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Docker容器中使用GPU 背景容器封装了应用程序的依赖项，以提供可重复和可靠的应用程序和服务执行，而无需整个虚拟机的开销。如果您曾经花了一天的时间为一个科学或 深度学习 应用程序提供一个包含大量软件包的服务器，或者已经花费数周的时间来确保您的应用程序可以在多个 linux 环境中构建和部署，那么 Docker 容器非常值得您花费时间。安装添加docker源[root@localhost ~]# sudo yum-config-manager --add-repo=https://download.docker.com/linux/centos/docker-ce.repo Loaded plugins: fastestmirror, langpacks adding repo from: https://download.docker.com/linux/centos/docker-ce.repo grabbing file https://download.docker.com/linux/centos/docker-ce.repo to /etc/yum.repos.d/docker-ce.repo repo saved to /etc/yum.repos.d/docker-ce.repo [root@localhost ~]# [root@localhost ~]# cat /etc/yum.repos.d/docker-ce.repo [docker-ce-stable] name=Docker CE Stable - $basearch baseurl=https://download.docker.com/linux/centos/$releasever/$basearch/stable enabled=1 gpgcheck=1 gpgkey=https://download.docker.com/linux/centos/gpg [docker-ce-stable-debuginfo] name=Docker CE Stable - Debuginfo $basearch baseurl=https://download.docker.com/linux/centos/$releasever/debug-$basearch/stable enabled=0 gpgcheck=1 gpgkey=https://download.docker.com/linux/centos/gpg [docker-ce-stable-source] name=Docker CE Stable - Sources baseurl=https://download.docker.com/linux/centos/$releasever/source/stable enabled=0 gpgcheck=1 gpgkey=https://download.docker.com/linux/centos/gpg [docker-ce-test] name=Docker CE Test - $basearch baseurl=https://download.docker.com/linux/centos/$releasever/$basearch/test enabled=0 gpgcheck=1 gpgkey=https://download.docker.com/linux/centos/gpg [docker-ce-test-debuginfo] name=Docker CE Test - Debuginfo $basearch baseurl=https://download.docker.com/linux/centos/$releasever/debug-$basearch/test enabled=0 gpgcheck=1 gpgkey=https://download.docker.com/linux/centos/gpg [docker-ce-test-source] name=Docker CE Test - Sources baseurl=https://download.docker.com/linux/centos/$releasever/source/test enabled=0 gpgcheck=1 gpgkey=https://download.docker.com/linux/centos/gpg [docker-ce-nightly] name=Docker CE Nightly - $basearch baseurl=https://download.docker.com/linux/centos/$releasever/$basearch/nightly enabled=0 gpgcheck=1 gpgkey=https://download.docker.com/linux/centos/gpg [docker-ce-nightly-debuginfo] name=Docker CE Nightly - Debuginfo $basearch baseurl=https://download.docker.com/linux/centos/$releasever/debug-$basearch/nightly enabled=0 gpgcheck=1 gpgkey=https://download.docker.com/linux/centos/gpg [docker-ce-nightly-source] name=Docker CE Nightly - Sources baseurl=https://download.docker.com/linux/centos/$releasever/source/nightly enabled=0 gpgcheck=1 gpgkey=https://download.docker.com/linux/centos/gpg [root@localhost ~]#下载安装包[root@localhost ~]# cd docker [root@localhost docker]# [root@localhost docker]# repotrack docker-ce安装docker 并设置开机自启[root@localhost docker]# yum install ./* [root@localhost docker]# systemctl start docker [root@localhost docker]# [root@localhost docker]# systemctl enable docker Created symlink