querying_the_model.md

Querying the model

In this section, you will setup a barebones web server that displays the prediction provided by the previously deployed model.

The following steps describe how to build a docker image and deploy it locally, where it accepts as input any arbitrary text and displays a machine-generated summary.

Prerequisites

Ensure that your model is live and listening for HTTP requests as described in serving.

Build the frontend image

To build the frontend image, issue the following commands:

cd docker
docker build -t gcr.io/gcr-repository-name/issue-summarization-ui .

Store the frontend image

To store the image in a location accessible to GKE, push it to the container registry of your choice. Here, it is pushed to Google Container Registry.

gcloud docker -- push gcr.io/gcr-repository-name/issue-summarization-ui:0.1

Deploy the frontend image to your kubernetes cluster

notebooks contains a ksonnet app(ks-app). The ui component in the ks-app contains the frontend image deployment.

Create an environment to deploy the ksonnet app

cd notebooks/ks-app
ks env add frontendenv --namespace ${NAMESPACE}

To serve the frontend interface, apply the ui component of the ksonnet app:

ks apply frontendenv -c ui

View results from the frontend

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 service=ambassador -o jsonpath='{.items[0].metadata.name}') -n ${NAMESPACE} 8080:80

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