Test: e2e Cypress migration from ODS-2206 (#3580)

* test: Cypress Pipelines e2e ODS2206

* Save point

* checkpoint

* try to wait to the modal to be closed

* lint fixes

* Unskip first test

* Split into two different files

* rename file

* Lint fixes

* fix application issue

* Apply Purva's solution

* workbenches

* fix pip config retrieving

* Hope It does fix the mocked tests

* Hope this really fixes the mocked tests

* delete only from globalDistributedWorkloads

frontend/src/__tests__/cypress/cypress/fixtures/resources/pipelines/iris_pipeline_pip_index_url_compiled.yaml

@@ -0,0 +1,296 @@
+# PIPELINE DEFINITION
+# Name: iris-training-pipeline
+# Inputs:
+#    neighbors: int [Default: 3.0]
+#    standard_scaler: bool [Default: True]
+# Outputs:
+#    train-model-metrics: system.ClassificationMetrics
+components:
+  comp-create-dataset:
+    executorLabel: exec-create-dataset
+    outputDefinitions:
+      artifacts:
+        iris_dataset:
+          artifactType:
+            schemaTitle: system.Dataset
+            schemaVersion: 0.0.1
+  comp-normalize-dataset:
+    executorLabel: exec-normalize-dataset
+    inputDefinitions:
+      artifacts:
+        input_iris_dataset:
+          artifactType:
+            schemaTitle: system.Dataset
+            schemaVersion: 0.0.1
+      parameters:
+        standard_scaler:
+          parameterType: BOOLEAN
+    outputDefinitions:
+      artifacts:
+        normalized_iris_dataset:
+          artifactType:
+            schemaTitle: system.Dataset
+            schemaVersion: 0.0.1
+  comp-train-model:
+    executorLabel: exec-train-model
+    inputDefinitions:
+      artifacts:
+        normalized_iris_dataset:
+          artifactType:
+            schemaTitle: system.Dataset
+            schemaVersion: 0.0.1
+      parameters:
+        n_neighbors:
+          parameterType: NUMBER_INTEGER
+    outputDefinitions:
+      artifacts:
+        metrics:
+          artifactType:
+            schemaTitle: system.ClassificationMetrics
+            schemaVersion: 0.0.1
+        model:
+          artifactType:
+            schemaTitle: system.Model
+            schemaVersion: 0.0.1
+deploymentSpec:
+  executors:
+    exec-create-dataset:
+      container:
+        args:
+          - --executor_input
+          - '{{$}}'
+          - --function_to_execute
+          - create_dataset
+        command:
+          - sh
+          - -c
+          - "\nif ! [ -x \"$(command -v pip)\" ]; then\n    python3 -m ensurepip ||\
+            \ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
+            \ python3 -m pip install --quiet --no-warn-script-location --index-url $PIP_INDEX_URL\
+            \ --trusted-host $PIP_TRUSTED_HOST 'kfp==2.9.0' '--no-deps' 'typing-extensions>=3.7.4,<5;\
+            \ python_version<\"3.9\"'  &&  python3 -m pip install --quiet --no-warn-script-location\
+            \ --index-url $PIP_INDEX_URL --trusted-host $PIP_TRUSTED_HOST 'pandas==2.2.0'\
+            \ && \"$0\" \"$@\"\n"
+          - sh
+          - -ec
+          - 'program_path=$(mktemp -d)
+
+
+            printf "%s" "$0" > "$program_path/ephemeral_component.py"
+
+            _KFP_RUNTIME=true python3 -m kfp.dsl.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"
+
+            '
+          - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
+            \ *\n\ndef create_dataset(iris_dataset: Output[Dataset]):\n    from io import\
+            \ StringIO  # noqa: PLC0415\n\n    import pandas as pd  # noqa: PLC0415\n\
