Merge pull request opendatahub-io#45 from gmfrasca/update-tile-route

chore(manifests): Update Dashboard Tile Route and repo links

Author: anishasthana

manifests/opendatahub/overlays/integration-odhdashboard/odhapplications/data-science-pipelines-odhapplication.yaml

@@ -6,8 +6,8 @@ metadata:
     opendatahub.io/categories: 'Model development,Model training,Model optimization,Data analysis,Data preprocessing'
 spec:
   beta: true
-  betaTitle: Data Science Pipelines
-  betaText: This application is available for early access prior to official release.
+  betaTitle: Data Science Pipelines (beta)
+  betaText: This beta application is available for early access prior to official release.
   displayName: Data Science Pipelines
   description: Data Science Pipelines is a workflow platform with a focus on enabling Machine Learning operations such as Model development, experimentation, orchestration and automation.
   provider: Red Hat
@@ -27,16 +27,16 @@ spec:
     validationConfigMap: ds-pipelines-dashboardtile-validation-result
   kfdefApplications: []
   #kfdefApplications: ['data-science-pipelines']  # https://github.com/opendatahub-io/odh-dashboard/issues/625
-  route: ml-pipeline-ui
-  internalRoute: ml-pipeline-ui
+  route: ds-pipeline-ui
+  internalRoute: ds-pipeline-ui
   getStartedMarkDown: |-
     # Getting Started With Data Science Pipelines
-    Below are the list of samples that are currently running end to end taking the compiled Tekton yaml and deploying on a Tekton cluster directly. If you are interested more in the larger list of pipelines samples we are testing for whether they can be 'compiled to Tekton' format, please [look at the corresponding status page](https://github.com/opendatahub-io/ml-pipelines/tree/master/sdk/python/tests/README.md)
-    [DSP Tekton User Guide](https://github.com/opendatahub-io/ml-pipelines/tree/master/guides/kfp-user-guide) is a guideline for the possible ways to develop and consume Data Science Pipelines. It's recommended to go over at least one of the methods in the user guide before heading into the KFP Tekton Samples.
+    Below are the list of samples that are currently running end to end taking the compiled Tekton yaml and deploying on a Tekton cluster directly. If you are interested more in the larger list of pipelines samples we are testing for whether they can be 'compiled to Tekton' format, please [look at the corresponding status page](https://github.com/opendatahub-io/data-science-pipelines/tree/master/sdk/python/tests/README.md)
+    [DSP Tekton User Guide](https://github.com/opendatahub-io/data-science-pipelines/tree/master/guides/kfp-user-guide) is a guideline for the possible ways to develop and consume Data Science Pipelines. It's recommended to go over at least one of the methods in the user guide before heading into the KFP Tekton Samples.
     ## Prerequisites 
     - Install [OpenShift Pipelines Operator](https://docs.openshift.com/container-platform/4.7/cicd/pipelines/installing-pipelines.html). Then connect the cluster to the current shell with `oc` 
     
-    - Install [kfp-tekton](https://github.com/opendatahub-io/ml-pipelines/tree/master/sdk/README.md) SDK
+    - Install [kfp-tekton](https://github.com/opendatahub-io/data-science-pipelines/tree/master/sdk/README.md) SDK
 
         ```
         # Set up the python virtual environment
@@ -48,24 +48,24 @@ spec:
         ```
 
     ## Samples
-    - [MNIST End to End example with DSP components](https://github.com/opendatahub-io/ml-pipelines/tree/master/samples/e2e-mnist)
+    - [MNIST End to End example with DSP components](https://github.com/opendatahub-io/data-science-pipelines/tree/master/samples/e2e-mnist)
 
-    - [Hyperparameter tuning using Katib](https://github.com/opendatahub-io/ml-pipelines/tree/master/samples/katib)
+    - [Hyperparameter tuning using Katib](https://github.com/opendatahub-io/data-science-pipelines/tree/master/samples/katib)
 
