【推荐】2019 Java 开发者跳槽指南.pdf(吐血整理) >>> 
简介： google出品在国内都存在墙的问题，而kubeflow作为云原生的机器学习套件对团队的帮助很大，对于无翻墙条件的团队，基于国内镜像搭建kubeflow可以帮助大家解决不少麻烦，这里给大家提供一套基于国内阿里云镜像的kubeflow 0.6的安装方案。
环境准备
kubeflow 为环境要求很高，看官方要求： at least one worker node with a minimum of:
- 4 CPU
- 50 GB storage
- 12 GB memory
当然，没达到也能安装，不过在后面使用中会出现资源问题，因为这是整包安装方案。
一个已经安装好的kubernetes集群，这里我采用的是rancher安装的集群。
sudo docker run -d --restart=unless-stopped -p 80:80 -p 443:443 rancher/rancher
这里我选择的是k8s的1.14版本，kubeflow和k8s之间的版本兼容可以查看官网说明，这里我的kubeflow采用了0.6版本。
如果直接想安装可以直接调到kubeflow一键安装部分
kustomize
下载kustomize文件
官方的教程是用 kfclt 安装的，kfclt 本质上是使用了 kustomize 来安装，因此这里我直接下载 kustomize 文件，通过修改镜像的方式安装。
官方kustomize文件下载地址
git clone https://github.com/kubeflow/manifests
cd manifests
git checkout v0.6-branch
cd <target>/base
kubectl kustomize . | tee <output file>
文件比较多，可以用脚本分别导出，也可以用 kfctl 命令生成kfctl generate all -V:
kustomize/
├── ambassador.yaml
├── api-service.yaml
├── argo.yaml
├── centraldashboard.yaml
├── jupyter-web-app.yaml
├── katib.yaml
├── metacontroller.yaml
├── minio.yaml
├── mysql.yaml
├── notebook-controller.yaml
├── persistent-agent.yaml
├── pipelines-runner.yaml
├── pipelines-ui.yaml
├── pipelines-viewer.yaml
├── pytorch-operator.yaml
├── scheduledworkflow.yaml
├── tensorboard.yaml
└── tf-job-operator.yaml
ambassador 微服务网关
argo 用于任务工作流编排
centraldashboard kubeflow的dashboard看板页面
tf-job-operator 深度学习框架引擎，一个基于tensorflow构建的CRD，资源类型kind为TFJob
katib 超参数服务器
机器学习套件使用流程
修改kustomize文件
修改kustomize镜像
修改镜像：
grc_image = [
"gcr.io/kubeflow-images-public/ingress-setup:latest",
"gcr.io/kubeflow-images-public/admission-webhook:v20190520-v0-139-gcee39dbc-dirty-0d8f4c",
"gcr.io/kubeflow-images-public/kubernetes-sigs/application:1.0-beta",
"gcr.io/kubeflow-images-public/centraldashboard:v20190823-v0.6.0-rc.0-69-gcb7dab59",
"gcr.io/kubeflow-images-public/jupyter-web-app:9419d4d",
"gcr.io/kubeflow-images-public/katib/v1alpha2/katib-controller:v0.6.0-rc.0",
"gcr.io/kubeflow-images-public/katib/v1alpha2/katib-manager:v0.6.0-rc.0",
"gcr.io/kubeflow-images-public/katib/v1alpha2/katib-manager-rest:v0.6.0-rc.0",
"gcr.io/kubeflow-images-public/katib/v1alpha2/suggestion-bayesianoptimization:v0.6.0-rc.0",
"gcr.io/kubeflow-images-public/katib/v1alpha2/suggestion-grid:v0.6.0-rc.0",
"gcr.io/kubeflow-images-public/katib/v1alpha2/suggestion-hyperband:v0.6.0-rc.0",
"gcr.io/kubeflow-images-public/katib/v1alpha2/suggestion-nasrl:v0.6.0-rc.0",
"gcr.io/kubeflow-images-public/katib/v1alpha2/suggestion-random:v0.6.0-rc.0",
"gcr.io/kubeflow-images-public/katib/v1alpha2/katib-ui:v0.6.0-rc.0",
"gcr.io/kubeflow-images-public/metadata:v0.1.8",
"gcr.io/kubeflow-images-public/metadata-frontend:v0.1.8",
"gcr.io/ml-pipeline/api-server:0.1.23",
"gcr.io/ml-pipeline/persistenceagent:0.1.23",
"gcr.io/ml-pipeline/scheduledworkflow:0.1.23",
"gcr.io/ml-pipeline/frontend:0.1.23",
"gcr.io/ml-pipeline/viewer-crd-controller:0.1.23",
"gcr.io/kubeflow-images-public/notebook-controller:v20190603-v0-175-geeca4530-e3b0c4",
"gcr.io/kubeflow-images-public/profile-controller:v20190619-v0-219-gbd3daa8c-dirty-1ced0e",
"gcr.io/kubeflow-images-public/kfam:v20190612-v0-170-ga06cdb79-dirty-a33ee4",
"gcr.io/kubeflow-images-public/pytorch-operator:v1.0.0-rc.0",
"gcr.io/google_containers/spartakus-amd64:v1.1.0",
"gcr.io/kubeflow-images-public/tf_operator:v0.6.0.rc0",
"gcr.io/arrikto/kubeflow/oidc-authservice:v0.2"
]
doc_image = [
