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Collocate transformer and predictor in same pod

KServe by default deploys the Transformer and Predictor as separate services, allowing you to deploy them on different devices and scale them independently.
Nevertheless, there are certain situations where you might prefer to collocate the transformer and predictor within the same pod. Here are a few scenarios:

  1. If your transformer is tightly coupled with the predictor and you want to perform canary deployment together.
  2. If you want to reduce sidecar resources.
  3. If you want to reduce networking latency.

Before you begin

  1. Your ~/.kube/config should point to a cluster with KServe installed.
  2. Your cluster's Istio Ingress gateway must be network accessible.
  3. You can find the code samples on kserve repository.

Deploy the InferenceService

Since, the predictor and the transformer are in the same pod, they need to listen on different ports to avoid conflict. Transformer is configured to listen on port 8000 and 8081 while, Predictor listens on port 8080 and 8082. Transformer calls Predictor on port 8082 via local socket. Deploy the Inferenceservice using the below command. 

cat <<EOF | kubectl apply -f -
apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: custom-transformer-collocation
spec:
  predictor:
    containers:
      - name: kserve-container
        image: kserve/custom-model-grpc:latest
        args:
          - --model_name=custom-model
          - --grpc_port=8082
          - --http_port=8080

      - image: kserve/image-transformer:latest
        name: transformer-container    # Do not change the container name
        args:
          - --model_name=custom-model
          - --protocol=grpc-v2
          - --http_port=8000
          - --grpc_port=8081
          - --predictor_host=localhost:8082
        ports:
          - containerPort: 8000
            protocol: TCP
EOF

Expected output

$ inferenceservice.serving.kserve.io/custom-transformer-collocation created

Warning

Always use the transformer container name as transformer-container. Otherwise, the model volume is not mounted to the transformer container which may result in an error.

Note

Currently, The collocation support is limited to the custom container spec for kserve model container.

Check InferenceService status

kubectl get isvc custom-transformer-collocation

Expected output

NAME                             URL                                                         READY   PREV   LATEST   PREVROLLEDOUTREVISION   LATESTREADYREVISION                              AGE
custom-transformer-collocation   http://custom-transformer-collocation.default.example.com   True           100                              custom-transformer-collocation-predictor-00001   133m

Note

If your DNS contains svc.cluster.local, then Inferenceservice is not exposed through Ingress. you need to configure DNS or use a custom domain in order to expose the isvc.

Run a prediction

Prepare the inputs for the inference request. Copy the following Json into a file named input.json.

Now, determine the ingress IP and ports and set INGRESS_HOST and INGRESS_PORT

SERVICE_NAME=custom-transformer-collocation
MODEL_NAME=custom-model
INPUT_PATH=@./input.json
SERVICE_HOSTNAME=$(kubectl get inferenceservice $SERVICE_NAME -o jsonpath='{.status.url}' | cut -d "/" -f 3)
You can use curl to send the inference request as: 
curl -v -H "Host: ${SERVICE_HOSTNAME}" -H "Content-Type: application/json" -d $INPUT_PATH http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/$MODEL_NAME/infer

Expected output

*   Trying 127.0.0.1:8080...
* Connected to localhost (127.0.0.1) port 8080 (#0)
> POST /v2/models/custom-model/infer HTTP/1.1
> Host: custom-transformer-collocation.default.example.com
> User-Agent: curl/7.85.0
> Accept: */*
> Content-Type: application/json
> Content-Length: 105396
> 
* We are completely uploaded and fine
* Mark bundle as not supporting multiuse
< HTTP/1.1 200 OK
< content-length: 298
< content-type: application/json
< date: Thu, 04 May 2023 10:35:30 GMT
< server: istio-envoy
< x-envoy-upstream-service-time: 1273
< 
* Connection #0 to host localhost left intact
{"model_name":"custom-model","model_version":null,"id":"d685805f-a310-4690-9c71-a2dc38085d6f","parameters":null,"outputs":[{"name":"output-0","shape":[1,5],"datatype":"FP32","parameters":null,"data":[14.975618362426758,14.036808967590332,13.966032028198242,12.252279281616211,12.086268424987793]}]}
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