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Kserve之RawDeployment模式部署
最近更新：2025-08-11   |   字数总计：1.8k   |   阅读估时：9分钟   |  

前置说明

Kserve 有多种安装部署模式，包括 Serverless、ModelMesh 以及 Kubernetes Deployment，下文的安装模型采用的 Kubernetes Deployment 中的 RawDeployment 模式，其他安装详见官方文档，

官方文档提到安装 Kserve 需要依赖 Istio 和 Cert Manager、由于 Isito 组件比较重且不太了解，考虑到我的目标是为了快速完成 kserve 的部署，所以就使用 Ingress-nginx 来替代了 Istio，效果是一样。另外安装时需要注意版本对应关系

K8s v1.29.7
KServe v0.13.0
Cert-manager v1.15.3
Ingress-nginx（与k8s版本对应关系） v4.11.2

依赖组件

ingress-nginx 部署

该组件采用 Helm 安装

  • 如果在外网状态良好时，可以采用在线安装，由于当前是公司内网，所以采用将 helm 安装包下载下来，进行离线安装
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# 添加repo
helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx

# 下载ingress-nginx.tgz
wget https://github.com/kubernetes/ingress-nginx/releases/download/helm-chart-4.11.2/ingress-nginx-4.11.2.tgz

# 解压
tar -zxf ingress-nginx-4.11.2.tgz

# 安装
helm install ingress-nginx ./ingress-nginx --namespace ingress-nginx --create-namespace


# 由于ingress-nginx依赖的镜像被墙无法下载，所以需要曲线救国，主要删除value.yaml中的镜像hash,需要在执行节点下载
docker pull k8s.kubesre.xyz/ingress-nginx/kube-webhook-certgen:v1.4.3
docker tag k8s.kubesre.xyz/ingress-nginx/kube-webhook-certgen:v1.4.3 registry.k8s.io/ingress-nginx/kube-webhook-certgen:v1.4.3

docker pull k8s.kubesre.xyz/ingress-nginx/controller:v1.11.2
docker tag k8s.kubesre.xyz/ingress-nginx/controller:v1.11.2 registry.k8s.io/ingress-nginx/controller:v1.11.2
  • 安装成功后满刻看到如下信息
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(base) [root@node77 ingress-nginx]# helm install ingress-nginx ./ingress-nginx --namespace ingress-nginx
NAME: ingress-nginx
LAST DEPLOYED: Fri Aug 30 19:34:39 2024
NAMESPACE: ingress-nginx
STATUS: deployed
REVISION: 1
TEST SUITE: None
NOTES:
The ingress-nginx controller has been installed.
It may take a few minutes for the load balancer IP to be available.
You can watch the status by running 'kubectl get service --namespace ingress-nginx ingress-nginx-controller --output wide --watch'

An example Ingress that makes use of the controller:
  apiVersion: networking.k8s.io/v1
  kind: Ingress
  metadata:
    name: example
    namespace: foo
  spec:
    ingressClassName: nginx
    rules:
      - host: www.example.com
        http:
          paths:
            - pathType: Prefix
              backend:
                service:
                  name: exampleService
                  port:
                    number: 80
              path: /
    # This section is only required if TLS is to be enabled for the Ingress
    tls:
      - hosts:
        - www.example.com
        secretName: example-tls

If TLS is enabled for the Ingress, a Secret containing the certificate and key must also be provided:

  apiVersion: v1
  kind: Secret
  metadata:
    name: example-tls
    namespace: foo
  data:
    tls.crt: <base64 encoded cert>
    tls.key: <base64 encoded key>
  type: kubernetes.io/tls
(base) [root@node77 ingress-nginx]# kubectl get service --namespace ingress-nginx ingress-nginx-controller --output wide --watch
NAME                       TYPE           CLUSTER-IP    EXTERNAL-IP   PORT(S)                      AGE   SELECTOR
ingress-nginx-controller   LoadBalancer   10.96.1.190   <pending>     80:32702/TCP,443:32064/TCP   37s   app.kubernetes.io/component=controller,app.kubernetes.io/instance=ingress-nginx,app.kubernetes.io/name=ingress-nginx

CertManager 部署

本组件采用 YAML 方式安装，若使用 Helm 安装，请参考：Link

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kubectl apply -f https://github.com/cert-manager/cert-manager/releases/download/v1.15.3/cert-manager.yaml

(base) [root@node77 cert-manager]# kubectl get pods --namespace cert-manager
NAME                                       READY   STATUS    RESTARTS   AGE
cert-manager-cainjector-5fd6444f95-rbczx   1/1     Running   0          80s
cert-manager-d894bbbd4-zd8ns               1/1     Running   0          80s
cert-manager-webhook-869674f96f-cgp92      1/1     Running   0          80s

kserve 部署

安装

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kubectl apply -f https://github.com/kserve/kserve/releases/download/v0.13.0/kserve.yaml

kubectl apply -f https://github.com/kserve/kserve/releases/download/v0.13.0/kserve-cluster-resources.yaml

# 修改 inferenceservice-config 的部署模型为 RawDeployment
kubectl patch configmap/inferenceservice-config -n kserve --type=strategic -p '{"data": {"deploy": "{\"defaultDeploymentMode\": \"RawDeployment\"}"}}'


# 将kserve.yaml中ingressClassName的值修改为nginx，默认值为istio
ingress: |-
{
    "ingressClassName" : "nginx",
}

测试

  1. 新建一个 Namespace

不要放在默认 namespace 下，很重要很关键，

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kubectl create namespace test
  1. 创建一个 inferenceservice