from /etc/systemd/system/multi-user.target.wants/docker.service to /usr/lib/systemd/system/docker.service. [root@localhost docker]#配置nvidia-docker的源[root@localhost docker]# distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ > && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo | sudo tee /etc/yum.repos.d/nvidia-docker.repo [root@localhost docker]# cat /etc/yum.repos.d/nvidia-docker.repo [libnvidia-container] name=libnvidia-container baseurl=https://nvidia.github.io/libnvidia-container/stable/centos7/$basearch repo_gpgcheck=1 gpgcheck=0 enabled=1 gpgkey=https://nvidia.github.io/libnvidia-container/gpgkey sslverify=1 sslcacert=/etc/pki/tls/certs/ca-bundle.crt [libnvidia-container-experimental] name=libnvidia-container-experimental baseurl=https://nvidia.github.io/libnvidia-container/experimental/centos7/$basearch repo_gpgcheck=1 gpgcheck=0 enabled=0 gpgkey=https://nvidia.github.io/libnvidia-container/gpgkey sslverify=1 sslcacert=/etc/pki/tls/certs/ca-bundle.crt [nvidia-container-runtime] name=nvidia-container-runtime baseurl=https://nvidia.github.io/nvidia-container-runtime/stable/centos7/$basearch repo_gpgcheck=1 gpgcheck=0 enabled=1 gpgkey=https://nvidia.github.io/nvidia-container-runtime/gpgkey sslverify=1 sslcacert=/etc/pki/tls/certs/ca-bundle.crt [nvidia-container-runtime-experimental] name=nvidia-container-runtime-experimental baseurl=https://nvidia.github.io/nvidia-container-runtime/experimental/centos7/$basearch repo_gpgcheck=1 gpgcheck=0 enabled=0 gpgkey=https://nvidia.github.io/nvidia-container-runtime/gpgkey sslverify=1 sslcacert=/etc/pki/tls/certs/ca-bundle.crt [nvidia-docker] name=nvidia-docker baseurl=https://nvidia.github.io/nvidia-docker/centos7/$basearch repo_gpgcheck=1 gpgcheck=0 enabled=1 gpgkey=https://nvidia.github.io/nvidia-docker/gpgkey sslverify=1 sslcacert=/etc/pki/tls/certs/ca-bundle.crt [root@localhost docker]#安装下载nvidia-docker[root@localhost ~]# mkdir nvidia-docker2 [root@localhost ~]# cd nvidia-docker2 [root@localhost nvidia-docker2]# yum update -y [root@localhost nvidia-docker2]# repotrack nvidia-docker2 [root@localhost nvidia-docker2]# yum install ./* [root@localhost ~]# mkdir nvidia-container-toolkit [root@localhost ~]# cd nvidia-container-toolkit [root@localhost nvidia-container-toolkit]# repotrack nvidia-container-toolkit [root@ai-rd nvidia-container-toolkit]# yum install ./*下载镜像，并保存[root@localhost ~]# docker pull nvidia/cuda:11.0-base 11.0-base: Pulling from nvidia/cuda 54ee1f796a1e: Pull complete f7bfea53ad12: Pull complete 46d371e02073: Pull complete b66c17bbf772: Pull complete 3642f1a6dfb3: Pull complete e5ce55b8b4b9: Pull complete 155bc0332b0a: Pull complete Digest: sha256:774ca3d612de15213102c2dbbba55df44dc5cf9870ca2be6c6e9c627fa63d67a Status: Downloaded newer image for nvidia/cuda:11.0-base docker.io/nvidia/cuda:11.0-base [root@localhost ~]# [root@localhost ~]# docker images REPOSITORY TAG IMAGE ID CREATED