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Deploy Tensorflow Model with InferenceService

Create the HTTP InferenceService

Create an InferenceService yaml which specifies the framework tensorflow and storageUri that is pointed to a saved tensorflow model, and name it as tensorflow.yaml.

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "flower-sample"
spec:
  predictor:
    tensorflow:
      storageUri: "gs://kfserving-samples/models/tensorflow/flowers"

Apply the tensorflow.yaml to create the InferenceService, by default it exposes a HTTP/REST endpoint.

kubectl apply -f tensorflow.yaml 

Expected Output

$ inferenceservice.serving.kserve.io/flower-sample created

Wait for the InferenceService to be in ready state

kubectl get isvc flower-sample
NAME            URL                                        READY   PREV   LATEST   PREVROLLEDOUTREVISION        LATESTREADYREVISION                     AGE
flower-sample   http://flower-sample.default.example.com   True           100                                   flower-sample-predictor-default-n9zs6   7m15s

Run a prediction

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

MODEL_NAME=flower-sample
INPUT_PATH=@./input.json
SERVICE_HOSTNAME=$(kubectl get inferenceservice ${MODEL_NAME} -o jsonpath='{.status.url}' | cut -d "/" -f 3)
curl -v -H "Host: ${SERVICE_HOSTNAME}" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict -d $INPUT_PATH

Expected Output

* Connected to localhost (::1) port 8080 (#0)
> POST /v1/models/tensorflow-sample:predict HTTP/1.1
> Host: tensorflow-sample.default.example.com
> User-Agent: curl/7.73.0
> Accept: */*
> Content-Length: 16201
> Content-Type: application/x-www-form-urlencoded
> 
* upload completely sent off: 16201 out of 16201 bytes
* Mark bundle as not supporting multiuse
< HTTP/1.1 200 OK
< content-length: 222
< content-type: application/json
< date: Sun, 31 Jan 2021 01:01:50 GMT
< x-envoy-upstream-service-time: 280
< server: istio-envoy
< 
{
    "predictions": [
        {
            "scores": [0.999114931, 9.20987877e-05, 0.000136786213, 0.000337257545, 0.000300532585, 1.84813616e-05],
            "prediction": 0,
            "key": "   1"
        }
    ]
}

Canary Rollout

Canary rollout is a great way to control the risk of rolling out a new model by first moving a small percent of the traffic to it and then gradually increase the percentage. To run a canary rollout, you can apply the canary.yaml with the canaryTrafficPercent field specified.

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "flower-example"
spec:
  predictor:
    canaryTrafficPercent: 20
    tensorflow:
      storageUri: "gs://kfserving-samples/models/tensorflow/flowers-2"
kubectl apply -f canary.yaml 

To verify if the traffic split percentage is applied correctly, you can run the following command:

kubectl get isvc flower-sample
NAME            URL                                        READY   PREV   LATEST   PREVROLLEDOUTREVISION                   LATESTREADYREVISION                     AGE
flower-sample   http://flower-sample.default.example.com   True    80     20       flower-sample-predictor-default-n9zs6   flower-sample-predictor-default-2kwtr   7m15s

As you can see the traffic is split between the last rolled out revision and the current latest ready revision, KServe automatically tracks the last rolled out(stable) revision for you so you do not need to maintain both default and canary on the InferenceService as in v1alpha2.

Create the gRPC InferenceService

Create InferenceService which exposes the gRPC port and by default it listens on port 9000.

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "flower-grpc"
spec:
  predictor:
    tensorflow:
      storageUri: "gs://kfserving-samples/models/tensorflow/flowers"
      ports:
        - containerPort: 9000
          name: h2c
          protocol: TCP

Apply grpc.yaml to create the gRPC InferenceService.

kubectl apply -f grpc.yaml 

Expected Output

$ inferenceservice.serving.kserve.io/flower-grpc created

Run a prediction

We use a python gRPC client for the prediction, so you need to create a python virtual environment and install the tensorflow-serving-api.

# The prediction script is written in TensorFlow 1.x
pip install tensorflow-serving-api>=1.14.0,<2.0.0

Run prediction script

MODEL_NAME=flower-grpc
INPUT_PATH=./input.json
SERVICE_HOSTNAME=$(kubectl get inferenceservice ${MODEL_NAME} -o jsonpath='{.status.url}' | cut -d "/" -f 3)
python grpc_client.py --host $INGRESS_HOST --port $INGRESS_PORT --model $MODEL_NAME --hostname $SERVICE_HOSTNAME --input_path $INPUT_PATH

Expected Output

outputs {
  key: "key"
  value {
    dtype: DT_STRING
    tensor_shape {
      dim {
        size: 1
      }
    }
    string_val: "   1"
  }
}
outputs {
  key: "prediction"
  value {
    dtype: DT_INT64
    tensor_shape {
      dim {
        size: 1
      }
    }
    int64_val: 0
  }
}
outputs {
  key: "scores"
  value {
    dtype: DT_FLOAT
    tensor_shape {
      dim {
        size: 1
      }
      dim {
        size: 6
      }
    }
    float_val: 0.9991149306297302
    float_val: 9.209887502947822e-05
    float_val: 0.00013678647519554943
    float_val: 0.0003372581850271672
    float_val: 0.0003005331673193723
    float_val: 1.848137799242977e-05
  }
}
model_spec {
  name: "flowers-sample"
  version {
    value: 1
  }
  signature_name: "serving_default"
}