离线混合部署，tf-operator, tensorflow部署，Prometheus+grafana监控

部署环境及要求

阿里云k8s集群
k8s version ：1.14.6
工作节点配置：
4核 16G Memory 50G HD

由于配置要求相对较高，因此推荐使用阿里云ECS或直接使用集群来部署，避免资源不足的情况。

具体部署

1、K8s搭建完毕（docker等）

2、kubeflow部署方法1：

2.1 下载kfctl包与源码包

下载源码包

//任一
wget https://github.com/kubeflow/manifests/archive/v1.0.2.tar.gz
wget https://github.com/kubeflow/kfctl/archive/v1.0.2.tar.gz

作者在这一步花费了很多的时间，因为GitHub中的fkctl包下载速度非常缓慢。
你可以考虑直接使用wegt在云服务器上进行操作，从github中拉取，但作者本人用这种方式拉取的包会存在缺失不完整的情况。

可能的错误1）** 导致在执行kfctl命令时报错：Segmentation fault

当然，这种错误也可能是由于硬盘没有达到规定要求所导致。

因此最终采用的方法是，先直接从github上下载（更佳，下载速度更快），在从本地上传至阿里云服务器对应目录。

基于winscp传输

利用winscp在安装kubeflow的过程中发挥了重大的作用，

3、下载yaml文件并修改

wget https://raw.githubusercontent.com/kubeflow/manifests/v1.0-branch/kfdef/kfctl_k8s_istio.v1.0.2.yaml
//修改 kfctl_k8s_istio.v1.0.2.yaml 内容 
将 https://github.com/kubeflow/manifests/archive/v1.0.2.tar.gz 改为 file:///root/kubeflow/v1.0.2.tar.gz

2.2 apply yaml文件

tar -xvf kfctl_v1.0.2_linux.tar.gz
export PATH=$PATH:""
export KF_NAME=
export BASE_DIR=
export KF_DIR=${BASE_DIR}/${KF_NAME}
export CONFIG_URI="https://raw.githubusercontent.com/kubeflow/manifests/v1.0-branch/kfdef/kfctl_k8s_istio.v1.0.2.yaml"
mkdir -p ${KF_DIR}
cd ${KF_DIR}
kfctl apply -V -f ${CONFIG_URI}

2.3 创建pv pvc

在法二中详述

2.4 阿里云构建拉取所需要的镜像

github链接：
link.
克隆之后，在阿里云镜像仓库中构建，再从阿里云拉取，可以避免被墙的问题

gcr.io/kubeflow-images-public/ingress-setup:latest
gcr.io/kubeflow-images-public/admission-webhook:v1.0.0-gaf96e4e3
gcr.io/kubeflow-images-public/kubernetes-sigs/application:1.0-beta
argoproj/argoui:v2.3.0
gcr.io/kubeflow-images-public/centraldashboard:v1.0.0-g3ec0de71
gcr.io/kubeflow-images-public/jupyter-web-app:v1.0.0-g2bd63238
gcr.io/kubeflow-images-public/katib/v1alpha3/katib-controller:v0.8.0
gcr.io/kubeflow-images-public/katib/v1alpha3/katib-db-manager:v0.8.0
mysql:8
gcr.io/kubeflow-images-public/katib/v1alpha3/katib-ui:v0.8.0
gcr.io/kubebuilder/kube-rbac-proxy:v0.4.0
gcr.io/kfserving/kfserving-controller:0.2.2
metacontroller/metacontroller:v0.3.0
mysql:8.0.3
gcr.io/kubeflow-images-public/metadata:v0.1.11
gcr.io/ml-pipeline/envoy:metadata-grpc
gcr.io/tfx-oss-public/ml_metadata_store_server:v0.21.1
gcr.io/kubeflow-images-public/metadata-frontend:v0.1.8
minio/minio:RELEASE.2018-02-09T22-40-05Z
gcr.io/ml-pipeline/api-server:0.2.5
gcr.io/ml-pipeline/visualization-server:0.2.5
gcr.io/ml-pipeline/persistenceagent:0.2.5
gcr.io/ml-pipeline/scheduledworkflow:0.2.5
gcr.io/ml-pipeline/frontend:0.2.5
gcr.io/ml-pipeline/viewer-crd-controller:0.2.5
mysql:5.6
gcr.io/kubeflow-images-public/notebook-controller:v1.0.0-gcd65ce25
gcr.io/kubeflow-images-public/profile-controller:v1.0.0-ge50a8531
gcr.io/kubeflow-images-public/kfam:v1.0.0-gf3e09203
gcr.io/kubeflow-images-public/pytorch-operator:v1.0.0-g047cf0f
docker.io/seldonio/seldon-core-operator:1.0.1
gcr.io/spark-operator/spark-operator:v1beta2-1.0.0-2.4.4
gcr.io/spark-operator/spark-operator:v1beta2-1.0.0-2.4.4
gcr.io/spark-operator/spark-operator:v1beta2-1.0.0-2.4.4
gcr.io/google_containers/spartakus-amd64:v1.1.0
tensorflow/tensorflow:1.8.0
gcr.io/kubeflow-images-public/tf_operator:v1.0.0-g92389064
argoproj/workflow-controller:v2.3.0