SIZE nvidia/cuda 11.0-base 2ec708416bb8 15 months ago 122MB [root@localhost ~]# [root@localhost ~]# docker save -o cuda-11.0.tar nvidia/cuda:11.0-base [root@localhost ~]# [root@localhost ~]# ls cuda-11.0.tar cuda-11.0.tar [root@localhost ~]#在要测试的服务器上导入镜像[root@ai-rd cby]# docker load -i cuda-11.0.tar 2ce3c188c38d: Loading layer [==================================================>] 75.23MB/75.23MB ad44aa179b33: Loading layer [==================================================>] 1.011MB/1.011MB 35a91a75d24b: Loading layer [==================================================>] 15.36kB/15.36kB a4399aeb9a0e: Loading layer [==================================================>] 3.072kB/3.072kB fa39d0e9f3dc: Loading layer [==================================================>] 18.84MB/18.84MB 232fb43df6ad: Loading layer [==================================================>] 30.08MB/30.08MB 0da51e35db05: Loading layer [==================================================>] 22.53kB/22.53kB Loaded image: nvidia/cuda:11.0-base [root@ai-rd cby]# [root@ai-rd cby]# docker images | grep cuda nvidia/cuda 11.0-base 2ec708416bb8 15 months ago 122MB [root@ai-rd cby]#安装升级内核[root@ai-rd cby]# yum install kernel-headers [root@ai-rd cby]# yum install kernel-devel [root@ai-rd cby]# yum update kernel*禁用模块，并升级boot[root@ai-rd cby]# vim /etc/modprobe.d/blacklist-nouveau.conf [root@ai-rd cby]# cat /etc/modprobe.d/blacklist-nouveau.conf blacklist nouveau options nouveau modeset=0 [root@ai-rd cby]# [root@ai-rd cby]# mv /boot/initramfs-$(uname -r).img /boot/initramfs-$(uname -r).img.bak [root@ai-rd cby]# sudo dracut -v /boot/initramfs-$(uname -r).img $(uname -r)下载驱动并安装[root@localhost ~]# wget https://cn.download.nvidia.cn/tesla/450.156.00/NVIDIA-Linux-x86_64-450.156.00.run [root@ai-rd cby]# chmod +x NVIDIA-Linux-x86_64-450.156.00.run [root@ai-rd cby]# ./NVIDIA-Linux-x86_64-450.156.00.run配置docker[root@ai-rd ~]# vim /etc/docker/daemon.json [root@ai-rd ~]# cat /etc/docker/daemon.json { "runtimes": { "nvidia": { "path": "nvidia-container-runtime", "runtimeArgs": [] } } } [root@ai-rd ~]# [root@ai-rd ~]# systemctl daemon-reload [root@ai-rd ~]# [root@ai-rd ~]# [root@ai-rd ~]# [root@ai-rd ~]# systemctl restart docker [root@ai-rd ~]#测试docker中的调用情况[root@ai-rd ~]# [root@ai-rd ~]# sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi Tue Nov 23 06:03:04 2021 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 450.156.00 Driver Version: 450.156.00 CUDA Version: 11.0 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 Tesla T4 Off | 00000000:86:00.0 Off | 0 | | N/A 90C P0 34W / 70W | 0MiB / 15109MiB | 6% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+ [root@ai-rd ~]# https://blog.csdn.net/qq_33921750https://my.oschina.net/u/3981543https://www.zhihu.com/people/chen-bu-yun-2https://segmentfault.com/u/hppyvyv6/articleshttps://juejin.cn/user/3315782802482007https://space.bilibili.com/352476552/articlehttps://cloud.tencent.com/developer/column/93230知乎、CSDN、开源中国、思否、掘金、哔哩哔哩、腾讯云本文使用 文章同步助手 同步 -
