1. Setting Up!

1.1 Install KFServing

일단 KFServing은 Kubeflow와 함께 설치가 되기 때문에.. 사실.. 뭐 딱히 더 해줄 필요는 없지만..
늘 그렇듯.. Kubeflow의 버젼 업그레이드가 느리기 때문에.. 나처럼 최신 버젼 사용하고 싶어하는.. <생략>

Version 목록 을 참조해서 원하는 KFSErving 버젼을 설치 합니다.

$ wget https://raw.githubusercontent.com/kubeflow/kfserving/master/install/v0.4.1/kfserving.yaml
$ kubectl apply -f kfserving.yaml

1.2 Install Local Gateway

먼저 Knative > 0.11.2 이상이 설치가 미리 되어 있어야 합니다.
cluster-local-gateway 는 필수적으로 필요하며, 해당 gateway는 transformer 그리고 explainer사용시 반드시 필요합니다.
하여튼 그냥 설치 해두면 됩니다.
자세한 내용은 Installing Istio for Knative 를 참고 합니다.

아래 yaml

cat << EOF > ./local-cluster-gateway.yaml
apiVersion: install.istio.io/v1alpha1
kind: IstioOperator
spec:
  values:
    global:
      proxy:
        autoInject: enabled
      useMCP: false

  addonComponents:
    pilot:
      enabled: true
    prometheus:
      enabled: false

  components:
    ingressGateways:
      - name: cluster-local-gateway
        enabled: true
        label:
          istio: cluster-local-gateway
          app: cluster-local-gateway
        k8s:
          service:
            type: ClusterIP
            ports:
              - name: http
                protocol: TCP
                port: 80
                targetPort: 8080
              - name: https
                protocol: TCP
                port: 443
                targetPort: 8443
              - name: status-port
                port: 15021
                targetPort: 15021
              - name: tls
                port: 15443
                targetPort: 15443
EOF
$ istioctl manifest generate -f local-cluster-gateway.yaml > manifest.yaml
$ kubectl apply -f manifest.yaml
$ kubectl get svc cluster-local-gateway -n istio-system 
NAME                    TYPE        CLUSTER-IP       EXTERNAL-IP   PORT(S)                              AGE
cluster-local-gateway   ClusterIP   10.100.243.162   <none>        80/TCP,443/TCP,15021/TCP,15443/TCP   69s

1.3 Serving namespace 지정

Kubeflow에서는 이미 KFServing 이 설치되어서 나옵니다.

$ kubectl create namespace kfserving
$ kubectl label namespace kfserving istio-injection=enabled
$ kubectl label namespace kfserving serving.kubeflow.org/inferenceservice=enabled
$ kubectl get ns kfserving -o json | jq .metadata.labels
{
  "istio-injection": "enabled",
  "serving.kubeflow.org/inferenceservice": "enabled"
}

KFServing controller 가 설치되어 있는지 확인합니다.

# Kuberflow 설치시
$ kubectl get po -n kubeflow | grep kfserving-controller-manager
kfserving-controller-manager-0        2/2     Running   0          3h55m

# KFServing 단독 설치시
$ kubectl get po -n kfserving-system | grep kfserving-controller-manager
kfserving-controller-manager-0        2/2     Running   0          56s

2. Getting Started

2.1 Iris Tutorial

2.1.1 Model Deployment

cat <<EOF > sklearn.yaml 
apiVersion: "serving.kubeflow.org/v1alpha2"
kind: "InferenceService"
metadata:
  name: "sklearn-iris"
spec:
  default:
    predictor:
      sklearn:
        storageUri: "gs://kfserving-samples/models/sklearn/iris"
EOF

데이터 생성

cat <<EOF > iris-input.json
{
  "instances": [
    [5.0,  3.4,  1.5,  0.2],
    [6.7,  3.1,  4.4,  1.4],
    [6.1,  3.0,  4.9,  1.8]
  ]
}
EOF