+            \n    data = \"\"\"\n    5.1,3.5,1.4,0.2,Iris-setosa\n    4.9,3.0,1.4,0.2,Iris-setosa\n\
+            \    4.7,3.2,1.3,0.2,Iris-setosa\n    4.6,3.1,1.5,0.2,Iris-setosa\n    5.0,3.6,1.4,0.2,Iris-setosa\n\
+            \    5.7,3.8,1.7,0.3,Iris-setosa\n    5.1,3.8,1.5,0.3,Iris-setosa\n    5.4,3.4,1.7,0.2,Iris-setosa\n\
+            \    5.1,3.7,1.5,0.4,Iris-setosa\n    5.1,3.4,1.5,0.2,Iris-setosa\n    5.0,3.5,1.3,0.3,Iris-setosa\n\
+            \    4.5,2.3,1.3,0.3,Iris-setosa\n    4.4,3.2,1.3,0.2,Iris-setosa\n    5.0,3.5,1.6,0.6,Iris-setosa\n\
+            \    5.1,3.8,1.9,0.4,Iris-setosa\n    4.8,3.0,1.4,0.3,Iris-setosa\n    5.1,3.8,1.6,0.2,Iris-setosa\n\
+            \    4.6,3.2,1.4,0.2,Iris-setosa\n    5.3,3.7,1.5,0.2,Iris-setosa\n    5.0,3.3,1.4,0.2,Iris-setosa\n\
+            \    7.0,3.2,4.7,1.4,Iris-versicolor\n    6.4,3.2,4.5,1.5,Iris-versicolor\n\
+            \    6.9,3.1,4.9,1.5,Iris-versicolor\n    5.5,2.3,4.0,1.3,Iris-versicolor\n\
+            \    6.5,2.8,4.6,1.5,Iris-versicolor\n    6.2,2.2,4.5,1.5,Iris-versicolor\n\
+            \    5.6,2.5,3.9,1.1,Iris-versicolor\n    5.9,3.2,4.8,1.8,Iris-versicolor\n\
+            \    6.1,2.8,4.0,1.3,Iris-versicolor\n    6.3,2.5,4.9,1.5,Iris-versicolor\n\
+            \    6.1,2.8,4.7,1.2,Iris-versicolor\n    6.4,2.9,4.3,1.3,Iris-versicolor\n\
+            \    6.6,3.0,4.4,1.4,Iris-versicolor\n    5.6,2.7,4.2,1.3,Iris-versicolor\n\
+            \    5.7,3.0,4.2,1.2,Iris-versicolor\n    5.7,2.9,4.2,1.3,Iris-versicolor\n\
+            \    6.2,2.9,4.3,1.3,Iris-versicolor\n    5.1,2.5,3.0,1.1,Iris-versicolor\n\
+            \    5.7,2.8,4.1,1.3,Iris-versicolor\n    6.3,3.3,6.0,2.5,Iris-virginica\n\
+            \    5.8,2.7,5.1,1.9,Iris-virginica\n    7.1,3.0,5.9,2.1,Iris-virginica\n\
+            \    6.3,2.9,5.6,1.8,Iris-virginica\n    6.5,3.0,5.8,2.2,Iris-virginica\n\
+            \    6.9,3.1,5.1,2.3,Iris-virginica\n    5.8,2.7,5.1,1.9,Iris-virginica\n\
+            \    6.8,3.2,5.9,2.3,Iris-virginica\n    6.7,3.3,5.7,2.5,Iris-virginica\n\
+            \    6.7,3.0,5.2,2.3,Iris-virginica\n    6.3,2.5,5.0,1.9,Iris-virginica\n\
+            \    6.5,3.0,5.2,2.0,Iris-virginica\n    6.2,3.4,5.4,2.3,Iris-virginica\n\
+            \    5.9,3.0,5.1,1.8,Iris-virginica\n    \"\"\"\n    col_names = [\"Sepal_Length\"\
+            , \"Sepal_Width\", \"Petal_Length\", \"Petal_Width\", \"Labels\"]\n    df\
+            \ = pd.read_csv(StringIO(data), names=col_names)\n\n    with open(iris_dataset.path,\
+            \ \"w\") as f:\n        df.to_csv(f)\n\n"
+        image: registry.redhat.io/ubi8/python-39@sha256:3523b184212e1f2243e76d8094ab52b01ea3015471471290d011625e1763af61
+    exec-normalize-dataset:
+      container:
+        args:
+          - --executor_input
+          - '{{$}}'
+          - --function_to_execute
+          - normalize_dataset
+        command:
+          - sh
+          - -c
+          - "\nif ! [ -x \"$(command -v pip)\" ]; then\n    python3 -m ensurepip ||\
+            \ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
+            \ python3 -m pip install --quiet --no-warn-script-location --index-url $PIP_INDEX_URL\
+            \ --trusted-host $PIP_TRUSTED_HOST 'kfp==2.9.0' '--no-deps' 'typing-extensions>=3.7.4,<5;\
+            \ python_version<\"3.9\"'  &&  python3 -m pip install --quiet --no-warn-script-location\
+            \ --index-url $PIP_INDEX_URL --trusted-host $PIP_TRUSTED_HOST 'pandas==2.2.0'\
+            \ 'scikit-learn==1.4.0' && \"$0\" \"$@\"\n"
