prometheus使用哪种方式可以监控容器的gpu（请注意是监控容器中的gpu信息），请列出详细步骤及对应的镜像版本

要使用Prometheus监控容器中的GPU信息，可以使用NVIDIA GPU Operator。以下是详细步骤：

1. 安装NVIDIA GPU Operator

kubectl create -f https://raw.githubusercontent.com/NVIDIA/gpu-operator/master/deploy/gpu-operator.yaml

2. 安装NVIDIA GPU Exporter

kubectl create -f https://raw.githubusercontent.com/NVIDIA/gpu-monitoring-tools/master/exporter/deployments/gpu-exporter.yaml

3. 创建一个DaemonSet来在每个节点上运行NVIDIA GPU Exporter

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: gpu-exporter
  namespace: monitoring
spec:
  selector:
    matchLabels:
      app: gpu-exporter
  template:
    metadata:
      labels:
        app: gpu-exporter
    spec:
      containers:
      - name: gpu-exporter
        image: nvcr.io/nvidia/k8s/gpu-exporter:1.2.0
        imagePullPolicy: IfNotPresent
        args:
        - --no-collector.gpu
        - --collector.nvml
        ports:
        - containerPort: 9101
          name: metrics
        resources:
          limits:
            nvidia.com/gpu: 1
          requests:
            nvidia.com/gpu: 1
        volumeMounts:
        - name: nvidia
          mountPath: /usr/local/nvidia
      volumes:
      - name: nvidia
        hostPath:
          path: /usr/local/nvidia

4. 在Prometheus的配置文件中添加以下内容：

- job_name: 'gpu-exporter'
  scrape_interval: 10s
  static_configs:
  - targets: ['gpu-exporter.monitoring.svc.cluster.local:9101']

5. 在Prometheus的Docker Compose文件中添加以下内容：

gpu-exporter:
  image: nvcr.io/nvidia/k8s/gpu-exporter:1.2.0
  container_name: gpu-exporter
  ports:
    - 9101:9101
  args:
    - --no-collector.gpu
    - --collector.nvml
  resources:
    limits:
      nvidia.com/gpu: 1
    requests:
      nvidia.com/gpu: 1
  volumeMounts:
    - name: nvidia
      mountPath: /usr/local/nvidia
  volumes:
    - name: nvidia
      hostPath:
        path: /usr/local/nvidia

6. 重启Prometheus和NVIDIA GPU Exporter容器。现在，您应该能够在Prometheus中看到来自NVIDIA GPU Exporter的指标，包括容器中的GPU信息。

镜像版本：nvcr.io/nvidia/k8s/gpu-exporter:1.2.0