2.5 修改各个deploy statefulset 的镜像下载策略

下载策略为Always ，需要修改为（imagePullPolicy后面值改为 IfNotPresent）
例如：
#kubectl edit deploy deploy名字 -n kubeflow

部署成功

博主本人在使用上述官方方法部署时遭遇了诸多问题，更推荐下列方法：

3、kubeflow部署方法2

3.1 kustomize

在方法一中利用kfctl安装，但本质上是使用kustomize安装

git clone https://github.com/kubeflow/manifests
cd manifests
git checkout v0.6-branch
cd /base
kubectl kustomize . | tee 

3.2 修改kustomize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"
]

3.3 修改PVC，使用动态存储

采用local-path-provisioner动态分配PV
否则：

可能的错误2）

调用StorageClass的几个pod例如mysql始终处于pending状态，无法正常运行。

解决方法：
安装local-path-provisioner：

kubectl apply -f https://raw.githubusercontent.com/rancher/local-path-provisioner/master/deploy/local-path-storage.yaml

kubectl apply -f local-path-storage.yaml

apiVersion: v1
kind: Namespace
metadata:
  name: local-path-storage
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: local-path-provisioner-service-account
  namespace: local-path-storage
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: local-path-provisioner-role
rules:
- apiGroups: [""]
  resources: ["nodes", "persistentvolumeclaims"]
  verbs: ["get", "list", "watch"]
- apiGroups: [""]
  resources: ["endpoints", "persistentvolumes", "pods"]
  verbs: ["*"]
- apiGroups: [""]
  resources: ["events"]
  verbs: ["create", "patch"]
- apiGroups: ["storage.k8s.io"]
  resources: ["storageclasses"]
  verbs: ["get", "list", "watch"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: local-path-provisioner-bind
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: local-path-provisioner-role
subjects:
- kind: ServiceAccount
  name: local-path-provisioner-service-account
  namespace: local-path-storage
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: local-path-provisioner
  namespace: local-path-storage
spec:
  replicas: 1
  selector:
    matchLabels:
      app: local-path-provisioner
  template:
    metadata:
      labels:
        app: local-path-provisioner
    spec:
      serviceAccountName: local-path-provisioner-service-account
      containers:
      - name: local-path-provisioner
        image: rancher/local-path-provisioner:v0.0.11
        imagePullPolicy: IfNotPresent
        command:
        - local-path-provisioner
        - --debug
        - start
        - --config
        - /etc/config/config.json
        volumeMounts:
        - name: config-volume
          mountPath: /etc/config/
        env:
        - name: POD_NAMESPACE
          valueFrom:
            fieldRef:
              fieldPath: metadata.namespace
      volumes:
        - name: config-volume
          configMap:
            name: local-path-config
---
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
---
kind: ConfigMap
apiVersion: v1
metadata:
  name: local-path-config
  namespace: local-path-storage
data:
  config.json: |-
        {
                "nodePathMap":[
                {
                        "node":"DEFAULT_PATH_FOR_NON_LISTED_NODES",
                        "paths":["/opt/local-path-provisioner"]
                }
                ]
        }

可能的错误3）

存在crash的问题，原因：防火墙（iptables）规则错乱或者缓存导致
解决方案：

iptables --flush
iptables -tnat --flush

3.4 apply yaml文件

先创建namespace
namespce.yaml

apiVersion: v1
kind: Namespace
metadata:
  name: kubeflow

内容较多，不做赘述，到此步可以采用别人集成好的
一键安装: link.

