/tools/benchmarks/run_service_api.ipynb">https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/tools/benchmarks/run_service_api.ipynb</a>).</p>
         </td>
                     </tr>
                     <tr>
@@ -2067,7 +2067,7 @@ <h3 class="panel-title"><span class="operation-name">POST</span> <strong>/apis/v
             changes to the pipeline&apos;s most recent pipeline version. If there are no
             remaining pipeline versions, the pipeline will have no default version.
             Examines the run_service_api.ipynb notebook to learn more about creating a
-            run using a pipeline version (<a href="https://github.com/kubeflow/pipelines/blob/master/tools/benchmarks/run_service_api.ipynb">https://github.com/kubeflow/pipelines/blob/master/tools/benchmarks/run_service_api.ipynb</a>).</div>
+            run using a pipeline version (<a href="https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/tools/benchmarks/run_service_api.ipynb">https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/tools/benchmarks/run_service_api.ipynb</a>).</div>
                     <h3 class="panel-title"><span class="operation-name">DELETE</span> <strong>/apis/v1beta1/pipeline_versions/{version_id}</strong></h3>
                         Tags:
                             <a href="#tag-PipelineService">PipelineService</a>

content/en/docs/components/pipelines/reference/component-spec.md

@@ -51,7 +51,7 @@ See some examples of real-world
 ## Detailed specification (ComponentSpec)
 
 This section describes the 
-[ComponentSpec](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/components/_structures.py).
+[ComponentSpec](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/components/_structures.py).
 
 ### Metadata
 
@@ -80,7 +80,7 @@ This section describes the
         as hints for pipeline authors and can be used by the pipeline system/UI
         to validate arguments and connections between components. Basic types
         are **String**, **Integer**, **Float**, and **Bool**. See the full list
-        of [types](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/types.py)
+        of [types](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/types.py)
         defined by the Kubeflow Pipelines SDK.
     * `optional`: Specifies if input is optional or not. This is of type
         **Bool**, and defaults to **False**. **Only valid for inputs.**

content/en/docs/components/pipelines/sdk-v2/build-pipeline.ipynb

@@ -242,7 +242,7 @@
     "All outputs are returned as files, using the the paths that Kubeflow Pipelines\n",
     "provides.\n",
     "\n",
-    "[kfp-artifact]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/io_types.py\n",
+    "[kfp-artifact]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/io_types.py\n",
     "\n",
     "Python function-based components make it easier to build pipeline components\n",
     "by building the component specification for you. Python function-based\n",
@@ -411,11 +411,11 @@
     "\n",
     "The following example shows the updated `merge_csv` function.\n",
     "\n",
-    "[web-download-component]: https://github.com/kubeflow/pipelines/blob/master/components/web/Download/component.yaml\n",
+    "[web-download-component]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/components/web/Download/component.yaml\n",
     "[python-function-components]: https://www.kubeflow.org/docs/components/pipelines/sdk-v2/python-function-components/\n",
-    "[input]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/io_types.py\n",
-    "[output]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/io_types.py\n",
-    "[dsl-component]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/_component.py"
+    "[input]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/io_types.py\n",
+    "[output]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/io_types.py\n",
+    "[dsl-component]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/_component.py"
    ]
   },
   {
@@ -621,7 +621,7 @@
     "    pipeline][conditional].\n",
     "\n",
     "\n",
-    "[conditional]: https://github.com/kubeflow/pipelines/blob/master/samples/tutorials/DSL%20-%20Control%20structures/DSL%20-%20Control%20structures.py\n",
+    "[conditional]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/samples/tutorials/DSL%20-%20Control%20structures/DSL%20-%20Control%20structures.py\n",
     "[k8s-resources]: https://www.kubeflow.org/docs/components/pipelines/sdk/manipulate-resources/"
    ]
   }

content/en/docs/components/pipelines/sdk-v2/build-pipeline.md

@@ -238,7 +238,7 @@ depending on their data type.
 All outputs are returned as files, using the the paths that Kubeflow Pipelines
 provides.
 
-[kfp-artifact]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/io_types.py
+[kfp-artifact]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/io_types.py
 
 Python function-based components make it easier to build pipeline components
 by building the component specification for you. Python function-based
@@ -373,11 +373,11 @@ Learn more about [building Python function-based components][python-function-com
 
 The following example shows the updated `merge_csv` function.
 
