【k8s完整实战教程6】完整实践-部署一个federated_download项目

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1. 【k8s完整实战教程0】前言
2. 【k8s完整实战教程1】源码管理-Coding
3. 【k8s完整实战教程2】腾讯云搭建k8s托管集群
4. 【k8s完整实战教程3】k8s集群部署kubesphere
5. 【k8s完整实战教程4】使用kubesphere部署项目到k8s
6. 【k8s完整实战教程5】网络服务配置（nodeport/loadbalancer/ingress）
7. 【k8s完整实战教程6】完整实践-部署一个federated_download项目

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1 Coding代码仓库开发源码

2 本地测试

2.1 git拉取到本地仓库

17211@hqc MINGW64 ~
$ cd /d/research/git_repository/

17211@hqc MINGW64 /d/research/git_repository (master)
$ ls
Federated/  federated_with_flask/  hello_world/

17211@hqc MINGW64 /d/research/git_repository (master)
$ git clone https://e.coding.net/hqc12/hqc/federated_download.git
Cloning into 'federated_download'...
remote: Enumerating objects: 31, done.
remote: Counting objects: 100% (31/31), done.
remote: Compressing objects: 100% (22/22), done.
remote: Total 31 (delta 5), reused 0 (delta 0), pack-reused 0
Receiving objects: 100% (31/31), 12.44 KiB | 849.00 KiB/s, done.
Resolving deltas: 100% (5/5), done.

17211@hqc MINGW64 /d/research/git_repository (master)
$ ls
Federated/  federated_download/  federated_with_flask/  hello_world/

17211@hqc MINGW64 /d/research/git_repository (master)
$ cd federated_download/

17211@hqc MINGW64 /d/research/git_repository/federated_download (master)
$ ls
Dockerfile  README.md  main.py  requirement.txt  static/  templates/

2.2 本地运行测试

打开pycharm，运行测试

出错了，查找资料好像说是conda环境中存在两个该.dll文件造成的，运行在docker中应该不会出现这个问题。
为了进行本地测试，还是在本地解决一下，代码加上：

import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"

再次运行测试：

2.3 访问

两个网址都可以进行访问
点击即可下载

成功！！！

2.4 有个小问题

好像没法指定下载到设置的downloads文件夹中，就是直接浏览器下载。因此这个文件夹可以不要了。

3 制作成容器镜像

3.1 创建制品仓库

直接使用现有仓库

3.2 构建计划

采用之前的构建计划，更换一下代码仓库即可
另外，为提升二次构建镜像的速度，设置缓存目录

缓存目录设置参考：https://coding.net/help/docs/ci/configuration/cache.html
由于使用的是pip下载依赖，因此设置对应的缓存目录

3.3 立即构建

构建时 Dockerfile中COPY源码进容器这一步骤 出现问题，多方尝试之后，下面这样是ok的。

FROM python:3.7
WORKDIR . # 指定容器工作目录
COPY Dockerfile main.py requirement.txt ./ 
COPY /templates/download.html ./templates/download.html
RUN pip install -r requirement.txt
EXPOSE 5000
RUN /bin/bash -c 'echo init ok'
CMD ["python", "main.py"]

注意：

1. 目标路径必须是一个目录，并以/ 结尾
2. 不能直接复制文件夹，它默认会只复制文件夹中的文件，破坏文件结构
   构建成功：
   

3.4 镜像仓库查看

成功！

4 搭建k8s集群

按之前的记录搭建即可

5 docker镜像测试

5.1 本地拉取镜像

# 登录
ubuntu@VM-1-15-ubuntu:~$ sudo docker login -u federated_project-1667453994942 -p 55a845e185c9bda4ba21fd2227a7538b55717b00 hqc12-docker.pkg.coding.net
WARNING! Using --password via the CLI is insecure. Use --password-stdin.
WARNING! Your password will be stored unencrypted in /home/ubuntu/.docker/config.json.
Configure a credential helper to remove this warning. See
https://docs.docker.com/engine/reference/commandline/login/#credentials-store