在Kubernetes（k8s）中使用GPU 介绍Kubernetes 支持对节点上的 AMD 和 NVIDIA GPU （图形处理单元）进行管理，目前处于实验状态。修改docker配置文件root@hello:~# cat /etc/docker/daemon.json { "default-runtime": "nvidia", "runtimes": { "nvidia": { "path": "/usr/bin/nvidia-container-runtime", "runtimeArgs": [] } }, "data-root": "/var/lib/docker", "exec-opts": ["native.cgroupdriver=systemd"], "registry-mirrors": [ "https://docker.mirrors.ustc.edu.cn", "http://hub-mirror.c.163.com" ], "insecure-registries": ["127.0.0.1/8"], "max-concurrent-downloads": 10, "live-restore": true, "log-driver": "json-file", "log-level": "warn", "log-opts": { "max-size": "50m", "max-file": "1" }, "storage-driver": "overlay2" } root@hello:~# root@hello:~# systemctl daemon-reload root@hello:~# systemctl start docker添加标签root@hello:~# kubectl label nodes 192.168.1.56 nvidia.com/gpu.present=true root@hello:~# kubectl get nodes -L nvidia.com/gpu.present NAME STATUS ROLES AGE VERSION GPU.PRESENT 192.168.1.55 Ready,SchedulingDisabled master 128m v1.22.2 192.168.1.56 Ready node 127m v1.22.2 true root@hello:~#安装helm仓库root@hello:~# curl https://baltocdn.com/helm/signing.asc | sudo apt-key add - root@hello:~# sudo apt-get install apt-transport-https --yes root@hello:~# echo "deb https://baltocdn.com/helm/stable/debian/ all main" | sudo tee /etc/apt/sources.list.d/helm-stable-debian.list root@hello:~# sudo apt-get update root@hello:~# sudo apt-get install helm helm install \ --version=0.10.0 \ --generate-name \ nvdp/nvidia-device-plugin查看是否有nvidiaroot@hello:~# kubectl describe node 192.168.1.56 | grep nv nvidia.com/gpu.present=true nvidia.com/gpu: 1 nvidia.com/gpu: 1 kube-system nvidia-device-plugin-1637728448-fgg2d 0 (0%) 0 (0%) 0 (0%) 0 (0%) 50s nvidia.com/gpu 0 0 root@hello:~#下载镜像root@hello:~# docker pull registry.cn-beijing.aliyuncs.com/ai-samples/tensorflow:1.5.0-devel-gpu root@hello:~# docker save -o tensorflow-gpu.tar registry.cn-beijing.aliyuncs.com/ai-samples/tensorflow:1.5.0-devel-gpu root@hello:~# docker load -i tensorflow-gpu.tar创建tensorflow测试podroot@hello:~# vim gpu-test.yaml root@hello:~# cat gpu-test.yaml apiVersion: v1 kind: Pod metadata: name: test-gpu labels: test-gpu: "true" spec: containers: - name: training image: registry.cn-beijing.aliyuncs.com/ai-samples/tensorflow:1.5.0-devel-gpu command: - python - tensorflow-sample-code/tfjob/docker/mnist/main.py - --max_steps=300 - --data_dir=tensorflow-sample-code/data resources: limits: nvidia.com/gpu: 1 tolerations: - effect: NoSchedule operator: Exists root@hello:~# root@hello:~# kubectl apply -f gpu-test.yaml pod/test-gpu created root@hello:~#查看日志root@hello:~# kubectl logs test-gpu WARNING:tensorflow:From tensorflow-sample-code/tfjob/docker/mnist/main.py:120: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version. Instructions for updating: Future major versions of TensorFlow will allow gradients to flow into the labels input on backprop by default. See tf.nn.softmax_cross_entropy_with_logits_v2. 