-[web-download-component]: https://github.com/kubeflow/pipelines/blob/master/components/web/Download/component.yaml
+[web-download-component]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/components/web/Download/component.yaml
 [python-function-components]: https://www.kubeflow.org/docs/components/pipelines/sdk-v2/python-function-components/
-[input]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/io_types.py
-[output]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/io_types.py
-[dsl-component]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/_component.py
+[input]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/io_types.py
+[output]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/io_types.py
+[dsl-component]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/_component.py
 
 
 ```python
@@ -505,7 +505,7 @@ client.create_run_from_pipeline_func(
     pipeline][conditional].
 
 
-[conditional]: https://github.com/kubeflow/pipelines/blob/master/samples/tutorials/DSL%20-%20Control%20structures/DSL%20-%20Control%20structures.py
+[conditional]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/samples/tutorials/DSL%20-%20Control%20structures/DSL%20-%20Control%20structures.py
 [k8s-resources]: https://www.kubeflow.org/docs/components/pipelines/sdk/manipulate-resources/
 
 

content/en/docs/components/pipelines/sdk-v2/component-development.md

@@ -423,7 +423,7 @@ The following examples demonstrate how to specify your component's interface.
         ]
     ```
 
-[dsl-types]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/types.py
+[dsl-types]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/types.py
 [dsl-type-checking]: https://www.kubeflow.org/docs/components/pipelines/sdk/static-type-checking/
 
 ### Specify your component's metadata
@@ -584,7 +584,7 @@ components/<component group>/<component name>/
 
 See this [sample component][org-sample] for a real-life component example.
 
-[org-sample]: https://github.com/kubeflow/pipelines/tree/master/components/sample/keras/train_classifier
+[org-sample]: https://github.com/kubeflow/pipelines/tree/sdk/release-1.8/components/sample/keras/train_classifier
 
 ## Next steps
 
@@ -606,4 +606,4 @@ See this [sample component][org-sample] for a real-life component example.
   resources](/docs/examples/shared-resources/).
 
 
-[kfp-artifact]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/io_types.py
+[kfp-artifact]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/io_types.py

content/en/docs/components/pipelines/sdk-v2/python-function-components.ipynb

@@ -309,7 +309,7 @@
     "All outputs are returned as files, using the the paths that Kubeflow Pipelines\n",
     "provides.\n",
     "\n",
-    "[kfp-artifact]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/io_types.py\n",
+    "[kfp-artifact]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/io_types.py\n",
     "\n",
     "The following sections describe how to pass parameters and artifacts to your function. \n",
     "\n",
@@ -340,7 +340,7 @@
     "[kfp-metrics]: https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/\n",
     "[input-path]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.InputPath\n",
     "[output-path]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.OutputPath\n",
-    "[vs-dsl-component]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/v2/dsl/component_decorator.py"
+    "[vs-dsl-component]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/v2/components/component_decorator.py"
    ]
   },
   {
@@ -467,7 +467,7 @@
     "\n",
     "[input]: https://github.com/kubeflow/pipelines/blob/c5daa7532d18687b180badfca8d750c801805712/sdk/python/kfp/dsl/io_types.py\n",
     "[output]: https://github.com/kubeflow/pipelines/blob/c5daa7532d18687b180badfca8d750c801805712/sdk/python/kfp/dsl/io_types.py\n",
-    "[kfp-artifact]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/io_types.py\n",
+    "[kfp-artifact]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/io_types.py\n",
     "\n"
    ]
   },

content/en/docs/components/pipelines/sdk-v2/python-function-components.md

@@ -279,7 +279,7 @@ depending on their data type.
 All outputs are returned as files, using the the paths that Kubeflow Pipelines
 provides.
 
-[kfp-artifact]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/io_types.py
+[kfp-artifact]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/io_types.py
 
 The following sections describe how to pass parameters and artifacts to your function. 
 
@@ -310,7 +310,7 @@ The following example demonstrates how to return multiple outputs by value.
 [kfp-metrics]: https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/
 [input-path]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.InputPath
 [output-path]: https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.components.html#kfp.components.OutputPath
-[vs-dsl-component]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/v2/dsl/component_decorator.py
+[vs-dsl-component]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/v2/components/component_decorator.py
 
 
 ```python
@@ -419,7 +419,7 @@ To return a file as an output, use one of the following type annotations:
 
 [input]: https://github.com/kubeflow/pipelines/blob/c5daa7532d18687b180badfca8d750c801805712/sdk/python/kfp/dsl/io_types.py
 [output]: https://github.com/kubeflow/pipelines/blob/c5daa7532d18687b180badfca8d750c801805712/sdk/python/kfp/dsl/io_types.py
-[kfp-artifact]: https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/io_types.py
+[kfp-artifact]: https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/io_types.py
 
 
 

content/en/docs/components/pipelines/sdk-v2/v2-compatibility.md

@@ -64,7 +64,7 @@ introduces the following changes:
             are always passed by value, which means that they are inserted into the
             command used to execute the component. Parameters are stored in ML Metadata. 
 
-        *   [Artifacts](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/io_types.py)
+        *   [Artifacts](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/io_types.py)
             are larger inputs or outputs, such as datasets or models. Input
             artifacts are always passed as a reference to a path. 
 
@@ -75,7 +75,7 @@ introduces the following changes:
 *   The following changes affect how you define a pipeline:
 
     *   Pipeline functions must be decorated with
-         [`@kfp.dsl.pipeline`](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/dsl/_pipeline.py). Specify the following arguments for the 
+         [`@kfp.dsl.pipeline`](https://github.com/kubeflow/pipelines/blob/sdk/release-1.8/sdk/python/kfp/dsl/_pipeline.py). Specify the following arguments for the 
          `@pipeline` annotation.
 
         *   `name`: The pipeline name is used when querying MLMD to store or lookup