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Example Seldon Core Deployments using Helm with Istio

Prequisites

• Install istio

Setup Cluster and Ingress

Use the setup notebook to Setup Cluster with Istio Ingress. Instructions also online.

!kubectl create namespace seldon

namespace/seldon created

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

Context "kind-kind" modified.

Configure Istio

For this example we will create the default istio gateway for seldon which needs to be called seldon-gateway. You can supply your own gateway by adding to your SeldonDeployments resources the annotation seldon.io/istio-gateway with values the name of your istio gateway.

Create a gateway for our istio-ingress

%%writefile resources/seldon-gateway.yaml
apiVersion: networking.istio.io/v1alpha3
kind: Gateway
metadata:
  name: seldon-gateway
  namespace: istio-system
spec:
  selector:
    istio: ingressgateway # use istio default controller
  servers:
  - port:
      number: 80
      name: http
      protocol: HTTP
    hosts:
    - "*"

Overwriting resources/seldon-gateway.yaml

!kubectl create -f resources/seldon-gateway.yaml -n istio-system

gateway.networking.istio.io/seldon-gateway created

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

'1.9.0-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()))

Start Seldon Core

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

Serve Single Model

!helm install mymodel ../helm-charts/seldon-single-model --set model.image=seldonio/mock_classifier:$VERSION

NAME: mymodel
LAST DEPLOYED: Wed Mar 10 16:37:01 2021
NAMESPACE: seldon
STATUS: deployed
REVISION: 1
TEST SUITE: None

!helm template mymodel ../helm-charts/seldon-single-model --set model.image=seldonio/mock_classifier:$VERSION | pygmentize -l json

---
# Source: seldon-single-model/templates/seldondeployment.json
{
  "kind": "SeldonDeployment",
  "apiVersion": "machinelearning.seldon.io/v1",
  "metadata": {
    "name": "mymodel",
    "namespace": "seldon",
    "labels": {}
  },
  "spec": {
      "name": "mymodel",
      "protocol": "seldon",
    "annotations": {},
    "predictors": [
      {
        "name": "default",
        "graph": {
          "name": "model",
          "type": "MODEL",
        },
        "componentSpecs": [
          {
            "spec": {
              "containers": [
                {
                  "name": "model",
                  "image": "seldonio/mock_classifier:1.7.0-dev",
                  "env": [
                      {
                        "name": "LOG_LEVEL",
                        "value": "INFO"
                      },
                    ],
                  "resources": {"requests":{"memory":"1Mi"}},
                }
              ]
            },
          }
        ],
        "replicas": 1
      }
    ]
  }
}

!kubectl rollout status deploy/mymodel-default-0-model

Waiting for deployment "mymodel-default-0-model" rollout to finish: 0 of 1 updated replicas are available...
deployment "mymodel-default-0-model" successfully rolled out

Get predictions

from seldon_core.seldon_client import SeldonClient

sc = SeldonClient(
    deployment_name="mymodel", 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: 0.721679221744617
  }
}

Response:
{'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.1002015221659356]}}, 'meta': {'requestPath': {'model': 'seldonio/mock_classifier:1.7.0-dev'}}}

gRPC Request

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

Success:True message:
Request:
{'meta': {}, 'data': {'tensor': {'shape': [1, 1], 'values': [0.17825624441824628]}}}
Response:
{'meta': {'requestPath': {'model': 'seldonio/mock_classifier:1.7.0-dev'}}, 'data': {'names': ['proba'], 'tensor': {'shape': [1, 1], 'values': [0.06074453279395597]}}}

!helm delete mymodel

release "mymodel" uninstalled

Host Restriction

In this example we will restriction request to those with the Host header “seldon.io”

%%writetemplate resources/model_seldon.yaml
apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: example-seldon
  annotations:
    "seldon.io/istio-host": "seldon.io"
spec:
  protocol: seldon
  predictors:
  - componentSpecs:
    - spec:
        containers:
        - image: seldonio/mock_classifier:{VERSION}
          name: classifier
    graph:
      name: classifier
      type: MODEL
    name: model
    replicas: 1

!kubectl apply -f resources/model_seldon.yaml

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

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

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

for i in range(60):
    state = !kubectl get sdep example-seldon -o jsonpath='{.status.state}'
    state = state[0]
    print(state)
    if state == "Available":
        break
    time.sleep(1)
assert state == "Available"

Available

X=!curl -s -d '{"data": {"ndarray":[[1.0, 2.0, 5.0]]}}' \
   -X POST http://localhost:8003/seldon/seldon/example-seldon/api/v1.0/predictions \
   -H "Content-Type: application/json" \
assert X == []

import json
X=!curl -s -d '{"data": {"ndarray":[[1.0, 2.0, 5.0]]}}' \
   -X POST http://localhost:8003/seldon/seldon/example-seldon/api/v1.0/predictions \
   -H "Content-Type: application/json" \
   -H "Host: seldon.io"
d=json.loads(X[0])
print(d)
assert(d["data"]["ndarray"][0][0] > 0.4)

{'data': {'names': ['proba'], 'ndarray': [[0.43782349911420193]]}, 'meta': {'requestPath': {'classifier': 'seldonio/mock_classifier:1.9.0-dev'}}}

!kubectl delete -f resources/model_seldon.yaml

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