2021-11-24 04:38:50.846973: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:895] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-11-24 04:38:50.847698: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1105] Found device 0 with properties: name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59 pciBusID: 0000:00:10.0 totalMemory: 14.75GiB freeMemory: 14.66GiB 2021-11-24 04:38:50.847759: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1195] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: Tesla T4, pci bus id: 0000:00:10.0, compute capability: 7.5) root@hello:~# https://blog.csdn.net/qq_33921750https://my.oschina.net/u/3981543https://www.zhihu.com/people/chen-bu-yun-2https://segmentfault.com/u/hppyvyv6/articleshttps://juejin.cn/user/3315782802482007https://space.bilibili.com/352476552/articlehttps://cloud.tencent.com/developer/column/93230知乎、CSDN、开源中国、思否、掘金、哔哩哔哩、腾讯云本文使用 文章同步助手 同步
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Kubernetes（k8s）集群安装JupyterHub以及Lab 背景JupyterHub 为用户组带来了笔记本的强大功能。它使用户能够访问计算环境和资源，而不会给用户带来安装和维护任务的负担。用户——包括学生、研究人员和数据科学家——可以在他们自己的工作空间中完成他们的工作，共享资源可以由系统管理员有效管理。JupyterHub 在云端或您自己的硬件上运行，可以为世界上的任何用户提供预先配置的数据科学环境。它是可定制和可扩展的，适用于小型和大型团队、学术课程和大型基础设施。第一步、参考：https://cloud.tencent.com/developer/article/1902519 创建动态挂载存储第二步、安装helmroot@hello:~# curl https://baltocdn.com/helm/signing.asc | sudo apt-key add - root@hello:~# sudo apt-get install apt-transport-https --yes root@hello:~# echo "deb https://baltocdn.com/helm/stable/debian/ all main" | sudo tee /etc/apt/sources.list.d/helm-stable-debian.list root@hello:~# sudo apt-get update root@hello:~# sudo apt-get install helm第三步、导入镜像root@hello:~# docker load -i pause-3.5.tar root@hello:~# docker load -i kube-scheduler.tar第四步、安装jupyterhubhelm repo add jupyterhub https://jupyterhub.github.io/helm-chart/ helm repo update helm upgrade --cleanup-on-fail \ --install ju jupyterhub/jupyterhub \ --namespace ju \ --create-namespace \ --version=1.2.0 \ --values config.yaml注：此文件可以自定义内容，具体看注释，如下开启lab功能root@hello:~# vim config.yaml root@hello:~# cat config.yaml # This file can update the JupyterHub Helm chart's default configuration values. # # # # For reference see the configuration reference and default values, but make # # sure to refer to the Helm chart version of interest to you! # # # # Introduction to YAML: https://www.youtube.com/watch?v=cdLNKUoMc6c # # Chart config reference: https://zero-to-jupyterhub.readthedocs.io/en/stable/resources/reference.html # # Chart default values: https://github.com/jupyterhub/zero-to-jupyterhub-k8s/blob/HEAD/jupyterhub/values.yaml # # Available chart versions: https://jupyterhub.github.io/helm-chart/ # # singleuser: defaultUrl: "/lab" extraEnv: JUPYTERHUB_SINGLEUSER_APP: "jupyter_server.serverapp.ServerApp" #singleuser: # defaultUrl: "/lab" # extraEnv: # JUPYTERHUB_SINGLEUSER_APP: "notebook.notebookapp.NotebookApp" root@hello:~# root@hello:~# root@hello:~# 第五步、修改svc为nodeportroot@hello:~# kubectl get svc -A NAMESPACE NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE default kubernetes ClusterIP 10.68.0.1 <none> 443/TCP 16h ju hub ClusterIP 10.68.60.16 <none> 8081/TCP 114s ju proxy-api ClusterIP 10.68.239.54 <none> 8001/TCP 114s ju proxy-public LoadBalancer 10.68.62.47 <pending> 80:32070/TCP 114s kube-system dashboard-metrics-scraper ClusterIP 10.68.244.241 <none> 8000/TCP 16h kube-system kube-dns ClusterIP 10.68.0.2 <none> 53/UDP,53/TCP,9153/TCP 16h kube-system kube-dns-upstream ClusterIP 10.68.221.104 <none> 53/UDP,53/TCP 16h kube-system kubernetes-dashboard NodePort 10.68.206.196 <none> 443:32143/TCP 16h kube-system metrics-server ClusterIP 10.68.1.149 <none> 443/TCP 16h kube-system node-local-dns ClusterIP None <none> 9253/TCP 16h root@hello:~# kubectl edit svc proxy-public -n ju service/proxy-public edited root@hello:~# root@hello:~# root@hello:~# kubectl get svc -A NAMESPACE NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE default kubernetes ClusterIP 10.68.0.1 <none> 443/TCP 16h ju hub ClusterIP 10.68.60.16 <none> 8081/TCP 2m19s ju proxy-api ClusterIP 10.68.239.54 <none> 8001/TCP 2m19s ju proxy-public NodePort 10.68.62.47 <none> 80:32070/TCP 2m19s kube-system dashboard-metrics-scraper ClusterIP 10.68.244.241 <none> 8000/TCP 16h kube-system kube-dns ClusterIP 10.68.0.2 <none> 53/UDP,53/TCP,9153/TCP 16h kube-system kube-dns-upstream ClusterIP 10.68.221.104 <none> 53/UDP,53/TCP 16h kube-system kubernetes-dashboard NodePort 10.68.206.196 <none> 443:32143/TCP 16h kube-system metrics-server ClusterIP 10.68.1.149 <none> 443/TCP 16h kube-system node-local-dns ClusterIP None <none> 9253/TCP 16h root@hello:~# https://blog.csdn.net/qq_33921750https://my.oschina.net/u/3981543https://www.zhihu.com/people/chen-bu-yun-2https://segmentfault.com/u/hppyvyv6/articleshttps://juejin.cn/user/3315782802482007https://space.bilibili.com/352476552/articlehttps://cloud.tencent.com/developer/column/93230知乎、CSDN、开源中国、思否、掘金、哔哩哔哩、腾讯云本文使用 文章同步助手 同步
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kubernetes核心实战（五）--- StatefulSets 7、StatefulSetsStatefulSet 是用来管理有状态应用的工作负载 API 对象。StatefulSet 用来管理 Deployment 和扩展一组 Pod，并且能为这些 Pod 提供序号和唯一性保证。和 Deployment 相同的是，StatefulSet 管理了基于相同容器定义的一组 Pod。但和 Deployment 不同的是，StatefulSet 为它们的每个 Pod 维护了一个固定的 ID。这些 Pod 是基于相同的声明来创建的，但是不能相互替换：无论怎么调度，每个 Pod 都有一个永久不变的 ID。StatefulSet 和其他控制器使用相同的工作模式。你在 StatefulSet 对象 中定义你期望的状态，然后 StatefulSet 的 控制器 就会通过各种更新来达到那种你想要的状态。使用 StatefulSetsStatefulSets 对于需要满足以下一个或多个需求的应用程序很有价值：稳定的、唯一的网络标识符。稳定的、持久的存储。有序的、优雅的部署和缩放。有序的、自动的滚动更新。在上面，稳定意味着 Pod 调度或重调度的整个过程是有持久性的。如果应用程序不需要任何稳定的标识符或有序的部署、删除或伸缩，则应该使用由一组无状态的副本控制器提供的工作负载来部署应用程序，比如 Deployment 或者 ReplicaSet 可能更适用于您的无状态应用部署需要。限制给定 Pod 的存储必须由 PersistentVolume 驱动 基于所请求的 storage class 来提供，或者由管理员预先提供。删除或者收缩 StatefulSet 并不会删除它关联的存储卷。