+          - sh
+          - -ec
+          - 'program_path=$(mktemp -d)
+
+
+            printf "%s" "$0" > "$program_path/ephemeral_component.py"
+
+            _KFP_RUNTIME=true python3 -m kfp.dsl.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"
+
+            '
+          - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
+            \ *\n\ndef normalize_dataset(\n    input_iris_dataset: Input[Dataset],\n\
+            \    normalized_iris_dataset: Output[Dataset],\n    standard_scaler: bool,\n\
+            ):\n    import pandas as pd  # noqa: PLC0415\n    from sklearn.preprocessing\
+            \ import MinMaxScaler, StandardScaler  # noqa: PLC0415\n\n    with open(input_iris_dataset.path)\
+            \ as f:\n        df = pd.read_csv(f)\n    labels = df.pop(\"Labels\")\n\n\
+            \    scaler = StandardScaler() if standard_scaler else MinMaxScaler()\n\n\
+            \    df = pd.DataFrame(scaler.fit_transform(df))\n    df[\"Labels\"] = labels\n\
+            \    normalized_iris_dataset.metadata[\"state\"] = \"Normalized\"\n    with\
+            \ open(normalized_iris_dataset.path, \"w\") as f:\n        df.to_csv(f)\n\
+            \n"
+        image: registry.redhat.io/ubi8/python-39@sha256:3523b184212e1f2243e76d8094ab52b01ea3015471471290d011625e1763af61
+    exec-train-model:
+      container:
+        args:
+          - --executor_input
+          - '{{$}}'
+          - --function_to_execute
+          - train_model
+        command:
+          - sh
+          - -c
+          - "\nif ! [ -x \"$(command -v pip)\" ]; then\n    python3 -m ensurepip ||\
+            \ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
+            \ python3 -m pip install --quiet --no-warn-script-location --index-url $PIP_INDEX_URL\
+            \ --trusted-host $PIP_TRUSTED_HOST 'kfp==2.9.0' '--no-deps' 'typing-extensions>=3.7.4,<5;\
+            \ python_version<\"3.9\"'  &&  python3 -m pip install --quiet --no-warn-script-location\
+            \ --index-url $PIP_INDEX_URL --trusted-host $PIP_TRUSTED_HOST 'pandas==2.2.0'\
+            \ 'scikit-learn==1.4.0' && \"$0\" \"$@\"\n"
+          - sh
+          - -ec
+          - 'program_path=$(mktemp -d)
+
+
+            printf "%s" "$0" > "$program_path/ephemeral_component.py"
+
+            _KFP_RUNTIME=true python3 -m kfp.dsl.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"
+
+            '
+          - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
+            \ *\n\ndef train_model(\n    normalized_iris_dataset: Input[Dataset],\n\
+            \    model: Output[Model],\n    metrics: Output[ClassificationMetrics],\n\
+            \    n_neighbors: int,\n):\n    import pickle  # noqa: PLC0415\n\n    import\
+            \ pandas as pd  # noqa: PLC0415\n    from sklearn.metrics import confusion_matrix\
+            \  # noqa: PLC0415\n    from sklearn.model_selection import cross_val_predict,\
+            \ train_test_split  # noqa: PLC0415\n    from sklearn.neighbors import KNeighborsClassifier\
+            \  # noqa: PLC0415\n\n    with open(normalized_iris_dataset.path) as f:\n\
+            \        df = pd.read_csv(f)\n\n    y = df.pop(\"Labels\")\n    X = df\n\
+            \n    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\
+            \  # noqa: F841\n\n    clf = KNeighborsClassifier(n_neighbors=n_neighbors)\n\
+            \    clf.fit(X_train, y_train)\n\n    predictions = cross_val_predict(clf,\
+            \ X_train, y_train, cv=3)\n    metrics.log_confusion_matrix(\n        [\"\
