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Canary Rollout Example

Setup

  1. Your ~/.kube/config should point to a cluster with KServe installed.
  2. Your cluster's Istio Ingress gateway must be network accessible.

Create the InferenceService

Complete steps 1-3 in the First Inference Service tutorial. Set up a namespace (if not already created), and create an InferenceService.

After rolling out the first model, 100% traffic goes to the initial model with service revision 1.

Run kubectl get isvc sklearn-iris in the command line to see the amount of traffic routing to the InferenceService under the LATEST column.

NAME       URL                                   READY   PREV   LATEST   PREVROLLEDOUTREVISION   LATESTREADYREVISION                AGE
sklearn-iris   http://sklearn-iris.kserve-test.example.com   True           100                              sklearn-iris-predictor-default-00001   46s                               2m39s                             70s

Update the InferenceService with the canary rollout strategy

Add the canaryTrafficPercent field to the predictor component and update the storageUri to use a new/updated model.

NOTE: A new predictor schema was introduced in v0.8.0. New InferenceServices should be deployed using the new schema. The old schema is provided as reference.

kubectl apply -n kserve-test -f - <<EOF
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "sklearn-iris"
spec:
  predictor:
    canaryTrafficPercent: 10
    model:
      modelFormat:
        name: sklearn
      storageUri: "gs://kfserving-examples/models/sklearn/1.0/model-2"
EOF

kubectl apply -n kserve-test -f - <<EOF
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "sklearn-iris"
spec:
  predictor:
    canaryTrafficPercent: 10
    sklearn:
      storageUri: "gs://kfserving-examples/models/sklearn/1.0/model-2"
EOF

After rolling out the canary model, traffic is split between the latest ready revision 2 and the previously rolled out revision 1.

kubectl get isvc sklearn-iris

NAME       URL                                   READY   PREV   LATEST   PREVROLLEDOUTREVISION              LATESTREADYREVISION                AGE
sklearn-iris   http://sklearn-iris.kserve-test.example.com   True    90     10       sklearn-iris-predictor-default-00001   sklearn-iris-predictor-default-00002   9m19s

Check the running pods, you should now see port two pods running for the old and new model and 10% traffic is routed to the new model. Notice revision 1 contains default-0001 in its name, while revision 2 contains default-0002.

kubectl get pods 

NAME                                                              READY   STATUS      RESTARTS   AGE
sklearn-iris-predictor-default-00001-deployment-66c5f5b8d5-gmfvj   2/2     Running     0          11m
sklearn-iris-predictor-default-00002-deployment-5bd9ff46f8-shtzd   2/2     Running     0          12m

Run a prediction

Follow the next two steps (Determine the ingress IP and ports and Perform inference) in the First Inference Service tutorial.

Send more requests to the InferenceService to observe the 10% of traffic that routes to the new revision.

Promote the canary model

If the canary model is healthy/passes your tests, you can promote it by removing the canaryTrafficPercent field and re-applying the InferenceService custom resource.

kubectl apply -n kserve-test -f - <<EOF
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "sklearn-iris"
spec:
  predictor:
    model:
      modelFormat:
        name: sklearn
      storageUri: "gs://kfserving-examples/models/sklearn/1.0/model-2"
EOF

Now all traffic goes to the revision 2 for the new model.

kubectl get isvc sklearn-iris
NAME       URL                                   READY   PREV   LATEST   PREVROLLEDOUTREVISION   LATESTREADYREVISION                AGE
sklearn-iris   http://sklearn-iris.kserve-test.example.com   True           100                              sklearn-iris-predictor-default-00002   17m

The pods for revision generation 1 automatically scales down to 0 as it is no longer getting the traffic.

kubectl get pods -l serving.kserve.io/inferenceservice=sklearn-iris
NAME                                                           READY   STATUS        RESTARTS   AGE
sklearn-iris-predictor-default-00001-deployment-66c5f5b8d5-gmfvj   1/2     Terminating   0          17m
sklearn-iris-predictor-default-00002-deployment-5bd9ff46f8-shtzd   2/2     Running       0          15m

Rollback and pin the previous model

You can pin the previous model (model v1, for example) by setting the canaryTrafficPercent to 0 for the current model (model v2, for example). This rolls back from model v2 to model v1 and decreases model v2's traffic to zero.