这样做是为了保证数据安全，它通常比自动清除 StatefulSet 所有相关的资源更有价值。StatefulSet 当前需要无头服务 来负责 Pod 的网络标识。您需要负责创建此服务。当删除 StatefulSets 时，StatefulSet 不提供任何终止 Pod 的保证。为了实现 StatefulSet 中的 Pod 可以有序和优雅的终止，可以在删除之前将 StatefulSet 缩放为 0。在默认 Pod 管理策略(OrderedReady) 时使用 滚动更新，可能进入需要 人工干预 才能修复的损坏状态。示例：[root@k8s-master-node1 ~/yaml/test]# vim statefulsets.yaml [root@k8s-master-node1 ~/yaml/test]# cat statefulsets.yaml apiVersion: v1 kind: Service metadata: name: nginx labels: app: nginx spec: ports: - port: 80 name: web clusterIP: None selector: app: nginx --- kind: PersistentVolumeClaim apiVersion: v1 metadata: name: nginx-pvc-0 spec: accessModes: - ReadWriteMany resources: requests: storage: 200Mi --- kind: PersistentVolumeClaim apiVersion: v1 metadata: name: nginx-pvc-1 spec: accessModes: - ReadWriteMany resources: requests: storage: 200Mi --- kind: PersistentVolumeClaim apiVersion: v1 metadata: name: nginx-pvc-2 spec: accessModes: - ReadWriteMany resources: requests: storage: 200Mi --- apiVersion: apps/v1 kind: StatefulSet metadata: name: web spec: selector: matchLabels: app: nginx # has to match .spec.template.metadata.labels serviceName: "nginx" replicas: 3 # by default is 1 template: metadata: labels: app: nginx # has to match .spec.selector.matchLabels spec: terminationGracePeriodSeconds: 10 containers: - name: nginx image: nginx ports: - containerPort: 80 name: web volumeMounts: - name: www mountPath: /usr/share/nginx/html volumes: - name: www persistentVolumeClaim: claimName: nginx-pvc-0 volumes: - name: www persistentVolumeClaim: claimName: nginx-pvc-1 volumes: - name: www persistentVolumeClaim: claimName: nginx-pvc-2 [root@k8s-master-node1 ~/yaml/test]#创建statefulsets[root@k8s-master-node1 ~/yaml/test]# kubectl apply -f statefulsets.yaml service/nginx created statefulset.apps/web created [root@k8s-master-node1 ~/yaml/test]#查看pod[root@k8s-master-node1 ~/yaml/test]# kubectl get pod NAME READY STATUS RESTARTS AGE ingress-demo-app-694bf5d965-8rh7f 1/1 Running 0 67m ingress-demo-app-694bf5d965-swkpb 1/1 Running 0 67m nfs-client-provisioner-dc5789f74-5bznq 1/1 Running 0 52m web-0 1/1 Running 0 93s web-1 1/1 Running 0 85s web-2 1/1 Running 0 66s [root@k8s-master-node1 ~/yaml/test]#查看statefulsets[root@k8s-master-node1 ~/yaml/test]# kubectl get statefulsets.apps -o wide NAME READY AGE CONTAINERS IMAGES web 3/3 113s nginx nginx [root@k8s-master-node1 ~/yaml/test]#注意：前提是解决kubernetes动态分配pv，参考文档：https://cloud.tencent.com/developer/article/1902519 https://blog.csdn.net/qq_33921750https://my.oschina.net/u/3981543https://www.zhihu.com/people/chen-bu-yun-2https://segmentfault.com/u/hppyvyv6/articleshttps://juejin.cn/user/3315782802482007https://space.bilibili.com/352476552/articlehttps://cloud.tencent.com/developer/column/93230知乎、CSDN、开源中国、思否、掘金、哔哩哔哩、腾讯云本文使用 文章同步助手 同步