+            Iris-Setosa\", \"Iris-Versicolour\", \"Iris-Virginica\"],\n        confusion_matrix(y_train,\
+            \ predictions).tolist(),  # .tolist() to convert np array to list.\n   \
+            \ )\n\n    model.metadata[\"framework\"] = \"scikit-learn\"\n    with open(model.path,\
+            \ \"wb\") as f:\n        pickle.dump(clf, f)\n\n"
+        image: registry.redhat.io/ubi8/python-39@sha256:3523b184212e1f2243e76d8094ab52b01ea3015471471290d011625e1763af61
+pipelineInfo:
+  name: iris-training-pipeline
+root:
+  dag:
+    outputs:
+      artifacts:
+        train-model-metrics:
+          artifactSelectors:
+            - outputArtifactKey: metrics
+              producerSubtask: train-model
+    tasks:
+      create-dataset:
+        cachingOptions: {}
+        componentRef:
+          name: comp-create-dataset
+        taskInfo:
+          name: create-dataset
+      normalize-dataset:
+        cachingOptions: {}
+        componentRef:
+          name: comp-normalize-dataset
+        dependentTasks:
+          - create-dataset
+        inputs:
+          artifacts:
+            input_iris_dataset:
+              taskOutputArtifact:
+                outputArtifactKey: iris_dataset
+                producerTask: create-dataset
+          parameters:
+            standard_scaler:
+              componentInputParameter: standard_scaler
+        taskInfo:
+          name: normalize-dataset
+      train-model:
+        cachingOptions: {}
+        componentRef:
+          name: comp-train-model
+        dependentTasks:
+          - normalize-dataset
+        inputs:
+          artifacts:
+            normalized_iris_dataset:
+              taskOutputArtifact:
+                outputArtifactKey: normalized_iris_dataset
+                producerTask: normalize-dataset
+          parameters:
+            n_neighbors:
+              componentInputParameter: neighbors
+        taskInfo:
+          name: train-model
+  inputDefinitions:
+    parameters:
+      neighbors:
+        defaultValue: 3.0
+        isOptional: true
+        parameterType: NUMBER_INTEGER
+      standard_scaler:
+        defaultValue: true
+        isOptional: true
+        parameterType: BOOLEAN
+  outputDefinitions:
+    artifacts:
+      train-model-metrics:
+        artifactType:
+          schemaTitle: system.ClassificationMetrics
+          schemaVersion: 0.0.1
+schemaVersion: 2.1.0
+sdkVersion: kfp-2.9.0
+---
+platforms:
+  kubernetes:
+    deploymentSpec:
+      executors:
+        exec-create-dataset:
+          configMapAsEnv:
+            - configMapName: ds-pipeline-custom-env-vars
+              keyToEnv:
+                - configMapKey: pip_index_url
+                  envVar: PIP_INDEX_URL
+                - configMapKey: pip_trusted_host
+                  envVar: PIP_TRUSTED_HOST
+        exec-normalize-dataset:
+          configMapAsEnv:
+            - configMapName: ds-pipeline-custom-env-vars
+              keyToEnv:
+                - configMapKey: pip_index_url
+                  envVar: PIP_INDEX_URL
+                - configMapKey: pip_trusted_host
+                  envVar: PIP_TRUSTED_HOST
+        exec-train-model:
+          configMapAsEnv:
+            - configMapName: ds-pipeline-custom-env-vars
+              keyToEnv:
+                - configMapKey: pip_index_url
+                  envVar: PIP_INDEX_URL
+                - configMapKey: pip_trusted_host
+                  envVar: PIP_TRUSTED_HOST