Login Succeeded
# 拉取
ubuntu@VM-1-15-ubuntu:~$ sudo docker pull hqc12-docker.pkg.coding.net/hqc/federated_project/federated-download:v0.0.1
v0.0.1: Pulling from hqc/federated_project/federated-download
17c9e6141fdb: Pull complete 
de4a4c6caea8: Pull complete 
4edced8587e6: Pull complete 
a7969cffbf46: Pull complete 
74fbfde6af91: Pull complete 
16fe51aed899: Pull complete 
a418194ab798: Pull complete 
e1b9101d5fa4: Pull complete 
c0b070a4672c: Pull complete 
7094a060c489: Pull complete 
6927575c3e2a: Pull complete 
be9ca32391c3: Pull complete 
aa5d393447e7: Pull complete 
Digest: sha256:4a2cb2995c0a9e10e2bef840a39e96843c043d50256c70d95bd7fc2f2a0362fe
Status: Downloaded newer image for hqc12-docker.pkg.coding.net/hqc/federated_project/federated-download:v0.0.1
hqc12-docker.pkg.coding.net/hqc/federated_project/federated-download:v0.0.1

5.2 本地运行镜像

# 查看当前镜像
ubuntu@VM-1-15-ubuntu:~$ sudo docker images
REPOSITORY                                                             TAG                 IMAGE ID            CREATED             SIZE
hqc12-docker.pkg.coding.net/hqc/federated_project/federated-download   v0.0.1              135908d3f14b        50 minutes ago      3.32GB
...

# 打新的标签
ubuntu@VM-1-15-ubuntu:~$ sudo docker tag 135908d3f14b federated-download:v0.0.1
...

# 删除之前的镜像
ubuntu@VM-1-15-ubuntu:~$ sudo docker rmi hqc12-docker.pkg.coding.net/hqc/federated_project/federated-download:v0.0.1
Untagged: hqc12-docker.pkg.coding.net/hqc/federated_project/federated-download:v0.0.1
Untagged: hqc12-docker.pkg.coding.net/hqc/federated_project/federated-download@sha256:4a2cb2995c0a9e10e2bef840a39e96843c043d50256c70d95bd7fc2f2a0362fe

# 再次查看
ubuntu@VM-1-15-ubuntu:~$ sudo docker images
REPOSITORY                                                   TAG                 IMAGE ID            CREATED             SIZE
federated-download                                           v0.0.1              135908d3f14b        51 minutes ago      3.32GB
...

5.3 本地直接运行测试镜像

# 运行
ubuntu@VM-1-15-ubuntu:~$ sudo docker run federated-download:v0.0.1
2022-11-03 05:56:57.531568: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2022-11-03 05:56:57.531673: E tensorflow/stream_executor/cuda/cuda_driver.cc:313] failed call to cuInit: UNKNOWN ERROR (303)
2022-11-03 05:56:57.531708: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (ac30a6317b98): /proc/driver/nvidia/version does not exist
2022-11-03 05:56:57.532244: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2022-11-03 05:56:57.559485: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2494085000 Hz
2022-11-03 05:56:57.559964: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb718000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2022-11-03 05:56:57.560008: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 1s 0us/step
ubuntu@VM-1-15-ubuntu:~$

没法持续运行。

5.4 创建deployment部署上k8s测试

# 创建deployment的yaml文件
ubuntu@VM-1-15-ubuntu:~$ vim federated-dp.yaml
# 创建service的yaml文件
ubuntu@VM-1-15-ubuntu:~$ vim federated-svc.yaml

ubuntu@VM-1-15-ubuntu:~$ ls
federated-dp.yaml  federated-svc.yaml
# 部署deployment
ubuntu@VM-1-15-ubuntu:~$ kubectl apply -f federated-dp.yaml 
deployment.apps/federated-deployment created
# 查看，暂时正常
ubuntu@VM-1-15-ubuntu:~$ kubectl get all
NAME                                        READY   STATUS    RESTARTS   AGE
pod/federated-deployment-54f5db67fd-g8p9j   1/1     Running   0          6s
pod/federated-deployment-54f5db67fd-tdwdl   1/1     Running   0          6s

NAME                                   READY   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/federated-deployment   2/2     2            2           6s

NAME                                              DESIRED   CURRENT   READY   AGE
replicaset.apps/federated-deployment-54f5db67fd   2         2         2       6s
# 部署service
ubuntu@VM-1-15-ubuntu:~$ kubectl apply -f federated-svc.yaml 
service/federated-service created