배포

# sklearn InferenceService를 배포합니다. 
$ kubectl apply -f sklearn.yaml -n kfserving

# Service URL 을 확인합니다. (URL 뜨는데 까지 약 20~30초 걸림)
$ kubectl get inferenceservices sklearn-iris -n kfserving
NAME           URL                                                                READY   DEFAULT TRAFFIC
sklearn-iris   http://sklearn-iris.kfserving.example.com/v1/models/sklearn-iris   True    100            

AWS EKS는 ingress 를 외부 연결로 쓰지 않고 따로 LoadBalancer를 지정해줘야 합니다. ㅜㅜ 개불편.
(GCP는 ingress 설정하면 알아서 load balancer 잡힘)

$ kubectl get svc istio-ingressgateway -n istio-system
NAME                   TYPE       CLUSTER-IP       EXTERNAL-IP   PORT(S)        
istio-ingressgateway   NodePort   10.100.200.170   <none>        15020:30234/TCP <생략>

필요한건 NodePort를 LoadBalancer로 변경해주면 됩니다.

$ kubectl edit svc istio-ingressgateway  -n istio-system

type: NodePorttype: LoadBalancer로 변경해줍니다.
이후 다시 service를 확인해보면 EXTERNAL-IP가 잡혀있게 됩니다.

$ kubectl --namespace istio-system get service istio-ingressgateway
NAME                   TYPE           CLUSTER-IP       EXTERNAL-IP                        PORT(S)                    
istio-ingressgateway   LoadBalancer   10.100.200.170   ****.us-east-2.elb.amazonaws.com   80:32051/TCP,443:31101/TCP

2.1.2 Inference from External Source

  • INGRESS_HOST: Load Balancer Hostname (ex. a0e3184ae3-1490218815.us-east-2.elb.amazonaws.com)
  • INGRESS_PORT: Load Balancer Port (ex. 80)
  • SERVICE_HOSTNAME: 모델 서빙되고 있는 주소 (ex. sklearn-iris.kfserving.example.com)

환경변수 설정

$ INGRESS_HOST=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')
$ INGRESS_PORT=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name=="http2")].port}')
$ SERVICE_HOSTNAME=$(kubectl get inferenceservice sklearn-iris -n kfserving -o jsonpath='{.status.url}' | cut -d "/" -f 3)

Inference

$ curl -v -H  "Host: ${SERVICE_HOSTNAME}" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/sklearn-iris:predict -d @./iris-input.json
{"predictions": [0, 1, 2]}

PostMan

  • URL: {INGRESS_HOST}:{INGRESS_PORT}/v1/models/sklearn-iris
  • Headers 추가
    • Host: {SERVICE_HOSTNAME}
  • Body에 JSON형식으로 데이터 추가

2.1.3 Inference from Local Cluster Gateway

Cluster 내부에서의 통신은 위에처럼 외부 load balancer를 타서 통신을 할 필요가 없습니다.
즉 내부 통신을 이용하면 빠르게 데이터 교환을 할 수 있습니다.

먼저 내부에서 통신할 URL을 알아냅니다.

$ kubectl get inferenceService -n kfserving sklearn-iris -o jsonpath='{.status.address.url}' 
http://sklearn-iris.kfserving.svc.cluster.local/v1/models/sklearn-iris:predict

이제 특정 Container로 접속합니다.
아래는 예제 이며, “sklearn-iris-predictor-default-***” 요 부분은 pod 이름입니다.

