为什么一个seldon部署有两个服务

Question

我注意到，每当我部署一个模型时，就会有两个服务，例如

kubectl get service -n model-namespace 
NAME                            TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)             AGE
iris-model-default              ClusterIP   10.96.82.232   <none>        8000/TCP,5001/TCP   8h
iris-model-default-classifier   ClusterIP   10.96.76.141   <none>        9000/TCP            8h

我想知道为什么我们有两个而不是一个。

三个端口(8000、9000、5001(分别用于什么？我应该用哪一个？

清单yaml是

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
name: iris-model
namespace: model-namespace
spec:
name: iris
predictors:
- graph:
implementation: SKLEARN_SERVER
modelUri: gs://seldon-models/sklearn/iris
name: classifier
name: default
replicas: 1

来自https://docs.seldon.io/projects/seldon-core/en/v1.1.0/workflow/quickstart.html

CRD的定义似乎就在这里，以防有用。

k describe service/iris-model-default
Name:              iris-model-default
Namespace:         model-namespace
Labels:            app.kubernetes.io/managed-by=seldon-core
seldon-app=iris-model-default
seldon-deployment-id=iris-model
Annotations:       getambassador.io/config:
---
apiVersion: ambassador/v1
kind: Mapping
name: seldon_model-namespace_iris-model_default_rest_mapping
prefix: /seldon/model-namespace/iris-model/
rewrite: /
service: iris-model-default.model-namespace:8000
timeout_ms: 3000
---
apiVersion: ambassador/v1
kind: Mapping
name: seldon_model-namespace_iris-model_default_grpc_mapping
grpc: true
prefix: /(seldon.protos.*|tensorflow.serving.*)/.*
prefix_regex: true
rewrite: ""
service: iris-model-default.model-namespace:5001
timeout_ms: 3000
headers:
namespace: model-namespace
seldon: iris-model
Selector:          seldon-app=iris-model-default
Type:              ClusterIP
IP:                10.96.82.232
Port:              http  8000/TCP
TargetPort:        8000/TCP
Endpoints:         172.18.0.17:8000
Port:              grpc  5001/TCP
TargetPort:        8000/TCP
Endpoints:         172.18.0.17:8000
Session Affinity:  None
Events:            <none>

k describe service/iris-model-default-classifier 
Name:              iris-model-default-classifier
Namespace:         model-namespace
Labels:            app.kubernetes.io/managed-by=seldon-core
default=true
model=true
seldon-app-svc=iris-model-default-classifier
seldon-deployment-id=iris-model
Annotations:       <none>
Selector:          seldon-app-svc=iris-model-default-classifier
Type:              ClusterIP
IP:                10.96.76.141
Port:              http  9000/TCP
TargetPort:        9000/TCP
Endpoints:         172.18.0.17:9000
Session Affinity:  None
Events:            <none>

k get pods --show-labels
NAME                                               READY   STATUS    RESTARTS   AGE   LABELS
iris-model-default-0-classifier-579765fc5b-rm6np   2/2     Running   0          10h   app.kubernetes.io/managed-by=seldon-core,app=iris-model-default-0-classifier,fluentd=true,pod-template-hash=579765fc5b,seldon-app-svc=iris-model-default-classifier,seldon-app=iris-model-default,seldon-deployment-id=iris-model,version=default

所以只涉及一个吊舱，我认为这些端口是从不同的容器映射的：

k get pods -o json | jq '.items[].spec.containers[] | .name, .ports'                                                                                                                           [0] 0s 
"classifier"
[
{
"containerPort": 6000,
"name": "metrics",
"protocol": "TCP"
},
{
"containerPort": 9000,
"name": "http",
"protocol": "TCP"
}
]
"seldon-container-engine"
[
{
"containerPort": 8000,
"protocol": "TCP"
},
{
"containerPort": 8000,
"name": "metrics",
"protocol": "TCP"
}
]

一个更具体的问题是，为什么需要这么多端口？

Answer 1

是的，看起来您的pod中有两个容器。

第一项服务：

iris-model-default➡️seldon-container-engineHTTP:8000:8000和GRPC:5001:8000

第二项服务：

iris-model-default-classifier➡️classifierHTTP:9000:9000(6000在内部用于度量(

你没有提到，但听起来像是部署了分类器：

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
name: iris-model
namespace: seldon
spec:
name: iris
predictors:
- graph:
implementation: SKLEARN_SERVER
modelUri: gs://seldon-models/sklearn/iris
name: classifier
name: default
replicas: 1

如果你想了解为什么这两个容器/服务背后的理由，你可能会深入了解运营商本身🔧.