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 amachine-generated summary.
Ensure that your model is live and listening for HTTP requests as described in serving.
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 .
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
The folder ks-kubeflow contains a ksonnet app. The ui component
in the ks-kubeflow app contains the frontend image 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-kubeflow
ks param set ui github_token ${GITHUB_TOKEN} --env ${KF_ENV}
To serve the frontend interface, apply the ui component of the ksonnet app:
ks apply ${KF_ENV} -c ui
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 "Issuetext". Enter text into the input box and click
"Submit". You should see a summary that was provided by your trained model.
Next: Teardown
Back: Serving the Model