NVIDIA Data Center GPU Manager (DCGM)

This document describes how to configure your Google Kubernetes Engine deployment so that you can use Google Cloud Managed Service for Prometheus to collect metrics from NVIDIA Data Center GPU Manager. This document shows you how to do the following:

Set up the exporter for DCGM to report metrics.

These instructions apply only if you are using managed collection with Managed Service for Prometheus. If you are using self-deployed collection, then see the source repository for DCGM Exporter for installation information.

These instructions are provided as an example and are expected to work in most Kubernetes environments. For information about a managed DCGM offering, see Collect and view DCGM metrics.

If you are having trouble installing an application or exporter due to restrictive security or organizational policies, then we recommend you consult open-source documentation for support.

For information about NVIDIA Data Center GPU Manager, see NVIDIA DCGM.

Prerequisites

To collect metrics from DCGM by using Managed Service for Prometheus and managed collection, your deployment must meet the following requirements:

Your cluster must be running Google Kubernetes Engine version 1.28.15-gke.2475000 or later.
You must be running Managed Service for Prometheus with managed collection enabled. For more information, see Get started with managed collection.

Verify that you have sufficient quota for NVIDIA GPUs.

To enumerate GPU nodes in your GKE cluster and their GPU types in the relevant cluster, run the following command:

 kubectl get nodes -l cloud.google.com/gke-gpu -o jsonpath='{range .items[*]}{@.metadata.name}{" "}{@.metadata.labels.cloud\.google\.com/gke-accelerator}{"\n"}{end}'

Note that you might have to install a compatible NVIDIA GPU driver on the nodes if automatic installation was disabled or not supported for your GKE version. To verify that the NVIDIA GPU device plugin is running, run the following command:
```
 kubectl get pods -n kube-system | grep nvidia-gpu-device-plugin 
```

Install the DCGM exporter

We recommend that you install the DCGM exporter, DCGM-Exporter, by using the following config:

