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04_querying_the_model.md

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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 front-end docker image

To build the front-end docker image, issue the following commands:

cd docker
docker build -t gcr.io/gcr-repository-name/issue-summarization-ui:0.1 .

Store the front-end docker image

To store the docker 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 front-end docker image to your Kubernetes cluster

The folder ks_app contains a ksonnet app. The ui component in ks_app contains the frontend deployment.

To avoid rate-limiting by the GitHub API, you will need an authentication token stored in the form of an environment variable ${GITHUB_TOKEN}. The token does not require any permissions and is only used to prevent anonymous API calls.

To use this token, set it as a parameter in the ui component:

cd ks_app
ks param set ui github_token ${GITHUB_TOKEN} --env ${KF_ENV}

To set the URL of your trained model, add it as a parameter:

ks param set ui modelUrl "http://issue-summarization.${NAMESPACE}.svc.cluster.local:8000/api/v0.1/predictions" --env ${KF_ENV}

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

ks apply ${KF_ENV} -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 svc/ambassador -n ${NAMESPACE} 8080:80

In a browser, navigate to http://localhost:8080/issue-summarization/, where you will be greeted by a basic website. Press the Populate Random Issue button, then click Generate Title to view a summary that was provided by your trained model.

Next: Teardown

Back: Serving the Model