基于Kubernetes和Jenkins来实现CI/CD。 全部须要跑任务的jenkins slave(pod)经过模版动态建立,当任务执行结束自动删除。node
kubernetspython
jenkins-deployment.yaml
apiVersion: "apps/v1beta1" kind: "Deployment" metadata: name: "jenkins" labels: name: "jenkins" spec: replicas: 1 template: metadata: name: "jenkins" labels: name: "jenkins" spec: containers: - name: jenkins image: jenkinsci/jenkins:2.154 imagePullPolicy: IfNotPresent volumeMounts: - name: jenkins-home mountPath: /var/jenkins_home env: - name: TZ value: Asia/Shanghai ports: - containerPort: 8080 name: web - containerPort: 50000 name: agent volumes: - name: jenkins-home nfs: path: "/nfs/jenkins/data" server: "cpu029.hogpu.cc" terminationGracePeriodSeconds: 10 serviceAccountName: jenkins
jenkins-account.yaml
--- apiVersion: v1 kind: ServiceAccount metadata: name: jenkins --- kind: Role apiVersion: rbac.authorization.k8s.io/v1beta1 metadata: name: jenkins rules: - apiGroups: [""] resources: ["pods"] verbs: ["create","delete","get","list","patch","update","watch"] - apiGroups: [""] resources: ["pods/exec"] verbs: ["create","delete","get","list","patch","update","watch"] - apiGroups: [""] resources: ["pods/log"] verbs: ["get","list","watch"] - apiGroups: [""] resources: ["secrets"] verbs: ["get"] - apiGroups: [""] resources: ["configmap"] verbs: ["get"] --- apiVersion: rbac.authorization.k8s.io/v1beta1 kind: RoleBinding metadata: name: jenkins roleRef: apiGroup: rbac.authorization.k8s.io kind: Role name: jenkins subjects: - kind: ServiceAccount name: jenkins
jenkins-service.yaml
kind: Service apiVersion: v1 metadata: labels: k8s-app: jenkins name: jenkins spec: type: NodePort ports: - port: 8080 name: web targetPort: 8080 - port: 50000 name: agent targetPort: 50000 selector: name: jenkins
说明一下:这里 Service 咱们暴漏了端口 8080 和 50000,8080 为访问 Jenkins Server 页面端口,50000 为建立的 Jenkins Slave 与 Master 创建链接进行通讯的默认端口,若是不暴露的话,Slave 没法跟 Master 创建链接。这里使用 NodePort 方式暴漏端口,并未指定其端口号,由 Kubernetes 系统默认分配,固然也能够指定不重复的端口号(范围在 30000~32767)git
接下来,经过 kubectl 命令行执行建立 Jenkins Service。 $ kubectl create namespace kubernetes-plugin $ kubectl config set-context $(kubectl config current-context) --namespace=kubernetes-plugin $ kubectl create -f jenkins-deployment.yaml $ kubectl create -f jenkins-account.yaml $ kubectl create -f jenkins-service.yaml
ps:github
建立一个新的 namespace 为 kubernetes-plugin,而且将当前 context 设置为 kubernetes-plugin namespace 这样就会自动切换到该空间下。
jianyu.tian@yz-gpu-k8s004 ~]$ kubectl get deployment,svc,pods NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE deploy/jenkins 1 1 1 1 1h NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE svc/jenkins NodePort 10.106.235.91 <none> 8080:31051/TCP,50000:30545/TCP 2h NAME READY STATUS RESTARTS AGE po/jenkins-64564fc5c9-pzlpb 1/1 Running 0 1h
ps:web
Jenkins Master 服务已经启动起来了,而且将端口暴漏到 8080:31051,50000:30545,此时能够经过浏览器打开 http://<Cluster_IP>:30645 访问 Jenkins 页面了。
1.主要对jenkins-plugin插件作说明docker
安装完毕后,点击 “系统管理” —> “系统设置” —> “新增一个云” —> 选择 “Kubernetes”,而后填写 Kubernetes 和 Jenkins 配置信息。centos
ps:
Name 处默认为 kubernetes,也能够修改成其余名称,若是这里修改了,下边在执行 Job 时指定 podTemplate() 参数 cloud 为其对应名称,不然会找不到,cloud 默认值取:kubernetes
Kubernetes URL 处我填写了 https://kubernetes.default.sv... 这里我填写了 Kubernetes Service 对应的 DNS 记录,经过该 DNS 记录能够解析成该 Service 的 Cluster IP,或者直接填写外部 Kubernetes 的地址 https://<ClusterIP>:<Ports>。
Jenkins URL 处我填写了 http://jenkins.kubernetes-plugin:8080,跟上边相似,也是使用 Jenkins Service 对应的 DNS 记录,不过要指定为 8080 端口,由于咱们设置暴漏 8080 端口。同时也能够用 http://<ClusterIP>:<Node_Port>api
