This page was generated from examples/istio/canary/istio_canary.ipynb.

Canary Rollout with Seldon and Istio

Setup Seldon Core

Use the setup notebook to Setup Cluster with Istio Ingress and Install Seldon Core. Instructions also online.

!kubectl create namespace seldon

namespace/seldon created

!kubectl config set-context $(kubectl config current-context) --namespace=seldon

Context "kind-kind" modified.

from IPython.core.magic import register_line_cell_magic


@register_line_cell_magic
def writetemplate(line, cell):
    with open(line, "w") as f:
        f.write(cell.format(**globals()))

Ensure the istio ingress gatewaty is port-forwarded to localhost:8004

• Istio: kubectl port-forward $(kubectl get pods -l istio=ingressgateway -n istio-system -o jsonpath='{.items[0].metadata.name}') -n istio-system 8004:8080

ISTIO_GATEWAY = "localhost:8004"

VERSION = !cat ../../../version.txt
VERSION = VERSION[0]
VERSION

'1.7.0-dev'

Launch main model

We will create a very simple Seldon Deployment with a dummy model image seldonio/mock_classifier:1.0. This deployment is named example.

%%writetemplate model.yaml
apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  labels:
    app: seldon
  name: example
spec:
  name: canary-example
  predictors:
  - componentSpecs:
    - spec:
        containers:
        - image: seldonio/mock_classifier:{VERSION}
          imagePullPolicy: IfNotPresent
          name: classifier
        terminationGracePeriodSeconds: 1
    graph:
      children: []
      endpoint:
        type: REST
      name: classifier
      type: MODEL
    name: main
    replicas: 1

!kubectl create -f model.yaml

seldondeployment.machinelearning.seldon.io/example created

!kubectl rollout status deploy/$(kubectl get deploy -l seldon-deployment-id=example -o jsonpath='{.items[0].metadata.name}')

Waiting for deployment "example-main-0-classifier" rollout to finish: 0 of 1 updated replicas are available...
deployment "example-main-0-classifier" successfully rolled out

Get predictions

from seldon_core.seldon_client import SeldonClient

sc = SeldonClient(
    deployment_name="example", namespace="seldon", gateway_endpoint=ISTIO_GATEWAY
)

REST Request

r = sc.predict(gateway="istio", transport="rest")
assert r.success == True
print(r)

Success:True message:
Request:
meta {
}
data {
  tensor {
    shape: 1
    shape: 1
    values: 0.6670563912281003
  }
}

Response:
{'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.09538308704053941]}}, 'meta': {'requestPath': {'classifier': 'seldonio/mock_classifier:1.7.0-dev'}}}

Launch Canary

We will now extend the existing graph and add a new predictor as a canary using a new model seldonio/mock_classifier_rest:1.1. We will add traffic values to split traffic 75/25 to the main and canary.

%%writetemplate canary.yaml
apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  labels:
    app: seldon
  name: example
spec:
  name: canary-example
  predictors:
  - componentSpecs:
    - spec:
        containers:
        - image: seldonio/mock_classifier:{VERSION}
          imagePullPolicy: IfNotPresent
          name: classifier
        terminationGracePeriodSeconds: 1
    graph:
      children: []
      endpoint:
        type: REST
      name: classifier
      type: MODEL
    name: main
    replicas: 1
    traffic: 75
  - componentSpecs:
    - spec:
        containers:
        - image: seldonio/mock_classifier:{VERSION}
          imagePullPolicy: IfNotPresent
          name: classifier
        terminationGracePeriodSeconds: 1
    graph:
      children: []
      endpoint:
        type: REST
      name: classifier
      type: MODEL
    name: canary
    replicas: 1
    traffic: 25

!kubectl apply -f canary.yaml

Warning: kubectl apply should be used on resource created by either kubectl create --save-config or kubectl apply
seldondeployment.machinelearning.seldon.io/example configured

!kubectl rollout status deploy/$(kubectl get deploy -l seldon-deployment-id=example -o jsonpath='{.items[0].metadata.name}')
!kubectl rollout status deploy/$(kubectl get deploy -l seldon-deployment-id=example -o jsonpath='{.items[1].metadata.name}')

Waiting for deployment "example-canary-0-classifier" rollout to finish: 0 of 1 updated replicas are available...
deployment "example-canary-0-classifier" successfully rolled out
deployment "example-main-0-classifier" successfully rolled out

Show our REST requests are now split with roughly 25% going to the canary.

sc.predict(gateway="istio", transport="rest")

Success:True message:
Request:
meta {
}
data {
  tensor {
    shape: 1
    shape: 1
    values: 0.5754642896429739
  }
}

Response:
{'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.08776759944872958]}}, 'meta': {'requestPath': {'classifier': 'seldonio/mock_classifier:1.7.0-dev'}}}

from collections import defaultdict

counts = defaultdict(int)
n = 100
for i in range(n):
    r = sc.predict(gateway="istio", transport="rest")

Following checks number of prediction requests processed by default/canary predictors respectively.

default_count = !kubectl logs $(kubectl get pod -lseldon-app=example-main -o jsonpath='{.items[0].metadata.name}') classifier | grep "root:predict" | wc -l

canary_count = !kubectl logs $(kubectl get pod -lseldon-app=example-canary -o jsonpath='{.items[0].metadata.name}') classifier | grep "root:predict" | wc -l

canary_percentage = float(canary_count[0]) / float(default_count[0])
print(canary_percentage)
assert canary_percentage > 0.1 and canary_percentage < 0.5

0.275

!kubectl delete -f canary.yaml

seldondeployment.machinelearning.seldon.io "example" deleted