# 查看，暂时也正常
ubuntu@VM-1-15-ubuntu:~$ kubectl get all
NAME                                        READY   STATUS    RESTARTS   AGE
pod/federated-deployment-54f5db67fd-g8p9j   1/1     Running   0          35s
pod/federated-deployment-54f5db67fd-tdwdl   1/1     Running   0          35s

NAME                        TYPE        CLUSTER-IP       EXTERNAL-IP   PORT(S)        AGE
service/federated-service   NodePort    172.16.253.172   <none>        80:30000/TCP   3s
service/kubernetes          ClusterIP   172.16.252.1     <none>        443/TCP        37m

NAME                                   READY   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/federated-deployment   2/2     2            2           36s

NAME                                              DESIRED   CURRENT   READY   AGE
replicaset.apps/federated-deployment-54f5db67fd   2         2         2       36s
# 过一会儿查看，出错
ubuntu@VM-1-15-ubuntu:~$ kubectl get all
NAME                                        READY   STATUS             RESTARTS   AGE
pod/federated-deployment-54f5db67fd-2hn6s   0/1     Evicted            0          33s
pod/federated-deployment-54f5db67fd-ddxts   0/1     Evicted            0          33s
pod/federated-deployment-54f5db67fd-dn9dn   0/1     Evicted            0          33s
pod/federated-deployment-54f5db67fd-g8p9j   0/1     Evicted            0          113s
pod/federated-deployment-54f5db67fd-jkrfn   0/1     Evicted            0          33s
pod/federated-deployment-54f5db67fd-jwfqp   0/1     Evicted            0          33s
pod/federated-deployment-54f5db67fd-mjxk8   0/1     Evicted            0          33s
pod/federated-deployment-54f5db67fd-mm8b6   0/1     Evicted            0          32s
pod/federated-deployment-54f5db67fd-tdwdl   1/1     Running            0          113s
pod/federated-deployment-54f5db67fd-wh6qk   0/1     ImagePullBackOff   0          32s
pod/federated-deployment-54f5db67fd-xdktv   0/1     Evicted            0          33s

NAME                        TYPE        CLUSTER-IP       EXTERNAL-IP   PORT(S)        AGE
service/federated-service   NodePort    172.16.253.172   <none>        80:30000/TCP   80s
service/kubernetes          ClusterIP   172.16.252.1     <none>        443/TCP        39m

NAME                                   READY   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/federated-deployment   1/2     2            1           113s

NAME                                              DESIRED   CURRENT   READY   AGE
replicaset.apps/federated-deployment-54f5db67fd   2         2         1       113s

失败

6 搭建kubesphere

6.1 安装

1 安装
kubectl apply -f https://github.com/kubesphere/ks-installer/releases/download/v3.3.0/kubesphere-installer.yaml
2 下载配置文件
wget https://github.com/kubesphere/ks-installer/releases/download/v3.3.0/cluster-configuration.yaml
3 更改配置文件
vim cluster-configuration.yaml 
4 部署配置文件
kubectl apply -f cluster-configuration.yaml
5 查看部署过程
kubectl logs -n kubesphere-system $(kubectl get pod -n kubesphere-system -l 'app in (ks-install, ks-installer)' -o jsonpath='{.items[0].metadata.name}') -f

6.2 查看

安装完成，虽然报错但可以正常登录（是因为服务器资源不足的原因，过一会会变好）

6.3 登录

7 用kubesphere部署

7.1 关联coding制品仓库

1. 获取coding制品仓库的配置令牌
2. kubesphere中创建秘钥
   配置-保密字典-创建
   

7.2 创建工作负载-部署（deployment）

应用负载-工作负载-创建

成功！

8 创建服务来访问应用（NodePort方式）

8.1 kubesphere创建service

kubesphere-应用负载-服务-创建

8.2 访问

失败！

9 更改源码

9.1 更改源码依赖的版本

python改为3.9版本

9.2 构建计划制作镜像推送到制品仓库

10 kubesphere重新创建dp和svc

11 访问

可以访问，并且可以正常下载。

至此，实践成功！

源码由已经开源至我的github，若对您有所帮助还望不吝啬您的star，开放交流才能进步！