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Monitor GPU Metrics of a Container Service Kubernetes Cluster

This article describes how to rapidly construct a GPU monitoring solution based on Prometheus and Grafana on Alibaba Cloud Container Service for Kubernetes.

Whenever you are training artificial intelligence (AI) models using an Alibaba Cloud Container Service for Kubernetes cluster constructed based on GPU ECS hosts, you need to know the GPU status of each pod. For example, you may need to know the video memory usage, GPU usage, and GPU temperature to ensure the stability of services. This document describes how to rapidly construct a GPU monitoring solution based on Prometheus and Grafana on Alibaba Cloud.

What Is Prometheus?

Prometheus is an open-source service monitoring system and a time series database. Since its inception in 2012 and open source placement on GitHub in 2015, Prometheus has attracted many companies and organizations. Prometheus joined the Cloud Native Computing Foundation (CNCF) in 2016 as the second hosted project, after Kubernetes. It graduated from the CNCF in August, 2018.

As a next-generation open-source solution, Prometheus has a lot of O&M ideas that happen to coincide with those of Google SRE.

1

Set Up Container Service for Kubernetes

Prerequisites: You have created a Kubernetes cluster consisting of GPU ECS hosts through Container Service.

Log on to the Container Service console and select Container Service - Kubernetes. Choose Application > Deployment and click Create by Template.

2

Select your GPU cluster and namespace. (For example, you can select the kube-system namespace.) Fill the YAML configuration template to deploy Prometheus and GPU-Exporter.

Deploy Prometheus

apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-env
data:
  storage-retention: 360h
---

apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRole
metadata:
  name: prometheus
rules:
  - apiGroups: ["", "extensions", "apps"]
    resources:
    - nodes
    - nodes/proxy
    - services
    - endpoints
    - pods
    - deployments
    - services
    verbs: ["get", "list", "watch"]
  - nonResourceURLs: ["/metrics"]
    verbs: ["get"]
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: prometheus
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRoleBinding
metadata:
  name: prometheus
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: prometheus
subjects:
- kind: ServiceAccount
  name: prometheus
  namespace: kube-system # Change the namespace if you plan to use a different one.
---
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  name: prometheus-deployment
spec:
  replicas: 1
  selector:
    matchLabels:
      app: prometheus
  template:
    metadata:
      name: prometheus
      labels:
        app: prometheus
    spec:
      serviceAccount: prometheus
      serviceAccountName: prometheus
      containers:
      - name: prometheus
        image: registry.cn-hangzhou.aliyuncs.com/acs/prometheus:v2.2.0-rc.0
        args:
          - '--storage.tsdb.path=/prometheus'
          - '--storage.tsdb.retention=$(STORAGE_RETENTION)'
          - '--web.enable-lifecycle'
          - '--storage.tsdb.no-lockfile'
          - '--config.file=/etc/prometheus/prometheus.yml'
        ports:
        - name: web
          containerPort: 9090
        env:
        - name: STORAGE_RETENTION
          valueFrom:
            configMapKeyRef:
              name: prometheus-env
              key: storage-retention
        volumeMounts:
        - name: config-volume
          mountPath: /etc/prometheus
        - name: prometheus-data
          mountPath: /prometheus
      volumes:
      - name: config-volume
        configMap:
          name: prometheus-configmap
      - name: prometheus-data
        emptyDir: {}
---

apiVersion: v1
kind: Service
metadata:
  labels:
    name: prometheus-svc
    kubernetes.io/name: "Prometheus"
  name: prometheus-svc
spec:
  type: LoadBalancer
  selector:
    app: prometheus
  ports:
  - name: prometheus
    protocol: TCP
    port: 9090
    targetPort: 9090

---
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-configmap
data:
  prometheus.yml: |-
    rule_files:
      - "/etc/prometheus-rules/*.rules"
    scrape_configs:
    - job_name: kubernetes-service-endpoints
      scrape_interval: 10s
      scrape_timeout: 10s
      kubernetes_sd_configs:
      - api_server: null
        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_service_namespace]
        action: replace
        target_label: kubernetes_namespace
      - source_labels: [__meta_kubernetes_service_name]
        action: replace
        target_label: kubernetes_name

If you use a namespace other than kube-system, you need to modify serviceAccount in ClusterRoleBinding in the YAML file.

apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRoleBinding
metadata:
  name: prometheus
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: prometheus
subjects:
- kind: ServiceAccount
  name: prometheus
  namespace: kube-system # Change the namespace if you plan to use a different one.

