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Predict on an InferenceService with transformer using Feast online feature store

Transformer is an InferenceService component which does pre/post processing alongside with model inference. In this example, instead of typical input transformation of raw data to tensors, we demonstrate a use case of online feature augmentation as part of preprocessing. We use a Feast Transformer to gather online features, run inference with a SKLearn predictor, and leave post processing as pass-through.

Setup

  1. Your ~/.kube/config should point to a cluster with KServe installed.
  2. Your cluster's Istio Ingress gateway must be network accessible.
  3. Your Feast online store is populated with driver data, instructions available here, and network accessible.

Build Transformer image

KServe.KFModel base class mainly defines three handlers preprocess, predict and postprocess, these handlers are executed in sequence, the output of the preprocess is passed to predict as the input, when predictor_host is passed the predict handler by default makes a HTTP call to the predictor url and gets back a response which then passes to postproces handler. KServe automatically fills in the predictor_host for Transformer and handle the call to the Predictor, for gRPC predictor currently you would need to overwrite the predict handler to make the gRPC call.

To implement a Transformer you can derive from the base KFModel class and then overwrite the preprocess and postprocess handler to have your own customized transformation logic.

Extend KFModel and implement pre/post processing functions

We created a class, DriverTransformer, which extends KFModel for this driver ranking example. It takes additional arguments for the transformer to interact with Feast: * feast_serving_url: The Feast serving URL, in the form of <host_name_or_ip:port> * entity_ids: The entity IDs for which to retrieve features from the Feast feature store * feature_refs: The feature references for the features to be retrieved

Please see the code example here

Build Transformer docker image

docker build -t {username}/driver-transformer:latest -f driver_transformer.Dockerfile .

docker push {username}/driver-transformer:latest

Create the InferenceService

Please use the YAML file and update the feast_serving_url argument to create the InferenceService, which includes a Feast Transformer and a SKLearn Predictor.

In the Feast Transformer image we packaged the driver transformer class so KServe knows to use the preprocess implementation to augment inputs with online features before making model inference requests. Then the InferenceService uses SKLearn to serve the driver ranking model, which is trained with Feast offline features, available in a gcs bucket specified under storageUri.

Apply the CRD

kubectl apply -f driver_transformer.yaml

Expected Output

$ inferenceservice.serving.kserve.io/driver-transformer created

Run a prediction

The first step is to determine the ingress IP and ports and set INGRESS_HOST and INGRESS_PORT

SERVICE_NAME=sklearn-driver-transformer
MODEL_NAME=sklearn-driver-transformer
INPUT_PATH=@./driver-input.json
SERVICE_HOSTNAME=$(kubectl get inferenceservice $SERVICE_NAME -o jsonpath='{.status.url}' | cut -d "/" -f 3)

curl -v -H "Host: ${SERVICE_HOSTNAME}" -d $INPUT_PATH http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict

Expected Output

> POST /v1/models/sklearn-driver-transformer:predict HTTP/1.1
> Host: sklearn-driver-transformer.default.example.com
> User-Agent: curl/7.58.0
> Accept: */*
> Content-Length: 57
> Content-Type: application/x-www-form-urlencoded
>
* upload completely sent off: 57 out of 57 bytes
< HTTP/1.1 200 OK
< content-length: 117
< content-type: application/json; charset=UTF-8
< date: Thu, 27 May 2021 00:34:21 GMT
< server: istio-envoy
< x-envoy-upstream-service-time: 47
<
* Connection #0 to host 1.2.3.4 left intact
{"predictions": [1.8440737040128852, 1.7381656744054226, 3.6771303027855993, 2.241143189554492, 0.06753551272342406]}