Use ambassador to talk to the frontend ui (#71)

* Create a ksonnet app component to deploy to k8s

github_issue_summarization/notebooks/Dockerfile

@@ -1,3 +1,3 @@
 FROM gcr.io/kubeflow-images-staging/tensorflow-1.6.0-notebook-cpu
-COPY tf-job/train.py /workdir/train.py
+COPY train.py /workdir/train.py
 COPY seq2seq_utils.py /workdir/seq2seq_utils.py

github_issue_summarization/notebooks/ks-app/components/params.libsonnet

@@ -8,6 +8,9 @@
     // Each object below should correspond to a component in the components/ directory
     tfjob: {
 
+    },
+    ui: {
+
     },
   },
 }

github_issue_summarization/notebooks/ks-app/components/ui.jsonnet

@@ -0,0 +1,7 @@
+local env = std.extVar("__ksonnet/environments");
+local params = std.extVar("__ksonnet/params").components.ui;
+local k = import "k.libsonnet";
+
+local ui = import "ui.libsonnet";
+
+std.prune(k.core.v1.list.new(ui.parts(params, env)))

github_issue_summarization/notebooks/ks-app/components/ui.libsonnet

@@ -0,0 +1,58 @@
+{
+  parts(params, env):: [
+    {
+      apiVersion: "v1",
+      kind: "Service",
+      metadata: {
+        name: "issue-summarization-ui",
+        namespace: env.namespace,
+        annotations: {
+          "getambassador.io/config": "---\napiVersion: ambassador/v0\nkind:  Mapping\nname:  issue_summarization_ui\nprefix: /issue-summarization/\nrewrite: /\nservice: issue-summarization-ui:80\n",
+        },
+      },
+      spec: {
+        ports: [
+          {
+            port: 80,
+            targetPort: 80,
+          },
+        ],
+        selector: {
+          app: "issue-summarization-ui",
+        },
+        type: "ClusterIP",
+      },
+    },
+    {
+      apiVersion: "apps/v1beta1",
+      kind: "Deployment",
+      metadata: {
+        name: "issue-summarization-ui",
+        namespace: env.namespace,
+      },
+      spec: {
+        replicas: 1,
+        template: {
+          metadata: {
+            labels: {
+              app: "issue-summarization-ui",
+            },
+          },
+          spec: {
+            containers: [
+              {
+                image: "gcr.io/kubeflow-images-staging/issue-summarization-ui:latest",
+                name: "issue-summarization-ui",
+                ports: [
+                  {
+                    containerPort: 80,
+                  },
+                ],
+              },
+            ],
+          },
+        },
+      },
+    },
+  ],
+}

github_issue_summarization/querying_the_model.md

@@ -34,34 +34,32 @@ gcloud docker -- push gcr.io/gcr-repository-name/issue-summarization-ui:0.1
 
 ## Deploy the frontend image to your kubernetes cluster
 
-To serve the frontend interface, run the image using the following command:
+[notebooks](notebooks) contains a ksonnet app([ks-app](notebooks/ks-app)). The ui component in the ks-app contains the frontend image deployment.
 
-```
-ks generate deployed-service issue-summarization-ui \
-  --image gcr.io/gcr-repository-name/issue-summarization-ui:0.1 \
-  --type ClusterIP
-ks param set issue-summarization-ui namespace $NAMESPACE
-ks apply cloud -n $NAMESPACE -c issue-summarization-ui
+Create an environment to deploy the ksonnet app
+
+```commandline
+cd notebooks/ks-app
+ks env add frontendenv --namespace ${NAMESPACE}
 ```
 
-TODO: Figure out why deployed-service prototype does not pick up the
-namespace parameter. The workaround is to generate the yaml for
-issue-summarization-ui service and deployment objects, inserting
-the namespace parameter, and applying manually to the cluster.
+To serve the frontend interface, apply the ui component of the ksonnet app:
 
+```
+ks apply frontendenv -c ui
+```
 
 ## View results from the frontend
 
-To setup a proxy to the UI port running in k8s, issue the following command:
+We use ambassador to route requests to the frontend. You can port-forward the ambassador container locally:
 
 ```
-kubectl port-forward $(kubectl get pods -n ${NAMESPACE} -l app=issue-summarization-ui -o jsonpath='{.items[0].metadata.name}') -n ${NAMESPACE} 8081:80
+kubectl port-forward $(kubectl get pods -n ${NAMESPACE} -l service=ambassador -o jsonpath='{.items[0].metadata.name}') -n ${NAMESPACE} 8080:80
 ```
 
-In a browser, navigate to `http://localhost:8081`, where you will be greeted by "Issue
+In a browser, navigate to `http://localhost:8080/issue-summarization/`, where you will be greeted by "Issue
 text" Enter text into the input box and click submit. You should see a
 summary that was provided by your trained model.
 
 
 Next: [Teardown](teardown.md)
-

github_issue_summarization/training_the_model_tfjob.md

@@ -6,7 +6,7 @@ back to GCS.
 
 ## Create the image for training
 
-The [tf-job](notebooks/tf-job) directory contains the necessary files to create a image for training. The [train.py](notebooks/tf-job/train.py) file contains the training code. Here is how you can create an image and push it to gcr.
+The [notebooks](notebooks) directory contains the necessary files to create a image for training. The [train.py](notebooks/train.py) file contains the training code. Here is how you can create an image and push it to gcr.
 
 ```commandline
 cd notebooks/
@@ -41,12 +41,12 @@ kubectl --namespace=${NAMESPACE} create secret generic gcp-credentials --from-fi
 
 ## Run the TFJob using your image
 
-[tf-job](notebooks/tf-job) contains a ksonnet app([ks-app](notebooks/tf-job/ks-app)) to deploy the TFJob.
+[notebooks](notebooks) contains a ksonnet app([ks-app](notebooks/ks-app)) to deploy the TFJob.
 
 Create an environment to deploy the ksonnet app
 
 ```commandline
-cd notebooks/tf-job/ks-app
+cd notebooks/ks-app
 ks env add tfjob --namespace ${NAMESPACE}
 ```