#[[instances]]
# 指定使用的指标定义文件, 一般使用 default-counters.csv就够了，也可以尝试用其他两个csv文件
# path to the file, that contains the DCGM fields to collect
# collectors = "conf/input.dcgm/default-counters.csv"

# 是否是K8s环境，设置为true会附件Pod的信息
# Enable kubernetes mapping metrics to kubernetes pods
# kubernetes=false

# 指标中是否附加 gpu id 作为一个标签
# Choose Type of GPU ID to use to map kubernetes resources to pods. Possible values: "uid", "device-name"
# kubernetes-gpu-id-type = "uid"

# 是否使用 1.x 的ns
# Use old 1.x namespace
# use-old-namespace = false

# 支持的选项是f g i 
# f: FlexKey 如果MIG被禁用，则监控所有GPU；如果MIG被启用，则监控所有GPU实例
# g: MajorKey 监控top-level entities：GPU或NvSwitches或CPU 
# i: MinorKey 监控sub-level entities: GPU实例/NvLinks/CPU核心 - 如果MIG被禁用，则不能指定该选项
  cpu-devices = "f"

# 与cpu-devices的选项一样
# gpu devices
  devices = "f"

# 与cpu-devices的选项一样
  switch-devices = "f"

# 使用ConfigMap 
# ConfigMap <NAMESPACE>:<NAME> for metric data
  configmap-data = "none"

# 这里就是前置依赖的nvidia-dcgm服务, 如果是本机采集，则使用localhost:5555 ,如果是远程采集，则使用远端IP:5555
# Connect to remote hostengine at <HOST>:<PORT>
# remote-hostengine-info = "localhost:5555"

# 允许用户在没有实际GPU硬件的环境中模拟GPU, 仅用于测试
# Accept GPUs that are fake, for testing purposes only
# fake-gpus = false

# 将GPU型号名称中的每个空格替换为破折号，确保标识符连续且无空格。 
# Replaces every blank space in the GPU model name with a dash, ensuring a continuous, space-free identifier.
# replace-blanks-in-model-name = false