Deploy Multiple Seldon Core Operators

This notebook illustrate how multiple Seldon Core Operators can share the same cluster. In particular:

• A Namespaced Operator that only manages Seldon Deployments inside its namespace. Only needs Role RBAC and Namespace labeled with seldon.io/controller-id

• A Clusterwide Operator that manges SeldonDeployment with a matching seldon.io/controller-id label.

• A Clusterwide Operator that manages Seldon Deployments not handled by the above.

Setup Seldon Core

Use the setup notebook to Setup Cluster with Ambassador Ingress.

Namespaced Seldon Core Operator

!kubectl create namespace seldon-ns1

namespace/seldon-ns1 created

!kubectl label namespace seldon-ns1 seldon.io/controller-id=seldon-ns1

namespace/seldon-ns1 labeled

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

'1.19.0-dev'

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()))

!helm install seldon-namespaced  ../helm-charts/seldon-core-operator  \
    --set singleNamespace=true \
    --set image.pullPolicy=IfNotPresent \
    --set usageMetrics.enabled=false \
    --set crd.create=false \
    --namespace seldon-ns1 \
    --wait

NAME: seldon-namespaced
LAST DEPLOYED: Thu Dec  4 10:08:54 2025
NAMESPACE: seldon-ns1
STATUS: deployed
REVISION: 1
TEST SUITE: None

!kubectl rollout status deployment/seldon-controller-manager -n seldon-ns1

deployment "seldon-controller-manager" successfully rolled out

%%writetemplate resources/model.yaml
apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: seldon-model
spec:
  name: test-deployment
  predictors:
  - componentSpecs:
    - spec:
        containers:
        - image: seldonio/mock_classifier:{VERSION}
          name: classifier
    graph:
      name: classifier
      type: MODEL
      endpoint:
        type: REST
    name: example
    replicas: 1

!kubectl create -f resources/model.yaml -n seldon-ns1

seldondeployment.machinelearning.seldon.io/seldon-model created

!kubectl wait sdep/seldon-model \
  --for=condition=ready \
  --timeout=120s \
  -n seldon-ns1

seldondeployment.machinelearning.seldon.io/seldon-model condition met

NAME = !kubectl get sdep -n seldon-ns1 -o jsonpath='{.items[0].metadata.name}'
assert NAME[0] == "seldon-model"

!kubectl config set-context $(kubectl config current-context) --namespace=seldon-ns1

Context "kind-kind" modified.

!kubectl delete -f resources/model.yaml -n seldon-ns1

seldondeployment.machinelearning.seldon.io "seldon-model" deleted

!helm delete seldon-namespaced

release "seldon-namespaced" uninstalled

Label Focused Seldon Core Operator

• We set crd.create=false as the CRD already exists in the cluster.

• We set controllerId=seldon-id1. SeldonDeployments with this label will be managed.

!kubectl create namespace seldon-id1

namespace/seldon-id1 created

!helm install seldon-controllerid  ../helm-charts/seldon-core-operator  \
    --set singleNamespace=false \
    --set image.pullPolicy=IfNotPresent \
    --set usageMetrics.enabled=false \
    --set crd.create=false \
    --set controllerId=seldon-id1 \
    --namespace seldon-id1 \
    --wait

NAME: seldon-controllerid
LAST DEPLOYED: Thu Dec  4 10:13:08 2025
NAMESPACE: seldon-id1
STATUS: deployed
REVISION: 1
TEST SUITE: None

!kubectl rollout status deployment/seldon-controller-manager -n seldon-id1

deployment "seldon-controller-manager" successfully rolled out

%%writetemplate resources/model_controller_id.yaml
apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  labels:
    app: seldon
    seldon.io/controller-id: seldon-id1
  name: test-c1
spec:
  name: test-c1
  predictors:
  - componentSpecs:
    - spec:
        containers:
        - image: seldonio/mock_classifier:{VERSION}
          imagePullPolicy: IfNotPresent
          name: classifier
          resources:
            requests:
              memory: 1Mi
        terminationGracePeriodSeconds: 1
    graph:
      children: []
      endpoint:
        type: REST
      name: classifier
      type: MODEL
    labels:
      version: v1
    name: example
    replicas: 1

!pygmentize resources/model_controller_id.yaml

apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  labels:
    app: seldon
    seldon.io/controller-id: seldon-id1
  name: test-c1
spec:
  name: test-c1
  predictors:
  - componentSpecs:
    - spec:
        containers:
        - image: seldonio/mock_classifier:1.19.0-dev
          imagePullPolicy: IfNotPresent
          name: classifier
          resources:
            requests:
              memory: 1Mi
        terminationGracePeriodSeconds: 1
    graph:
      children: []
      endpoint:
        type: REST
      name: classifier
      type: MODEL
    labels:
      version: v1
    name: example
    replicas: 1

!kubectl create -f resources/model_controller_id.yaml -n default

seldondeployment.machinelearning.seldon.io/test-c1 created

!kubectl wait sdep/test-c1 \
  --for=condition=ready \
  --timeout=120s \
  -n default

seldondeployment.machinelearning.seldon.io/test-c1 condition met

!kubectl get sdep -n default

NAME      AGE
test-c1   26s

NAME = !kubectl get sdep -n default -o jsonpath='{.items[0].metadata.name}'
assert NAME[0] == "test-c1"

!kubectl config set-context $(kubectl config current-context) --namespace=seldon-id1

Context "kind-kind" modified.

!kubectl delete -f resources/model_controller_id.yaml -n default

seldondeployment.machinelearning.seldon.io "test-c1" deleted

!helm delete seldon-controllerid

release "seldon-controllerid" uninstalled