update kubeflow dip-catalog

charts/kubeflow/tests/gh-actions/kf-objects/katib_test.yaml

@@ -0,0 +1,56 @@
+---
+---
+apiVersion: kubeflow.org/v1beta1
+kind: Experiment
+metadata:
+  namespace: kubeflow-user
+  name: grid
+spec:
+  objective:
+    type: minimize
+    goal: 0.1
+    objectiveMetricName: loss
+  algorithm:
+    algorithmName: grid
+  parallelTrialCount: 2
+  maxTrialCount: 2
+  maxFailedTrialCount: 2
+  parameters:
+    - name: lr
+      parameterType: double
+      feasibleSpace:
+        min: "0.01"
+        step: "0.005"
+        max: "0.05"
+    - name: momentum
+      parameterType: double
+      feasibleSpace:
+        min: "0.5"
+        step: "0.1"
+        max: "0.9"
+  trialTemplate:
+    primaryContainerName: training-container
+    trialParameters:
+      - name: learningRate
+        description: Learning rate for the training model
+        reference: lr
+      - name: momentum
+        description: Momentum for the training model
+        reference: momentum
+    trialSpec:
+      apiVersion: batch/v1
+      kind: Job
+      spec:
+        template:
+          spec:
+            containers:
+              - name: training-container
+                image: docker.io/kubeflowkatib/pytorch-mnist-cpu:latest
+                command:
+                  - "python3"
+                  - "/opt/pytorch-mnist/mnist.py"
+                  - "--epochs=1"
+                  - "--batch-size=16"
+                  - "--lr=${trialParameters.learningRate}"
+                  - "--momentum=${trialParameters.momentum}"
+            restartPolicy: Never

charts/kubeflow/tests/gh-actions/kf-objects/kserve_test.yaml

@@ -0,0 +1,15 @@
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+  name: "sklearn-iris"
+spec:
+  predictor:
+    sklearn:
+      resources:
+          limits:
+            cpu: "1"
+            memory: 2Gi
+          requests:
+            cpu: "0.1"
+            memory: 200M 
+      storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"

charts/kubeflow/tests/gh-actions/kf-objects/notebook.test.kubeflow-user-example.com.yaml

@@ -0,0 +1,27 @@
+apiVersion: kubeflow.org/v1
+kind: Notebook
+metadata:
+  annotations:
+    notebooks.kubeflow.org/creator: user@example.com
+    notebooks.kubeflow.org/server-type: jupyter
+  generation: 1
+  labels:
+    access-ml-pipeline: "true"
+    app: test
+  name: test
+  namespace: kubeflow-user-example-com
+spec:
+  template:
+    spec:
+      containers:
+      - name: test
+        image: kubeflownotebookswg/jupyter-scipy:v1.9.0-rc.1
+        imagePullPolicy: IfNotPresent
+        resources:
+          limits:
+            cpu: "0.6"
+            memory: 1.2Gi
+          requests:
+            cpu: "0.5"
+            memory: 1Gi
+      serviceAccountName: default-editor

charts/kubeflow/tests/gh-actions/kf-objects/poddefaults.access-ml-pipeline.kubeflow-user-example-com.yaml

@@ -0,0 +1,25 @@
+apiVersion: kubeflow.org/v1alpha1
+kind: PodDefault
+metadata:
+  name: access-ml-pipeline
+  namespace: kubeflow-user-example-com
+spec:
+  desc: Allow access to Kubeflow Pipelines
+  selector:
+    matchLabels:
+      access-ml-pipeline: "true"
+  env:
+    - name: KF_PIPELINES_SA_TOKEN_PATH
+      value: /var/run/secrets/kubeflow/pipelines/token
+  volumes:
+    - name: volume-kf-pipeline-token
+      projected:
+        sources:
+          - serviceAccountToken:
+              path: token
+              expirationSeconds: 7200
+              audience: pipelines.kubeflow.org
+  volumeMounts:
+    - mountPath: /var/run/secrets/kubeflow/pipelines
+      name: volume-kf-pipeline-token
+      readOnly: true

charts/kubeflow/tests/gh-actions/kf-objects/test_pipeline.py

@@ -0,0 +1,29 @@
+import kfp
+from kfp import dsl
+import kfp.components as comp
+
+
+@comp.create_component_from_func
+def echo_op():
+    print("Test pipeline")
+
+
+@dsl.pipeline(name="test-pipeline", description="A test pipeline.")
+def hello_world_pipeline():
+    echo_task = echo_op()
+
+
+if __name__ == "__main__":
+    # Run the Kubeflow Pipeline in the user's namespace.
+    kfp_client = kfp.Client(
+        host="http://localhost:3000", namespace="kubeflow-user-example-com"
+    )
+    kfp_client.runs.api_client.default_headers.update(
+        {"kubeflow-userid": "kubeflow-user-example-com"}
+    )
+    # create the KFP run
+    run_id = kfp_client.create_run_from_pipeline_func(
+        hello_world_pipeline,
+        namespace="kubeflow-user-example-com",
+        arguments={},
+    ).run_id

charts/kubeflow/tests/gh-actions/kf-objects/tfjob.yaml

@@ -0,0 +1,21 @@
+apiVersion: "kubeflow.org/v1"
+kind: TFJob
+metadata:
+  name: tfjob-simple
+  namespace: kubeflow
+spec:
+   tfReplicaSpecs:
+     Worker:
+       replicas: 2
+       restartPolicy: OnFailure
+       template:
+         spec:
+           containers:
+             - name: tensorflow
+               image: gcr.io/kubeflow-ci/tf-mnist-with-summaries:1.0
+               command:
+                 - "python"
+                 - "/var/tf_mnist/mnist_with_summaries.py"
+                 - "--log_dir=/train/logs"
+                 - "--learning_rate=0.01"
+                 - "--batch_size=150"