SeldonIO | sklearn_iris and sklearn_spacy_text | not working in k8s - kubernetes

Question

Ref:
https://github.com/SeldonIO/seldon-core/blob/master/examples/models/sklearn_iris/sklearn_iris.ipynb
https://github.com/SeldonIO/seldon-core/tree/master/examples/models/sklearn_spacy_text
#Steps Done
1. kubectl port-forward $(kubectl get pods -l istio=ingressgateway -n istio-system -o jsonpath='{.items[0].metadata.name}') -n istio-system 8003:80
2.kubectl create namespace john
3.kubectl config set-context $(kubectl config current-context) --namespace=john
4.kubectl create -f sklearn_iris_deployment.yaml
cat sklearn_iris_deployment.yaml
apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
name: seldon-deployment-example
namespace: john
spec:
name: sklearn-iris-deployment
predictors:
- componentSpecs:
- spec:
containers:
- image: seldonio/sklearn-iris:0.1
imagePullPolicy: IfNotPresent
name: sklearn-iris-classifier
graph:
children: []
endpoint:
type: REST
name: sklearn-iris-classifier
type: MODEL
name: sklearn-iris-predictor
replicas: 1
kubectl get sdep -n john seldon-deployment-example -o json | jq .status
"deploymentStatus": {
"sklearn-iris-deployment-sklearn-iris-predictor-0e43a2c": {
"availableReplicas": 1,
"replicas": 1
}
},
"serviceStatus": {
"seldon-635d389a05411932517447289ce51cde": {
"httpEndpoint": "seldon-635d389a05411932517447289ce51cde.john:9000",
"svcName": "seldon-635d389a05411932517447289ce51cde"
},
"seldon-bb8b177b8ec556810898594b27b5ec16": {
"grpcEndpoint": "seldon-bb8b177b8ec556810898594b27b5ec16.john:5001",
"httpEndpoint": "seldon-bb8b177b8ec556810898594b27b5ec16.john:8000",
"svcName": "seldon-bb8b177b8ec556810898594b27b5ec16"
}
},
"state": "Available"
}
5.here iam using istio and as per this doc https://docs.seldon.io/projects/seldon-core/en/v1.1.0/workflow/serving.html i did the same
Istio
Istio REST
Assuming the istio gateway is at <istioGateway> and with a Seldon deployment name <deploymentName> in namespace <namespace>:
A REST endpoint will be exposed at : http://<istioGateway>/seldon/<namespace>/<deploymentName>/api/v1.0/predictions
curl -s http://localhost:8003/seldon/john/sklearn-iris-deployment-sklearn-iris-predictor-0e43a2c/api/v0.1/predictions -H "Content-Type: application/json" -d '{"data":{"ndarray":[[5.964,4.006,2.081,1.031]]}}' -v
* Trying 127.0.0.1...
* TCP_NODELAY set
* Connected to localhost (127.0.0.1) port 8003 (#0)
> POST /seldon/johnson-az-videspan/sklearn-iris-deployment-sklearn-iris-predictor-0e43a2c/api/v0.1/predictions HTTP/1.1
> Host: localhost:8003
> User-Agent: curl/7.58.0
> Accept: */*
> Content-Type: application/json
> Content-Length: 48
>
* upload completely sent off: 48 out of 48 bytes
< HTTP/1.1 301 Moved Permanently
< location: https://localhost:8003/seldon/john/sklearn-iris-deployment-sklearn-iris-predictor-0e43a2c/api/v0.1/predictions
< date: Fri, 23 Oct 2020 13:09:46 GMT
< server: istio-envoy
< connection: close
< content-length: 0
<
* Closing connection 0
the same thing happen in sklearn_spacy_text model too but i wonder the same models working perfectly while running it on docker.
please find the sample responce from docker
curl -s http://localhost:5000/predict -H "Content-Type: application/json" -d '{"data":{"ndarray":[[5.964,4.006,2.081,1.031]]}}' -v
* Trying 127.0.0.1...
* TCP_NODELAY set
* Connected to localhost (127.0.0.1) port 5000 (#0)
> POST /predict HTTP/1.1
> Host: localhost:5000
> User-Agent: curl/7.61.1
> Accept: */*
> Content-Type: application/json
> Content-Length: 48
>
* upload completely sent off: 48 out of 48 bytes
* HTTP 1.0, assume close after body
< HTTP/1.0 200 OK
< Content-Type: application/json
< Content-Length: 125
< Access-Control-Allow-Origin: *
< Server: Werkzeug/1.0.0 Python/3.7.4
< Date: Fri, 23 Oct 2020 11:18:31 GMT
<
{"data":{"names":["t:0","t:1","t:2"],"ndarray":[[0.9548873249364169,0.04505474761561406,5.7927447968952436e-05]]},"meta":{}}
* Closing connection 0
curl -s http://localhost:5001/predict -H "Content-Type: application/json" -d '{"data": {"names": ["text"], "ndarray": ["Hello world this is a test"]}}'
{"data":{"names":["t:0","t:1"],"ndarray":[[0.6811839197596743,0.3188160802403257]]},"meta":{}}
can any one help to resolve this issue

Answer 1

Issue
It appears that the request you make incorrectly tries to re-direct to an https protocol (port 443)
Solution
Use https instead of http
curl -s https://localhost:8003/seldon/john/sklearn-iris-deployment-sklearn-iris-predictor-0e43a2c/api/v0.1/predictions -H "Content-Type: application/json" -d '{"data":{"ndarray":[[5.964,4.006,2.081,1.031]]}}' -v
Use curl with -L flag, which instructs curl to follow redirects. In this case, the server returned a redirect response (301 Moved Permanently) for the HTTP request to http://localhost:8003. The redirect response instructs the client to send an additional request, this time using HTTPS, to https://localhost:8003.
More about it here.