kubernetes - SeldonIO | sklearn_iris和sklearn_spacy_text |在k8s中不起作用

参考:
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
＃步骤完成

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 |状态

  "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.在这里我使用istio，并且按照这个doc https://docs.seldon.io/projects/seldon-core/en/v1.1.0/workflow/serving.html，我做了同样的事情

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

sklearn_spacy_text 模型也会发生相同的事情，但是我想知道相同的模型在docker上运行时能否完美运行。
请从docker找到示例响应

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":{}}

谁能帮助解决这个问题

最佳答案

问题
您似乎不正确地发出了请求，尝试将其重定向到https协议(protocol)(端口443)
解
使用 https代替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

使用带有-L标志的 curl，它指示curl遵循重定向。在这种情况下，服务器将HTTP请求的重定向响应(永久移动301)返回到http://localhost:8003。重定向响应指示客户端向https://localhost:8003发送附加请求，这次使用HTTPS。
有关它的更多信息here。

关于kubernetes - SeldonIO | sklearn_iris和sklearn_spacy_text |在k8s中不起作用，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/64500794/