-    - [Trusted AI Pipeline with AI Fairness 360 and Adversarial Robustness 360 components](https://github.com/opendatahub-io/ml-pipelines/tree/master/samples/trusted-ai)
+    - [Trusted AI Pipeline with AI Fairness 360 and Adversarial Robustness 360 components](https://github.com/opendatahub-io/data-science-pipelines/tree/master/samples/trusted-ai)
 
-    - [Training and Serving Models with Watson Machine Learning](https://github.com/opendatahub-io/ml-pipelines/tree/master/samples/watson-train-serve#training-and-serving-models-with-watson-machine-learning)
+    - [Training and Serving Models with Watson Machine Learning](https://github.com/opendatahub-io/data-science-pipelines/tree/master/samples/watson-train-serve#training-and-serving-models-with-watson-machine-learning)
 
-    - [Lightweight python components example](https://github.com/opendatahub-io/ml-pipelines/tree/master/samples/lightweight-component)
+    - [Lightweight python components example](https://github.com/opendatahub-io/data-science-pipelines/tree/master/samples/lightweight-component)
 
-    - [The flip-coin pipeline](https://github.com/opendatahub-io/ml-pipelines/tree/master/samples/flip-coin)
+    - [The flip-coin pipeline](https://github.com/opendatahub-io/data-science-pipelines/tree/master/samples/flip-coin)
 
-    - [Nested pipeline example](https://github.com/opendatahub-io/ml-pipelines/tree/master/samples/nested-pipeline)
+    - [Nested pipeline example](https://github.com/opendatahub-io/data-science-pipelines/tree/master/samples/nested-pipeline)
 
-    - [Pipeline with Nested loops](https://github.com/opendatahub-io/ml-pipelines/tree/master/samples/nested-loops)
+    - [Pipeline with Nested loops](https://github.com/opendatahub-io/data-science-pipelines/tree/master/samples/nested-loops)
 
-    - [Using Tekton Custom Task on DSP](https://github.com/opendatahub-io/ml-pipelines/tree/master/samples/tekton-custom-task)
+    - [Using Tekton Custom Task on DSP](https://github.com/opendatahub-io/data-science-pipelines/tree/master/samples/tekton-custom-task)
 
-    - [The flip-coin pipeline using custom task](https://github.com/opendatahub-io/ml-pipelines/tree/master/samples/flip-coin-custom-task)
+    - [The flip-coin pipeline using custom task](https://github.com/opendatahub-io/data-science-pipelines/tree/master/samples/flip-coin-custom-task)
 
-    - [Retrieve DSP run metadata using Kubernetes downstream API](https://github.com/opendatahub-io/ml-pipelines/tree/master/samples/k8s-downstream-api)
+    - [Retrieve DSP run metadata using Kubernetes downstream API](https://github.com/opendatahub-io/data-science-pipelines/tree/master/samples/k8s-downstream-api)

manifests/opendatahub/overlays/integration-odhdashboard/odhquickstarts/data-science-pipelines-odhquickstart.yaml

@@ -34,8 +34,8 @@ spec:
     - title: Install Python SDK and compile sample pipeline
       description: |-
         ### Install the Kubeflow Pipelines Python SDK
-        1. Follow the [Kubeflow Pipelines Tekton Python SDK Installation instructions](https://github.com/opendatahub-io/ml-pipelines/blob/master/samples/README.md#prerequisites)
-        2. Download, clone or copy the [flip-coin example pipeline](https://github.com/opendatahub-io/ml-pipelines/blob/master/samples/flip-coin/condition.py)
+        1. Follow the [Kubeflow Pipelines Tekton Python SDK Installation instructions](https://github.com/opendatahub-io/data-science-pipelines/blob/master/samples/README.md#prerequisites)
+        2. Download, clone or copy the [flip-coin example pipeline](https://github.com/opendatahub-io/data-science-pipelines/blob/master/samples/flip-coin/condition.py)
         3. Compile the python pipeline defintion into a Tekton YAML:
         ```
         python condition.py