update day-1 handson instruction

HandsOn/day1/README.md

@@ -5,23 +5,61 @@ the Kubeflow pipeline using the permission we have within the Kubeflow Jupyter N
 
 ## Instructions
 
-1. Go to the Kubeflow dashboard. It should be `<minikube/public_ip>:31380`. Then create a username call `anonymous`. 
+1. Go to the Kubeflow dashboard. It should be `<cluster_id>:30380` or `localhost:8080` if you use port-forward.
+   Log in with the username and password you specified when deploying Kubeflow.
 
-2. Click on the `Jupyter Servers` tab on the left-hand panel.
-![](images/demo1.png)
+2. Set up a `PodDefault` in the user's namespace to allow Notebook Server to access Pipeline APIs. Use the following
+   PodDefault as the template:
+   ```
+   apiVersion: kubeflow.org/v1alpha1
+    kind: PodDefault
+    metadata:
+    name: access-kf-pipeline
+    namespace: example
+    spec:
+    desc: Allow access to KFP
+    selector:
+        matchLabels:
+        access-kf-pipeline: "true"
+    volumeMounts:
+        - mountPath: /var/run/secrets/kubeflow/pipelines
+        name: volume-kf-pipeline-token
+        readOnly: true
+    volumes:
+        - name: volume-kf-pipeline-token
+        projected:
+            sources:
+            - serviceAccountToken:
+                path: token
+                expirationSeconds: 7200
+                audience: pipelines.kubeflow.org
+    env:
+        - name: KF_PIPELINES_SA_TOKEN_PATH
+        value: /var/run/secrets/kubeflow/pipelines/token
+   ```
+   Change the `namespace` to your user's namespace, save the file as `poddefault.yaml`, and use the following command to
+   create the PodDefault:
+   ```
+   kubecel apply -f poddefault.yaml
+   ```
+2. Click on the `Notebooks` tab on the left-hand panel.
+   ![](images/demo1.png)
 
 3. Click on the `New Server` button.
 ![](images/demo2.png)
 
-4. Give any name to your Jupyter Server, and keep the rest with the default settings. At the end of the form, click on the `Launch` button.
-![](images/demo3.png)
-![](images/demo4.png)
+4. Give any name to your Notebook Server, and keep the rest with the default settings.
+   ![](images/demo3.png)
+   In `Configurations` section, select `Allow access to KFP`
+   ![](images/demo3-1.png)
+   At the end of the form, click on the `Launch` button.
+   ![](images/demo4.png)
 
-5. Once the Jupyter Server is ready, click on the `Connect` button to access the Jupyter Server.
-![](images/demo5.png)
+5. Once the Notebook Server is ready, click on the `Connect` button to access the Notebook Server.
+   ![](images/demo5.png)
 
-6. Within the Jupyter Server, click upload and select the `lightweight_component.ipynb` example in this directory. Then click on the notebook and
+6. Within the Notebook Server, click upload and select the `lightweight_component.ipynb` example in this directory. Then click on the notebook and
 start running code using the Jupyter Notebook running on Kubeflow. The rest of the instructions are within the Jupyter Notebook.
-![](images/demo6.png)
-![](images/demo7.png)
+   ![](images/demo6.png)
+   ![](images/demo7.png)
 

HandsOn/day1/lightweight_component.ipynb

@@ -27,14 +27,14 @@
    "source": [
     "# Install the dependency packages\n",
     "!pip install --upgrade pip\n",
-    "!pip install numpy tensorflow kfp-tekton"
+    "!pip install numpy tensorflow kfp-tekton==1.1.1"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Important**: If you are running this notebook using the Kubeflow Jupyter Server, you need to restart the Python **Kernel** because the packages above overwrited some default packages inside the Kubeflow Jupyter image."
+    "**Important**: If you are running this notebook using the Kubeflow Notebook Server, you need to restart the Python **Kernel** because the packages above overwrited some default packages inside the Kubeflow Jupyter image."
    ]
   },
   {
@@ -191,7 +191,7 @@
    "source": [
     "import kfp.dsl as dsl\n",
     "@dsl.pipeline(\n",
-    "   name='Calculation pipeline',\n",
+    "   name='calculation-pipeline',\n",
     "   description='A toy pipeline that performs arithmetic calculations.'\n",
     ")\n",
     "# Currently kfp-tekton doesn't support pass parameter to the pipelinerun yet, so we hard code the number here\n",