$ kubectl exec -it sklearn-iris-predictor-default-*** -n kfserving  -c kfserving-container /bin/bash
$ curl -i http://sklearn-iris.kfserving.svc.cluster.local/v1/models/sklearn-iris:predict -d @./iris-input.json
{"predictions": [0, 1, 2]}

2.1.4 Performance Test

vi perf.yaml 로 다음을 입력합니다.
POST쪽에서 URL을 수정해줘야 합니다.

cat <<EOF > perf.yaml
apiVersion: batch/v1
kind: Job
metadata:
  generateName: load-test
spec:
  backoffLimit: 6
  parallelism: 1
  template:
    metadata:
      annotations:
        sidecar.istio.io/inject: "false"
    spec:
      restartPolicy: OnFailure
      containers:
      - args:
        - vegeta -cpus=5 attack -duration=1m -rate=500/1s -targets=/var/vegeta/cfg
          | vegeta report -type=text
        command:
        - sh 
        - -c
        image: peterevans/vegeta:latest
        imagePullPolicy: Always
        name: vegeta
        volumeMounts:
        - mountPath: /var/vegeta
          name: vegeta-cfg
      volumes:
      - configMap:
          defaultMode: 420
          name: vegeta-cfg
        name: vegeta-cfg
---
apiVersion: v1
data:
  cfg: |
    POST http://sklearn-iris.kfserving.ai.platform/v1/models/sklearn-iris:predict
    @/var/vegeta/payload
  payload: |
    {
      "instances": [
        [5.0,  3.4,  1.5,  0.2],
        [6.7,  3.1,  4.4,  1.4],
        [6.1,  3.0,  4.9,  1.8]
      ]
    }
kind: ConfigMap
metadata:
  annotations:
  name: vegeta-cfg
EOF
$ kubectl create -f perf.yaml -n kfserving 
job.batch/load-testpk9r2 created
configmap/vegeta-cfg created
$ kubectl logs load-testpk9r2-wmknb -n kfserving 
Requests      [total, rate, throughput]         30000, 500.02, 0.00
Duration      [total, attack, wait]             1m0s, 59.998s, 5.806ms
Latencies     [min, mean, 50, 90, 95, 99, max]  49.359µs, 3.64ms, 3.202ms, 5.691ms, 7.267ms, 12.156ms, 102.735ms
Bytes In      [total, mean]                     0, 0.00
Bytes Out     [total, mean]                     0, 0.00
Success       [ratio]                           0.00%
Status Codes  [code:count]                      0:30000  
Error Set:
Post "http://sklearn-iris.kfserving.ai.platform/v1/models/sklearn-iris:predict": dial tcp: lookup sklearn-iris.kfserving.ai.platform on 10.100.0.10:53: no such host

2.2 InferenceService with Custom Image

Flask App

cat <<EOF > app.py
import os
from flask import Flask

app = Flask(__name__)

@app.route('/v1/models/custom-image:predict')
def hello_world():
    greeting_target = os.environ.get('GREETING_TARGET', 'World')
    return 'Hello {}!\n'.format(greeting_target)

if __name__ == "__main__":
    app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('PORT', 8080)))
EOF

requirements.txt

cat <<EOF > requirements.txt
Flask==1.1.1
gunicorn==20.0.4
EOF

Dockerfile

cat <<EOF > Dockerfile
FROM python:3.7-slim

ENV APP_HOME=/app
WORKDIR \$APP_HOME
COPY app.py requirements.txt ./
RUN pip install --no-cache-dir -r ./requirements.txt

# Run the web service on container startup. Here we use the gunicorn
# webserver, with one worker process and 8 threads.
# For environments with multiple CPU cores, increase the number of workers
# to be equal to the cores available.
CMD exec gunicorn --bind :\$PORT --workers 1 --threads 8 app:app
EOF

Docker Build

  • 아래에 andersonjo 라고 되어 있는 부분은 Docker Hub의 ID를 넣어주시면 됩니다.
$ docker login
$ docker build -t andersonjo/custom-image .
$ docker run -d --name custom-test -p 8080:8080 -it test
$ docker push andersonjo/custom-image

Custom YAML

cat <<EOF > custom.yaml 
apiVersion: serving.kubeflow.org/v1alpha2
kind: InferenceService
metadata:
  labels:
    controller-tools.k8s.io: "1.0"
  name: custom-image
spec:
  default:
    predictor:
      custom:
        container:
          name: custom
          image: andersonjo/custom-image
          env:
            - name: GREETING_TARGET
              value: "Python KFServing Sample"
EOF