配置完毕,能够点击 “Test Connection” 按钮测试是否可以链接的到 Kubernetes,若是显示 Connection test successful 则表示链接成功,配置没有问题。浏览器
建立一个 Pipeline 类型 Job:bash
pipeline { agent any //并行操做 stages { stage("test_all") { parallel { stage("python3-cuda9.2") { agent { kubernetes { label 'mxnet-python3-cuda9' yaml """ apiVersion: "v1" kind: "Pod" metadata: labels: name: "mxnet-python3-cuda9" spec: affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: hobot.workas operator: In values: - gpu - key: kubernetes.io/nvidia-gpu-name operator: In values: - TITAN_V containers: - name: mxnetone image: docker.hobot.cc/dlp/mxnetci:runtime-py3.6-cudnn7.3-cuda9.2-centos7 imagePullPolicy: Always resources: limits: nvidia.com/gpu: 1 """ } } stages { stage("拉取代码") { steps { container("mxnetone") { checkout( [ $class: 'GitSCM', branches: [[name: 'nnvm']], browser: [$class: 'Phabricator', repo: 'rMXNET', repoUrl: ''], doGenerateSubmoduleConfigurations: false, extensions: [[$class: 'SubmoduleOption', disableSubmodules: false, parentCredentials: true, recursiveSubmodules: true, reference: '', trackingSubmodules: false]], submoduleCfg: [], userRemoteConfigs: [[credentialsId: 'zhaoming_private', url: '']] ] ) } } } stage("编译") { steps { container("mxnetone") { sh """ nvidia-smi source /root/.bashrc make deps echo -e "USE_PROFILER=1\nUSE_GLOG=0\nUSE_HDFS=0" >> ./make/config.mk sed -i "s#USE_CUDA_PATH = /usr/local/cuda-8.0#USE_CUDA_PATH = /usr/local/cuda-9.2#g" ./make/config.mk make lint make -j 12 ln -s /home/data ./ make test | tee unittest.log """ } } } stage("单元测试") { steps { container("mxnetone") { sh """ cp -rf python/mxnet ./ cp -f lib/libmxnet.so mxnet/ echo "-------Running tests under Python3-------" python3 -V python3 `which nosetests` tests/python/train python3 `which nosetests` -v -d tests/python/unittest """ } } } } } stage("python2-cuda9.2") { agent { kubernetes { label 'mxnet-python2-cuda9' yaml """ apiVersion: "v1" kind: "Pod" metadata: labels: name: "mxnet-python2-cuda9" spec: affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: hobot.workas operator: In values: - gpu - key: kubernetes.io/nvidia-gpu-name operator: In values: - TITAN_V containers: - name: mxnettwo image: docker.hobot.cc/dlp/mxnetci:runtime-cudnn7.3-cuda9.2-centos7 imagePullPolicy: Always resources: limits: nvidia.com/gpu: 1 """ } } stages { stage("拉取代码") { steps { container("mxnettwo") { checkout( [ $class: 'GitSCM', branches: [[name: 'nnvm']], browser: [$class: 'Phabricator', repo: 'rMXNET', repoUrl: ''], doGenerateSubmoduleConfigurations: false, extensions: [[$class: 'SubmoduleOption', disableSubmodules: false, parentCredentials: true, recursiveSubmodules: true, reference: '', trackingSubmodules: false]], submoduleCfg: [], userRemoteConfigs: [[credentialsId: 'zhaoming_private', url: '']] ] ) } } } stage("编译") { steps { container("mxnettwo") { sh """ nvidia-smi pip2 install numpy==1.14.3 -i https://mirrors.aliyun.com/pypi/simple/ source /root/.bashrc make deps echo -e "USE_PROFILER=1\nUSE_GLOG=0\nUSE_HDFS=0" >> ./make/config.mk sed -i "s#USE_CUDA_PATH = /usr/local/cuda-8.0#USE_CUDA_PATH = /usr/local/cuda-9.2#g" ./make/config.mk make lint make -j 12 ln -s /home/data ./ make test | tee unittest.log """ } } } stage("单元测试") { steps { container("mxnettwo") { sh """ cp -rf python/mxnet ./ cp -f lib/libmxnet.so mxnet/ echo "-------Running tests under Python2-------" python2 -V python2 `which nosetests` tests/python/train python2 `which nosetests` -v -d tests/python/unittest """ } } } } } } } } }