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Version: 0.17

Deploy InferenceService with a saved model on Google Cloud Storage (GCS)

Using Public GCS Bucket​

If no credential is provided, anonymous client will be used to download the artifact from GCS bucket. The uri is in the following format:

gs://${BUCKET_NAME}/${PATH}

e.g. gs://kfserving-examples/models/tensorflow/flowers

Using Private GCS bucket​

KServe supports authenticating using Google Service Account Key

Create a Google Service Account Key​

Create Secret and attach to Service Account​

Create secret​

You can create a secret using either the imperative kubectl command or by declaratively defining it in a YAML file.

Using Imperative Command​

kubectl create secret generic gcscreds --from-file=gcloud-application-credentials.json=/path/to/gcloud-application-credentials.json

Using Declarative YAML​

apiVersion: v1
kind: Secret
metadata:
  name: gcscreds
type: Opaque
data:
  gcloud-application-credentials.json: <base64 encoded value of the credential file>

Note that the key gcloud-application-credentials.json is configurable. If you change its value in the configuration, ensure you use the same name when creating your GCS credentials secret.

kubectl apply -f gcs-secret.yaml

Attach secret to a service account​

apiVersion: v1
kind: ServiceAccount
metadata:
  name: sa
secrets:
- name: gcscreds

Deploy the model on GCS with InferenceService​

Create the InferenceService with the Google service account credential

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: tensorflow-gcs
spec:
  predictor:
    serviceAccountName: sa
    model:
      modelFormat:
        name: tensorflow
      storageUri: "gs://kfserving-examples/models/tensorflow/flowers"

Apply the tensorflow-gcs.yaml.

kubectl apply -f tensorflow-gcs.yaml

Run a prediction​

Now, the ingress can be accessed at ${INGRESS_HOST}:${INGRESS_PORT} or follow this instruction to find out the ingress IP and port.

Now, you can run a prediction using the sample input:

SERVICE_HOSTNAME=$(kubectl get inferenceservice tensorflow-gcs -o jsonpath='{.status.url}' | cut -d "/" -f 3)

MODEL_NAME=tensorflow-gcs
INPUT_PATH=@./input.json
curl -v -H "Host: ${SERVICE_HOSTNAME}" -H "Content-Type: application/json" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict -d $INPUT_PATH
Expected Output
*   Trying 127.0.0.1:8080...
* TCP_NODELAY set
* Connected to localhost (127.0.0.1) port 8080 (#0)
> POST /v1/models/tensorflow-gcs:predict HTTP/1.1
> Host: tensorflow-gcs.default.example.com
> User-Agent: curl/7.68.0
> Accept: */*
> Content-Length: 84
> Content-Type: application/x-www-form-urlencoded
>
* upload completely sent off: 84 out of 84 bytes
* Mark bundle as not supporting multiuse
< HTTP/1.1 200 OK
< content-length: 23
< content-type: application/json; charset=UTF-8
< date: Mon, 20 Sep 2021 04:55:50 GMT
< server: istio-envoy
< x-envoy-upstream-service-time: 6
<
* Connection #0 to host localhost left intact
{"predictions": [1, 1]}