frontend/src/__tests__/cypress/cypress/pages/components/subComponents/SearchSelector.ts

@@ -9,12 +9,13 @@ export class SearchSelector extends SubComponentBase {
     return this.findScope().findByTestId(`${this.selectorId}-${suffix}`);
   }
 
-  findItem(name: string): Cypress.Chainable<JQuery<HTMLElement>> {
-    return this.findToggleButton().findMenuItem(name);
+  findItem(name: string, useMenuList: boolean): Cypress.Chainable<JQuery<HTMLElement>> {
+    const list = useMenuList ? this.findMenuList() : this.findResultTableList();
+    return list.contains(name).should('exist');
   }
 
-  selectItem(name: string): void {
-    this.findItem(name).click();
+  selectItem(name: string, useMenuList = false): void {
+    this.findItem(name, useMenuList).click();
   }
 
   findSearchInput(): Cypress.Chainable<JQuery<HTMLElement>> {
@@ -25,6 +26,10 @@ export class SearchSelector extends SubComponentBase {
     return this.findContextualItem('toggle');
   }
 
+  findResultTableList(): Cypress.Chainable<JQuery<HTMLElement>> {
+    return this.findContextualItem('table-list');
+  }
+
   findSearchHelpText(): Cypress.Chainable<JQuery<HTMLElement>> {
     return this.findContextualItem('searchHelpText');
   }
@@ -36,4 +41,16 @@ export class SearchSelector extends SubComponentBase {
   findMenuList(): Cypress.Chainable<JQuery<HTMLElement>> {
     return this.findContextualItem('menuList');
   }
+
+  // Search for an item by typing into the search input
+  searchItem(name: string): void {
+    this.findSearchInput().clear().type(name);
+  }
+
+  // Perform the entire process: open, search, and select
+  openAndSelectItem(name: string, useMenuList = false): void {
+    this.findToggleButton().click();
+    this.searchItem(name);
+    this.selectItem(name, useMenuList);
+  }
 }

frontend/src/__tests__/cypress/cypress/pages/components/table.ts

@@ -17,7 +17,7 @@ export class TableRow extends Contextual<HTMLTableRowElement> {
   }
 
   findKebabAction(name: string): Cypress.Chainable<JQuery<HTMLElement>> {
-    return this.find().findKebabAction(name);
+    return this.find().findKebabAction(name).should('exist').and('be.visible');
   }
 
   findKebab(): Cypress.Chainable<JQuery<HTMLElement>> {

frontend/src/__tests__/cypress/cypress/pages/pipelines/pipelineRunsGlobal.ts

@@ -1,4 +1,5 @@
 import { DeleteModal } from '~/__tests__/cypress/cypress/pages/components/DeleteModal';
+import { appChrome } from '~/__tests__/cypress/cypress/pages/appChrome';
 
 class PipelineRunsGlobal {
   visit(projectName: string, runType?: 'active' | 'archived' | 'scheduled') {
@@ -10,6 +11,11 @@ class PipelineRunsGlobal {
     this.wait();
   }
 
+  navigate() {
+    appChrome.findNavItem('Runs', 'Data Science Pipelines').click();
+    this.wait();
+  }
+
   private wait() {
     cy.findByTestId('app-page-title').contains('Runs');
     cy.testA11y();
@@ -32,7 +38,7 @@ class PipelineRunsGlobal {
   }
 
   findProjectSelect() {
-    return cy.findByTestId('project-selector-dropdown');
+    return cy.findByTestId('project-selector-toggle');
   }
 
   findCreateRunButton() {
@@ -60,7 +66,12 @@ class PipelineRunsGlobal {
   }
 
   selectProjectByName(name: string) {
-    this.findProjectSelect().findDropdownItem(name).click();
+    this.findProjectSelect().click();
+    cy.findByTestId('project-selector-search').fill(name);
+    cy.findByTestId('project-selector-menuList')
+      .contains('button', name)
+      .should('be.visible')
+      .click();
   }
 }