注意：针对于防火墙和动态存储的处理仍然必要！且应该先于apply yaml的过程，即链接中的python文件

可能的错误4）

可能有个别pod会一直处于pending状态，查看日志发现是资源不足的问题
增加节点服务器
因此再次推荐阿里云ECS或直接使用集群

成功：

成功的pvc和pv
kubectl get pv
kubectl get pvc

成功的kubeflow部署(节选）
kubectl get pods -n kubeflow

TensorFlow部署

TensorFlow 是由 Google Brain 团队为深度神经网络（DNN）开发的功能强大的开源软件库。
允许将深度神经网络的计算部署到任意数量的 CPU 或 GPU 的服务器、PC 或移动设备上，且只利用一个 TensorFlow API。

TensorFlow 则还有更多的特点：
1、支持所有流行语言，如 Python、C++、Java、R和Go
2、可以在多种平台上工作，甚至是移动平台和分布式平台
3、它受到所有云服务（AWS、Google和Azure）的支持
4、Keras——高级神经网络 API，已经与 TensorFlow 整合
5、与 Torch/Theano 比较，TensorFlow 拥有更好的计算图表可视化
6、允许模型部署到工业生产中，并且容易使用
7、有良好的社区支持
8、TensorFlow 不仅仅是一个软件库，它是一套包括 TensorFlow，TensorBoard 和 TensorServing 的软件

kubeflow通过几个核心组件帮助简化tensorflow的部署：
1、TF operator
tf-operator是Kubeflow的第一个CRD实现，解决的是TensorFlow模型训练的问题，它提供了广泛的灵活性和可配置，可以与阿里云上的NAS，OSS无缝集成，并且提供了简单的UI查看训练的历史记录。

2、TF job
一个新的资源类型，无需编写复杂的配置，只需要关注数据的输入，代码的运行和日志的输入输出。
TFJob 对象

属性	类型	描述
apiVersion	string	api版本，目前为 kubeflow.org/v1alpha1
kind	string	REST资源的类型. 这里是TFJob
metadata	ObjectMeta	标准元数据定义
spec	TFJobSpec	TensorFlow job的定义

3、TF hub
是一个用于促进机器学习模型中可复用部分再次进行探索与发布的库，主要将预训练过的TensorFlow模型片段再次利用到新的任务上

离线：

离线任务不需要对任务进行快速响应，但是计算量相对较大、占用资源多，在学习中我们可以直接认为运行一个pod

在线

采用分布式Tensorflow（多GPU）的方式
参考内容：
链接1: link.
链接2: link.

prometheus+grafana监控资源

参考：
链接: link.
prometheus的架构图：

1 部署node-exporter组件

apiVersion: extensions/v1beta1
kind: DaemonSet
metadata:
  name: node-exporter
  namespace: kube-system
  labels:
    k8s-app: node-exporter
spec:
  template:
    metadata:
      labels:
        k8s-app: node-exporter
    spec:
      containers:
      - image: prom/node-exporter
        name: node-exporter
        ports:
        - containerPort: 9100
          protocol: TCP
          name: http
---
apiVersion: v1
kind: Service
metadata:
  labels:
    k8s-app: node-exporter
  name: node-exporter
  namespace: kube-system
spec:
  ports:
  - name: http
    port: 9100
    nodePort: 31672
    protocol: TCP
  type: NodePort
  selector:
    k8s-app: node-exporter

2 rbac.yaml

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: prometheus
rules:
- apiGroups: [""]
  resources:
  - nodes
  - nodes/proxy
  - services
  - endpoints
  - pods
  verbs: ["get", "list", "watch"]
- apiGroups:
  - extensions
  resources:
  - ingresses
  verbs: ["get", "list", "watch"]
- nonResourceURLs: ["/metrics"]
  verbs: ["get"]
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: prometheus
  namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: prometheus
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: prometheus
subjects:
- kind: ServiceAccount
  name: prometheus
  namespace: kube-system