Deployment

$ kubectl apply -f custom.yaml -n kfserving
$ kubectl get inferenceservices -n kfserving
NAME           URL                                                                READY
custom-image   http://custom-image.kfserving.example.com/v1/models/custom-image   True 

Inference

$ INGRESS_HOST=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')
$ INGRESS_PORT=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name=="http2")].port}')
$ SERVICE_HOSTNAME=$(kubectl get inferenceservice custom-image -n kfserving -o jsonpath='{.status.url}' | cut -d "/" -f 3)
$ curl -H "Host: ${SERVICE_HOSTNAME}" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/custom-image:predict
Hello Python KFServing Sample!

2.3 Autoscale InferenceService

2.3.1 Create Inference Service

autoscale.yaml inference service 를 생성합니다.

cat <<EOF > autoscale.yaml
apiVersion: "serving.kubeflow.org/v1alpha2"
kind: "InferenceService"
metadata:
  name: "flowers-sample"
spec:
  default:
    predictor:
      tensorflow:
        storageUri: "gs://kfserving-samples/models/tensorflow/flowers" 
EOF
cat <<EOF > input.json
{  
    "instances":[  
       {  
          "image_bytes":{  
             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"
          },
          "key":"   1"
       }
    ]
}
EOF
$ kubectl apply -f autoscale.yaml -n kfserving
$ kubectl get inferenceservices flowers-sample -n kfserving
NAME             URL                                                                    READY   DEFAULT
flowers-sample   http://flowers-sample.kfserving.ai.platform/v1/models/flowers-sample   True    100    

2.3.2 Stress Test

hey 명령어를 통해서 스트레스 테스트를 가볍게 해볼수 있습니다.

  • -z: duration이고 10s는 10초, 3m 은 3분
  • -c: 동시 requests 갯수. concurrent requests 이며 전체 requests갯수는 아님
  • -m: HTTP method. POST, GET, PUT, DELETE, HEAD, OPTIONS ..
$ MODEL_NAME=flowers-sample
$ INGRESS_HOST=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')
$ INGRESS_PORT=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name=="http2")].port}')
$ SERVICE_HOSTNAME=$(kubectl get inferenceservice flowers-sample -n kfserving -o jsonpath='{.status.url}' | cut -d "/" -f 3)
$ hey -z 30s -c 5 -m POST -host ${HOST} -D $INPUT_PATH http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict

결과

Summary:
  Total:	30.1505 secs
  Slowest:	0.4009 secs
  Fastest:	0.1899 secs
  Average:	0.1944 secs
  Requests/sec:	25.6712
  
  Total data:	133128 bytes
  Size/request:	172 bytes

Response time histogram:
  0.190 [1]	|
  0.211 [768]	|■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■
  0.232 [0]	|
  0.253 [0]	|
  0.274 [0]	|
  0.295 [0]	|
  0.316 [0]	|
  0.338 [0]	|
  0.359 [0]	|
  0.380 [0]	|
  0.401 [5]	|


Latency distribution:
  10% in 0.1911 secs
  25% in 0.1916 secs
  50% in 0.1927 secs
  75% in 0.1944 secs
  90% in 0.1955 secs
  95% in 0.1963 secs
  99% in 0.2001 secs

Details (average, fastest, slowest):
  DNS+dialup:	0.0013 secs, 0.1899 secs, 0.4009 secs
  DNS-lookup:	0.0000 secs, 0.0000 secs, 0.0070 secs
  req write:	0.0000 secs, 0.0000 secs, 0.0002 secs
  resp wait:	0.1929 secs, 0.1897 secs, 0.2022 secs
  resp read:	0.0001 secs, 0.0000 secs, 0.0002 secs

Status code distribution:
  [404]	774 responses