官方的案例的 yaml 中，并没有指定 namespace，如果执行下列命令就会创建到默认 namespace 下，后续部署模型就会遇到问题，该问题排查了好久。

  • 官网示例
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kubectl apply -n test -f - <<EOF
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "sklearn-iris"
spec:
  predictor:
    model:
      modelFormat:
        name: sklearn
      storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"
EOF

  • 对上述内容进行修改。
    • 新建 yaml 文件(sklearn-iris-model.yaml)
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apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "sklearn-iris-1"
  namespace: "test"  # 这个很关键很重要
spec:
  predictor:
    model:
      modelFormat:
        name: sklearn
      storageUri: "http://172.16.x.xx:9999/model.joblib"
  • 由于官方给的示例的模型存储在 gs 上，一般情况下无法直接下载，我个人的解决方法是复制storageUri 的地址（models/sklearn/1.0/model），点开平台下载链接的后，将路径拼在后面进行下载到本地（需要注意，需要下载该模型前缀下的所有资源，并放到一个文件夹下），然后在上传到部署的环境中。
  1. 在模型文件夹下，启动一个 httpserver

该步骤非必须，如果可以直接从 gs 下，则忽略这一步。启动外部服务的目的是能够让 kserve 能够去下载模型

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(base) [root@node77 test-kserve-deploy]# python -m http.server 9999
Serving HTTP on 0.0.0.0 port 9999 (http://0.0.0.0:9999/) ...
127.0.0.1 - - [03/Sep/2024 12:19:13] "GET / HTTP/1.1" 200 -

(base) [root@node77 kserve]# curl 127.0.0.1:9999
<!DOCTYPE HTML>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Directory listing for /</title>
</head>
<body>
<h1>Directory listing for /</h1>
<hr>
<ul>
<li><a href="mlflow-iris.yml">mlflow-iris.yml</a></li>
<li><a href="mlflow-wine-model.yaml">mlflow-wine-model.yaml</a></li>
<li><a href="mlflow_wine/">mlflow_wine/</a></li>
<li><a href="mlflow_wine.zip">mlflow_wine.zip</a></li>
<li><a href="my_model/">my_model/</a></li>
<li><a href="my_model.zip">my_model.zip</a></li>
<li><a href="python_env.yaml">python_env.yaml</a></li>
<li><a href="python_model.pkl">python_model.pkl</a></li>
<li><a href="sklearn_iris/">sklearn_iris/</a></li>
<li><a href="tmp/">tmp/</a></li>
</ul>
<hr>
</body>
</html>
(base) [root@node77 kserve]#
  • 执行 yaml 文件
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# 在此目录下
(base) [root@node77 sklearn_iris]# ls
model.joblib  sklearn-iris-model.yaml

# 执行命令
kubectl apply -f sklearn-iris-model.yaml
  • 查看模型部署状态
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(base) [root@node77 sklearn_iris]# kubectl get all -n test
NAME                                            READY   STATUS    RESTARTS   AGE
pod/sklearn-iris-1-predictor-84d48876bb-wvlm7   1/1     Running   0          40s

NAME                               TYPE        CLUSTER-IP    EXTERNAL-IP   PORT(S)   AGE
service/sklearn-iris-1-predictor   ClusterIP   10.96.0.243   <none>        80/TCP    40s

NAME                                       READY   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/sklearn-iris-1-predictor   1/1     1            1           40s

NAME                                                  DESIRED   CURRENT   READY   AGE
replicaset.apps/sklearn-iris-1-predictor-84d48876bb   1         1         1       40s

NAME                                                           REFERENCE                             TARGETS         MINPODS   MAXPODS   REPLICAS   AGE
horizontalpodautoscaler.autoscaling/sklearn-iris-1-predictor   Deployment/sklearn-iris-1-predictor   <unknown>/80%   1         1         1          40s
  • 查看 inferenceservice 状态
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(base) [root@node77 sklearn_iris]# kubectl get inferenceservice -n test
NAME             URL                                      READY   PREV   LATEST   PREVROLLEDOUTREVISION   LATESTREADYREVISION   AGE
sklearn-iris-1   http://sklearn-iris-1-test.example.com   True                                                                  5m48s

使用 ingress 测试

  • 查看 ingress-nginx 访问地址
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(base) [root@node77 sklearn_iris]# kubectl get svc -n test
NAME                       TYPE        CLUSTER-IP    EXTERNAL-IP   PORT(S)   AGE
sklearn-iris-1-predictor   ClusterIP   10.96.0.243   <none>        80/TCP    7m2s
  • 编写输入参数 文件，input.json
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{
  "instances": [
    [6.8,  2.8,  4.8,  1.4],
    [6.0,  3.4,  4.5,  1.6]
  ]
}
  • 编写测试脚本
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#!/bin/bash

# nginx ingress 的访问地址
INGRESS_HOST=10.96.0.243
INGRESS_PORT=80

MODEL_NAME=sklearn-iris-1
INPUT_PATH=@./input.json

SERVICE_HOSTNAME=$(kubectl get inferenceservice ${MODEL_NAME} -o jsonpath='{.status.url}' | cut -d "/" -f 3)

# 使用 curl 发起请求
curl -H "Content-Type: application/json" -H "Host: ${SERVICE_HOSTNAME}" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict -d $INPUT_PATH

  • 执行测试 sh -x infer_test.sh
  • 测试结果如下图

2025-03-15 该篇文章被 sswfive 打上标签: K8S Kserve 归为分类: ModelOps

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