3 configmap.yaml

apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
  namespace: kube-system
data:
  prometheus.yml: |
    global:
      scrape_interval:     15s
      evaluation_interval: 15s
    scrape_configs:

    - job_name: 'kubernetes-apiservers'
      kubernetes_sd_configs:
      - role: endpoints
      scheme: https
      tls_config:
        ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
      bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
      relabel_configs:
      - source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name]
        action: keep
        regex: default;kubernetes;https

    - job_name: 'kubernetes-nodes'
      kubernetes_sd_configs:
      - role: node
      scheme: https
      tls_config:
        ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
      bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
      relabel_configs:
      - action: labelmap
        regex: __meta_kubernetes_node_label_(.+)
      - target_label: __address__
        replacement: kubernetes.default.svc:443
      - source_labels: [__meta_kubernetes_node_name]
        regex: (.+)
        target_label: __metrics_path__
        replacement: /api/v1/nodes/${1}/proxy/metrics

    - job_name: 'kubernetes-cadvisor'
      kubernetes_sd_configs:
      - role: node
      scheme: https
      tls_config:
        ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
      bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
      relabel_configs:
      - action: labelmap
        regex: __meta_kubernetes_node_label_(.+)
      - target_label: __address__
        replacement: kubernetes.default.svc:443
      - source_labels: [__meta_kubernetes_node_name]
        regex: (.+)
        target_label: __metrics_path__
        replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor

    - job_name: 'kubernetes-service-endpoints'
      kubernetes_sd_configs:
      - role: endpoints
      relabel_configs:
      - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]
        action: keep
        regex: true
      - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]
        action: replace
        target_label: __scheme__
        regex: (https?)
      - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]
        action: replace
        target_label: __metrics_path__
        regex: (.+)
      - source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port]
        action: replace
        target_label: __address__
        regex: ([^:]+)(?::\d+)?;(\d+)
        replacement: $1:$2
      - action: labelmap
        regex: __meta_kubernetes_service_label_(.+)
      - source_labels: [__meta_kubernetes_namespace]
        action: replace
        target_label: kubernetes_namespace
      - source_labels: [__meta_kubernetes_service_name]
        action: replace
        target_label: kubernetes_name

    - job_name: 'kubernetes-services'
      kubernetes_sd_configs:
      - role: service
      metrics_path: /probe
      params:
        module: [http_2xx]
      relabel_configs:
      - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_probe]
        action: keep
        regex: true
      - source_labels: [__address__]
        target_label: __param_target
      - target_label: __address__
        replacement: blackbox-exporter.example.com:9115
      - source_labels: [__param_target]
        target_label: instance
      - action: labelmap
        regex: __meta_kubernetes_service_label_(.+)
      - source_labels: [__meta_kubernetes_namespace]
        target_label: kubernetes_namespace
      - source_labels: [__meta_kubernetes_service_name]
        target_label: kubernetes_name

    - job_name: 'kubernetes-ingresses'
      kubernetes_sd_configs:
      - role: ingress
      relabel_configs:
      - source_labels: [__meta_kubernetes_ingress_annotation_prometheus_io_probe]
        action: keep
        regex: true
      - source_labels: [__meta_kubernetes_ingress_scheme,__address__,__meta_kubernetes_ingress_path]
        regex: (.+);(.+);(.+)
        replacement: ${1}://${2}${3}
        target_label: __param_target
      - target_label: __address__
        replacement: blackbox-exporter.example.com:9115
      - source_labels: [__param_target]
        target_label: instance
      - action: labelmap
        regex: __meta_kubernetes_ingress_label_(.+)
      - source_labels: [__meta_kubernetes_namespace]
        target_label: kubernetes_namespace
      - source_labels: [__meta_kubernetes_ingress_name]
        target_label: kubernetes_name