Apply the custom resource to set model v2's traffic to 0%.

kubectl apply -n kserve-test -f - <<EOF
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "sklearn-iris"
spec:
  predictor:
    canaryTrafficPercent: 0
    model:
      modelFormat:
        name: sklearn
      storageUri: "gs://kfserving-examples/models/sklearn/1.0/model-2"
EOF

Check the traffic split, now 100% traffic goes to the previous good model (model v1) for revision generation 1.

kubectl get isvc sklearn-iris
NAME       URL                                   READY   PREV   LATEST   PREVROLLEDOUTREVISION              LATESTREADYREVISION                AGE
sklearn-iris   http://sklearn-iris.kserve-test.example.com   True    100    0        sklearn-iris-predictor-default-00001   sklearn-iris-predictor-default-00002   18m

The pods for previous revision (model v1) now routes 100% of the traffic to its pods while the new model (model v2) routes 0% traffic to its pods.

kubectl get pods -l serving.kserve.io/inferenceservice=sklearn-iris

NAME                                                           READY   STATUS            RESTARTS   AGE
sklearn-iris-predictor-default-00001-deployment-66c5f5b8d5-gmfvj   1/2     Running       0          35s
sklearn-iris-predictor-default-00002-deployment-5bd9ff46f8-shtzd   2/2     Running       0          16m

Route traffic using a tag

You can enable tag based routing by adding the annotation serving.kserve.io/enable-tag-routing, so traffic can be explicitly routed to the canary model (model v2) or the old model (model v1) via a tag in the request URL.

Apply model v2 with canaryTrafficPercent: 10 and serving.kserve.io/enable-tag-routing: "true".

kubectl apply -n kserve-test -f - <<EOF
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "sklearn-iris"
  annotations:
    serving.kserve.io/enable-tag-routing: "true"
spec:
  predictor:
    canaryTrafficPercent: 10
    model:
      modelFormat:
        name: sklearn
      storageUri: "gs://kfserving-examples/models/sklearn/1.0/model-2"
EOF

Check the InferenceService status to get the canary and previous model URL.

kubectl get isvc sklearn-iris -ojsonpath="{.status.components.predictor}"  | jq

The output should look like

{
  "address": {
    "url": "http://sklearn-iris-predictor-default.kserve-test.svc.cluster.local"
  },
  "latestCreatedRevision": "sklearn-iris-predictor-default-00003",
  "latestReadyRevision": "sklearn-iris-predictor-default-00003",
  "latestRolledoutRevision": "sklearn-iris-predictor-default-00001",
  "previousRolledoutRevision": "sklearn-iris-predictor-default-00001",
  "traffic": [
    {
      "latestRevision": true,
      "percent": 10,
      "revisionName": "sklearn-iris-predictor-default-00003",
      "tag": "latest",
      "url": "http://latest-sklearn-iris-predictor-default.kserve-test.example.com"
    },
    {
      "latestRevision": false,
      "percent": 90,
      "revisionName": "sklearn-iris-predictor-default-00001",
      "tag": "prev",
      "url": "http://prev-sklearn-iris-predictor-default.kserve-test.example.com"
    }
  ],
  "url": "http://sklearn-iris-predictor-default.kserve-test.example.com"
}

Since we updated the annotation on the InferenceService, model v2 now corresponds to sklearn-iris-predictor-default-00003.

You can now send the request explicitly to the new model or the previous model by using the tag in the request URL. Use the curl command from Perform inference and add latest- or prev- to the model name to send a tag based request.

For example, set the model name and use the following commands to send traffic to each service based on the latest or prev tag.

MODEL_NAME=sklearn-iris

curl the latest revision

curl -v -H "Host: latest-${MODEL_NAME}-predictor-default.kserve-test.example.com" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict -d @./iris-input.json

or curl the previous revision

curl -v -H "Host: prev-${MODEL_NAME}-predictor-default.kserve-test.example.com" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict -d @./iris-input.json
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