Kubernetes SeldonIO | sklearn | iris和sklearn | spacy | text |不在k8s中工作

参考：

#完成的步骤

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

kubectl get sdep-n john seldon部署示例-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.我在这里使用istio，根据这份文件，我也这样做了

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

有人能帮助解决这个问题吗

问题

---

您发出的请求似乎错误地尝试重新定向到https协议（端口443）

解决方案 使用https而不是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curl with-L标志，该标志指示curl遵循重定向。在这种情况下，服务器为HTTP请求返回一个重定向响应（301永久移动到

http://localhost:8003

。重定向响应指示客户端向

https://localhost:8003

---

更多信息。

您发出的请求似乎错误地尝试重新定向到https协议（端口443），您能说明如何配置istio网关吗？下面是如何为http配置的示例。你能试一下吗，告诉我这是否有效？@Jakub是的，你是对的，它可以与https一起工作。。谢谢你的回复。我有一个问题是，通过helm安装了seldon core analytics，`` helm安装seldon core analytics seldon core analytics\--repo\--namespace seldon system``问题是预测分析仪表板没有显示任何metricsHi@Antony Johnson，我已经从上述评论中做出了回答，如果此答案或任何其他答案解决了您的问题，请将其标记为已接受，或根据进行投票。关于第二个问题，请遵循stackoverflow规则并为您的新问题创建新问题，kubernetes helm标签在这里可能会有所帮助。

*   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

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":{}}

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