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.ingress-setup:latest",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.admission-webhook:v20190520-v0-139-gcee39dbc-dirty-0d8f4c",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.kubernetes-sigs.application:1.0-beta",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.centraldashboard:v20190823-v0.6.0-rc.0-69-gcb7dab59",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.jupyter-web-app:9419d4d",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.katib.v1alpha2.katib-controller:v0.6.0-rc.0",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.katib.v1alpha2.katib-manager:v0.6.0-rc.0",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.katib.v1alpha2.katib-manager-rest:v0.6.0-rc.0",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.katib.v1alpha2.suggestion-bayesianoptimization:v0.6.0-rc.0",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.katib.v1alpha2.suggestion-grid:v0.6.0-rc.0",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.katib.v1alpha2.suggestion-hyperband:v0.6.0-rc.0",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.katib.v1alpha2.suggestion-nasrl:v0.6.0-rc.0",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.katib.v1alpha2.suggestion-random:v0.6.0-rc.0",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.katib.v1alpha2.katib-ui:v0.6.0-rc.0",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.metadata:v0.1.8",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.metadata-frontend:v0.1.8",
"registry.cn-shenzhen.aliyuncs.com/shikanon/ml-pipeline.api-server:0.1.23",
"registry.cn-shenzhen.aliyuncs.com/shikanon/ml-pipeline.persistenceagent:0.1.23",
"registry.cn-shenzhen.aliyuncs.com/shikanon/ml-pipeline.scheduledworkflow:0.1.23",
"registry.cn-shenzhen.aliyuncs.com/shikanon/ml-pipeline.frontend:0.1.23",
"registry.cn-shenzhen.aliyuncs.com/shikanon/ml-pipeline.viewer-crd-controller:0.1.23",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.notebook-controller:v20190603-v0-175-geeca4530-e3b0c4",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.profile-controller:v20190619-v0-219-gbd3daa8c-dirty-1ced0e",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.kfam:v20190612-v0-170-ga06cdb79-dirty-a33ee4",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.pytorch-operator:v1.0.0-rc.0",
"registry.cn-shenzhen.aliyuncs.com/shikanon/google_containers.spartakus-amd64:v1.1.0",
"registry.cn-shenzhen.aliyuncs.com/shikanon/kubeflow-images-public.tf_operator:v0.6.0.rc0",
"registry.cn-shenzhen.aliyuncs.com/shikanon/arrikto.kubeflow.oidc-authservice:v0.2"
]
修改PVC，使用动态存储
修改pvc存储，采用local-path-provisioner动态分配PV。
安装local-path-provisioner：
kubectl apply -f https://raw.githubusercontent.com/rancher/local-path-provisioner/master/deploy/local-path-storage.yaml
如果想直接在kubeflow中使用，还需要将StorageClass改为默认存储:
...
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
name: local-path
annotations: #添加为默认StorageClass
storageclass.beta.kubernetes.io/is-default-class: "true"
provisioner: rancher.io/local-path
volumeBindingMode: WaitForFirstConsumer
reclaimPolicy: Delete
...
完成后可以建一个PVC试试：
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: local-path-pvc
namespace: default
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 2Gi
注：如果没有设为默认storageclass需要在PVC加入storageClassName: local-path进行绑定
一键安装
这里我制作了一个一键启动的国内镜像版kubeflow项目： https://github.com/shikanon/kubeflow-manifests
中间踩过的坑
Coredns CrashLoopBackOff 问题
log日志：
kubectl -n kube-system logs coredns-6998d84bf5-r4dbk
E1028 06:36:35.489403 1 reflector.go:134] github.com/coredns/coredns/plugin/kubernetes/controller.go:322: Failed to list *v1.Namespace: Get https://10.96.0.1:443/api/v1/namespaces?limit=500&resourceVersion=0: dial tcp 10.96.0.1:443: connect: no route to host
E1028 06:36:35.489403 1 reflector.go:134] github.com/coredns/coredns/plugin/kubernetes/controller.go:322: Failed to list *v1.Namespace: Get https://10.96.0.1:443/api/v1/namespaces?limit=500&resourceVersion=0: dial tcp 10.96.0.1:443: connect: no route to host
log: exiting because of error: log: cannot create log: open /tmp/coredns.coredns-8686dcc4fd-7fwcz.unknownuser.log.ERROR.20191028-063635.1: no such file or directory
防火墙（iptables）规则错乱或者缓存导致的,解决方案：
iptables --flush
iptables -tnat --flush
该操作会丢失防火墙规则
来源：oschina
链接：https://my.oschina.net/Kanonpy/blog/3147705