Deploy the Prometheus GPU-Exporter

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: node-gpu-exporter
spec:
  selector:
    matchLabels:
      app: node-gpu-exporter
  template:
    metadata:
      labels:
        app: node-gpu-exporter
    spec:
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
            - matchExpressions:
              - key: aliyun.accelerator/nvidia_count
                operator: Exists
      hostPID: true
      containers:
      - name: node-gpu-exporter
        image: registry.cn-hangzhou.aliyuncs.com/acs/gpu-prometheus-exporter:0.1-f48bc3c
        imagePullPolicy: Always
        env:
        - name: MY_NODE_NAME
          valueFrom:
            fieldRef:
              fieldPath: spec.nodeName
        - name: MY_POD_NAME
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        - name: MY_NODE_IP
          valueFrom:
            fieldRef:
              fieldPath: status.hostIP
        - name: EXCLUDE_PODS
          value: $(MY_POD_NAME),nvidia-device-plugin-$(MY_NODE_NAME),nvidia-device-plugin-ctr
        - name: CADVISOR_URL
          value: http://$(MY_NODE_IP):10255
        ports:
        - containerPort: 9445
          hostPort: 9445
        resources:
          requests:
            memory: 30Mi
            cpu: 100m
          limits:
            memory: 50Mi
            cpu: 200m

---
apiVersion: v1
kind: Service
metadata:
  annotations:
    prometheus.io/scrape: 'true'
  name: node-gpu-exporter
  labels:
    app: node-gpu-exporter
    k8s-app: node-gpu-exporter
spec:
  type: ClusterIP
  clusterIP: None
  ports:
  - name: http-metrics
    port: 9445
    protocol: TCP
  selector:
    app: node-gpu-exporter

Deploy Grafana

apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  name: monitoring-grafana
spec:
  replicas: 1
  template:
    metadata:
      labels:
        task: monitoring
        k8s-app: grafana
    spec:
      containers:
      - name: grafana
        image: registry.cn-hangzhou.aliyuncs.com/acs/grafana:5.0.4-gpu-monitoring
        ports:
        - containerPort: 3000
          protocol: TCP
      volumes:
      - name: grafana-storage
        emptyDir: {}
---
apiVersion: v1
kind: Service
metadata:
  name: monitoring-grafana
spec:
  ports:
  - port: 80
    targetPort: 3000
  type: LoadBalancer
  selector:
    k8s-app: grafana

Choose Application > Service. Select the corresponding cluster and the kube-system namespace, and click the external endpoint of monitoring-grafana. The logon page of Grafana is displayed. Log on to Grafana using the initial username and password, which are both admin. You can change the password or add other accounts after successful logon. On the Dashboard, you can view the node and pod GPU monitoring information.

3

Node GPU Monitoring

4

Pod GPU Monitoring

5

Deploy Applications

If you already have Arena, use it to submit a training task.

arena submit tf --name=style-transfer              \
              --gpus=1              \
              --workers=1              \
              --workerImage=registry.cn-hangzhou.aliyuncs.com/tensorflow-samples/neural-style:gpu \
              --workingDir=/neural-style \
              --ps=1              \
              --psImage=registry.cn-hangzhou.aliyuncs.com/tensorflow-samples/style-transfer:ps   \
              "python neural_style.py --styles /neural-style/examples/1-style.jpg --iterations 1000000"

NAME:   style-transfer
LAST DEPLOYED: Thu Sep 20 14:34:55 2018
NAMESPACE: default
STATUS: DEPLOYED

RESOURCES:
==> v1alpha2/TFJob
NAME                  AGE
style-transfer-tfjob  0s

After the task is submitted, you can see the pod deployed through Arena and the pod GPU monitoring information.

6

You can also see the GPU and load information about each node.

7

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