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kubernetes核心实战（二）---Pod+ReplicaSet 3、podPod 是可以在 Kubernetes 中创建和管理的、最小的可部署的计算单元。Pod （就像在鲸鱼荚或者豌豆荚中）是一组（一个或多个） 容器；这些容器共享存储、网络、以及怎样运行这些容器的声明。Pod 中的内容总是并置（colocated）的并且一同调度，在共享的上下文中运行。Pod 所建模的是特定于应用的“逻辑主机”，其中包含一个或多个应用容器， 这些容器是相对紧密的耦合在一起的。在非云环境中，在相同的物理机或虚拟机上运行的应用类似于 在同一逻辑主机上运行的云应用。除了应用容器，Pod 还可以包含在 Pod 启动期间运行的 Init 容器。你也可以在集群中支持临时性容器 的情况外，为调试的目的注入临时性容器。使用 Pod通常你不需要直接创建 Pod，甚至单实例 Pod。相反，你会使用诸如 Deployment 或 Job 这类工作负载资源 来创建 Pod。如果 Pod 需要跟踪状态， 可以考虑 StatefulSet 资源。Kubernetes 集群中的 Pod 主要有两种用法：运行单个容器的 Pod。"每个 Pod 一个容器"模型是最常见的 Kubernetes 用例；在这种情况下，可以将 Pod 看作单个容器的包装器，并且 Kubernetes 直接管理 Pod，而不是容器。运行多个协同工作的容器的 Pod。Pod 可能封装由多个紧密耦合且需要共享资源的共处容器组成的应用程序。这些位于同一位置的容器可能形成单个内聚的服务单元 —— 一个容器将文件从共享卷提供给公众， 而另一个单独的“挂斗”（sidecar）容器则刷新或更新这些文件。Pod 将这些容器和存储资源打包为一个可管理的实体。说明：将多个并置、同管的容器组织到一个 Pod 中是一种相对高级的使用场景。只有在一些场景中，容器之间紧密关联时你才应该使用这种模式。每个 Pod 都旨在运行给定应用程序的单个实例。如果希望横向扩展应用程序（例如，运行多个实例 以提供更多的资源），则应该使用多个 Pod，每个实例使用一个 Pod。在 Kubernetes 中，这通常被称为 副本（Replication）。通常使用一种工作负载资源及其控制器 来创建和管理一组 Pod 副本。Pod 怎样管理多个容器Pod 被设计成支持形成内聚服务单元的多个协作过程（形式为容器）。Pod 中的容器被自动安排到集群中的同一物理机或虚拟机上，并可以一起进行调度。容器之间可以共享资源和依赖、彼此通信、协调何时以及何种方式终止自身。例如，你可能有一个容器，为共享卷中的文件提供 Web 服务器支持，以及一个单独的 “sidecar（挂斗）”容器负责从远端更新这些文件，如下图所示：4、ReplicaSetReplicaSet 的目的是维护一组在任何时候都处于运行状态的 Pod 副本的稳定集合。因此，它通常用来保证给定数量的、完全相同的 Pod 的可用性。ReplicaSet 的工作原理RepicaSet 是通过一组字段来定义的，包括一个用来识别可获得的 Pod 的集合的选择算符，一个用来标明应该维护的副本个数的数值，一个用来指定应该创建新 Pod 以满足副本个数条件时要使用的 Pod 模板等等。每个 ReplicaSet 都通过根据需要创建和 删除 Pod 以使得副本个数达到期望值，进而实现其存在价值。当 ReplicaSet 需要创建 新的 Pod 时，会使用所提供的 Pod 模板。ReplicaSet 通过 Pod 上的 metadata.ownerReferences 字段连接到附属 Pod，该字段给出当前对象的属主资源。ReplicaSet 所获得的 Pod 都在其 ownerReferences 字段中包含了属主 ReplicaSet 的标识信息。正是通过这一连接，ReplicaSet 知道它所维护的 Pod 集合的状态， 并据此计划其操作行为。ReplicaSet 使用其选择算符来辨识要获得的 Pod 集合。如果某个 Pod 没有 OwnerReference 或者其 OwnerReference 不是一个 控制器，且其匹配到 某 ReplicaSet 的选择算符，则该 Pod 立即被此 ReplicaSet 获得。示例：[root@k8s-master-node1 ~/yaml/test]# vim pod.yaml [root@k8s-master-node1 ~/yaml/test]# cat pod.yaml apiVersion: apps/v1kind: ReplicaSetmetadata: name: frontend labels: app: guestbook tier: frontendspec: # modify replicas according to your case replicas: 3 selector: matchLabels: tier: frontend template: metadata: labels: tier: frontend spec: containers: - name: nginx image: nginx[root@k8s-master-node1 ~/yaml/test]#创建[root@k8s-master-node1 ~/yaml/test]# kubectl apply -f pod.yaml replicaset.apps/frontend created查看[root@k8s-master-node1 ~/yaml/test]# kubectl get podNAME READY STATUS RESTARTS AGEfrontend-8zxxw 1/1 Running 0 2m26sfrontend-l22df 1/1 Running 0 2m26sfrontend-qnhkr 1/1 Running 0 2m26s[root@k8s-master-node1 ~/yaml/test]#删除[root@k8s-master-node1 ~/yaml/test]# kubectl delete -f pod.yaml replicaset.apps "frontend" deleted[root@k8s-master-node1 ~/yaml/test]# https://blog.csdn.net/qq_33921750https://my.oschina.net/u/3981543https://www.zhihu.com/people/chen-bu-yun-2https://segmentfault.com/u/hppyvyv6/articleshttps://juejin.cn/user/3315782802482007https://space.bilibili.com/352476552/articlehttps://cloud.tencent.com/developer/column/93230知乎、CSDN、开源中国、思否、掘金、哔哩哔哩、腾讯云本文使用 文章同步助手 同步