    - job_name: 'kubernetes-pods'
      kubernetes_sd_configs:
      - role: pod
      relabel_configs:
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
        action: keep
        regex: true
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
        action: replace
        target_label: __metrics_path__
        regex: (.+)
      - source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
        action: replace
        regex: ([^:]+)(?::\d+)?;(\d+)
        replacement: $1:$2
        target_label: __address__
      - action: labelmap
        regex: __meta_kubernetes_pod_label_(.+)
      - source_labels: [__meta_kubernetes_namespace]
        action: replace
        target_label: kubernetes_namespace
      - source_labels: [__meta_kubernetes_pod_name]
        action: replace
        target_label: kubernetes_pod_name

4 Prometheus deployment

yaml文件：

apiVersion: apps/v1beta2
kind: Deployment
metadata:
  labels:
    name: prometheus-deployment
  name: prometheus
  namespace: kube-system
spec:
  replicas: 1
  selector:
    matchLabels:
      app: prometheus
  template:
    metadata:
      labels:
        app: prometheus
    spec:
      containers:
      - image: prom/prometheus:v2.0.0
        name: prometheus
        command:
        - "/bin/prometheus"
        args:
        - "--config.file=/etc/prometheus/prometheus.yml"
        - "--storage.tsdb.path=/prometheus"
        - "--storage.tsdb.retention=24h"
        ports:
        - containerPort: 9090
          protocol: TCP
        volumeMounts:
        - mountPath: "/prometheus"
          name: data
        - mountPath: "/etc/prometheus"
          name: config-volume
        resources:
          requests:
            cpu: 100m
            memory: 100Mi
          limits:
            cpu: 500m
            memory: 2500Mi
      serviceAccountName: prometheus    
      volumes:
      - name: data
        emptyDir: {}
      - name: config-volume
        configMap:
          name: prometheus-config      
---
kind: Service
apiVersion: v1
metadata:
  labels:
    app: prometheus
  name: prometheus
  namespace: kube-system
spec:
  type: NodePort
  ports:
  - port: 9090
    targetPort: 9090
    nodePort: 30003
  selector:
    app: prometheus

5 apply

kubectl create -f node-exporter.yaml
kubectl create -f rbac-setup.yaml
kubectl create -f configmap.yaml
kubectl create -f prometheus.yaml

6 查看相关pod

kubectl get pods -n kube-system
kubectl get svc -n kube-system

7 查看metrics

http://localhost:31672/metrics
利用阿里云则选用公网ip

8 查看targets

http://localhost:30003/targets

如果你采用阿里云ECS进行部署，你会发现这与ECS自带的Prometheus监控界面是相同的。
你可以通过上栏中的graph等功能实现更加具象的监控。

grafana

1 grafana deployment

apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  name: grafana-core
  namespace: kube-system
  labels:
    app: grafana
    component: core
spec:
  replicas: 1
  template:
    metadata:
      labels:
        app: grafana
        component: core
    spec:
      containers:
      - image: grafana/grafana:5.0.0
        name: grafana-core
        imagePullPolicy: IfNotPresent
        resources:
          limits:
            cpu: 100m
            memory: 100Mi
          requests:
            cpu: 100m
            memory: 100Mi
        env:
          - name: GF_AUTH_BASIC_ENABLED
            value: "true"
          - name: GF_AUTH_ANONYMOUS_ENABLED
            value: "false"
        readinessProbe:
          httpGet:
            path: /login
            port: 3000
        volumeMounts:
        - name: grafana-persistent-storage
          mountPath: /var
      volumes:
      - name: grafana-persistent-storage
        emptyDir: {}

---
apiVersion: v1
kind: Service
metadata:
  name: grafana
  namespace: kube-system
  labels:
    app: grafana
    component: core
spec:
  type: NodePort
  ports:
    - port: 3000
      nodePort: 31000
  selector:
    app: grafana

2 apply

kubectl create -f grafana.yaml

3 查看grafana pod和service

kubectl get pod -n kube-system
kubectl get svc -n kube-system

4 访问

http://localhost:31000
//初始用户名： admin
//初始密码： admin

我们采用在database设置中采用Prometheus的格式，并设置相应的url
grafna相对于Prometheus更加多样化，因此也具有较多的模板，在学习过程中发现编号为315的模板比较通用，可以直接在import中导入即可进行全面的管理。

例如不同pod的cpu use都在此处呈现：