# Copyright 2023 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. apiVersion: apps/v1 kind: DaemonSet metadata:  name: nvidia-dcgm  namespace: gmp-public  labels:  app: nvidia-dcgm spec:  selector:  matchLabels:  app: nvidia-dcgm  updateStrategy:  type: RollingUpdate  template:  metadata:  labels:  name: nvidia-dcgm  app: nvidia-dcgm  spec:  affinity:  nodeAffinity:  requiredDuringSchedulingIgnoredDuringExecution:  nodeSelectorTerms:  - matchExpressions:  - key: cloud.google.com/gke-accelerator  operator: Exists  tolerations:  - operator: "Exists"  volumes:  - name: nvidia-install-dir-host  hostPath:  path: /home/kubernetes/bin/nvidia  type: Directory  containers:  - image: "nvcr.io/nvidia/cloud-native/dcgm:3.3.0-1-ubuntu22.04"  command: ["nv-hostengine", "-n", "-b", "ALL"]  ports:  - containerPort: 5555  hostPort: 5555  name: nvidia-dcgm  securityContext:  privileged: true  volumeMounts:  - name: nvidia-install-dir-host  mountPath: /usr/local/nvidia --- apiVersion: apps/v1 kind: DaemonSet metadata:  name: nvidia-dcgm-exporter  namespace: gmp-public  labels:  app.kubernetes.io/name: nvidia-dcgm-exporter spec:  selector:  matchLabels:  app.kubernetes.io/name: nvidia-dcgm-exporter  updateStrategy:  type: RollingUpdate  template:  metadata:  labels:  app.kubernetes.io/name: nvidia-dcgm-exporter  spec:  affinity:  nodeAffinity:  requiredDuringSchedulingIgnoredDuringExecution:  nodeSelectorTerms:  - matchExpressions:  - key: cloud.google.com/gke-accelerator  operator: Exists  tolerations:  - operator: "Exists"  volumes:  - name: nvidia-dcgm-exporter-metrics  configMap:  name: nvidia-dcgm-exporter-metrics  - name: nvidia-install-dir-host  hostPath:  path: /home/kubernetes/bin/nvidia  type: Directory  - name: pod-resources  hostPath:  path: /var/lib/kubelet/pod-resources  containers:  - name: nvidia-dcgm-exporter  image: nvcr.io/nvidia/k8s/dcgm-exporter:3.3.0-3.2.0-ubuntu22.04  command: ["/bin/bash", "-c"]  args:  - hostname $NODE_NAME; dcgm-exporter --remote-hostengine-info $(NODE_IP) --collectors /etc/dcgm-exporter/counters.csv  ports:  - name: metrics  containerPort: 9400  securityContext:  privileged: true  env:  - name: NODE_NAME  valueFrom:  fieldRef:  fieldPath: spec.nodeName  - name: "DCGM_EXPORTER_KUBERNETES_GPU_ID_TYPE"  value: "device-name"  - name: LD_LIBRARY_PATH  value: /usr/local/nvidia/lib64  - name: NODE_IP  valueFrom:  fieldRef:  fieldPath: status.hostIP  - name: DCGM_EXPORTER_KUBERNETES  value: 'true'  - name: DCGM_EXPORTER_LISTEN  value: ':9400'  volumeMounts:  - name: nvidia-dcgm-exporter-metrics  mountPath: "/etc/dcgm-exporter"  readOnly: true  - name: nvidia-install-dir-host  mountPath: /usr/local/nvidia  - name: pod-resources  mountPath: /var/lib/kubelet/pod-resources --- apiVersion: v1 kind: ConfigMap metadata:  name: nvidia-dcgm-exporter-metrics  namespace: gmp-public data:  counters.csv: |  # Utilization (the sample period varies depending on the product),,  DCGM_FI_DEV_GPU_UTIL, gauge, GPU utilization (in %).  DCGM_FI_DEV_MEM_COPY_UTIL, gauge, Memory utilization (in %).  # Temperature and power usage,,  DCGM_FI_DEV_GPU_TEMP, gauge, Current temperature readings for the device in degrees C.  DCGM_FI_DEV_MEMORY_TEMP, gauge, Memory temperature for the device.  DCGM_FI_DEV_POWER_USAGE, gauge, Power usage for the device in Watts.  # Utilization of IP blocks,,  DCGM_FI_PROF_SM_ACTIVE, gauge, The ratio of cycles an SM has at least 1 warp assigned  DCGM_FI_PROF_SM_OCCUPANCY, gauge, The fraction of resident warps on a multiprocessor  DCGM_FI_PROF_PIPE_TENSOR_ACTIVE, gauge, The ratio of cycles the tensor (HMMA) pipe is active (off the peak sustained elapsed cycles)  DCGM_FI_PROF_PIPE_FP64_ACTIVE, gauge, The fraction of cycles the FP64 (double precision) pipe was active.  DCGM_FI_PROF_PIPE_FP32_ACTIVE, gauge, The fraction of cycles the FP32 (single precision) pipe was active.  DCGM_FI_PROF_PIPE_FP16_ACTIVE, gauge, The fraction of cycles the FP16 (half precision) pipe was active.  # Memory usage,,  DCGM_FI_DEV_FB_FREE, gauge, Framebuffer memory free (in MiB).  DCGM_FI_DEV_FB_USED, gauge, Framebuffer memory used (in MiB).  DCGM_FI_DEV_FB_TOTAL, gauge, Total Frame Buffer of the GPU in MB.  # PCIE,,  DCGM_FI_PROF_PCIE_TX_BYTES, gauge, Total number of bytes transmitted through PCIe TX  DCGM_FI_PROF_PCIE_RX_BYTES, gauge, Total number of bytes received through PCIe RX  # NVLink,,  DCGM_FI_PROF_NVLINK_TX_BYTES, gauge, The number of bytes of active NvLink tx (transmit) data including both header and payload.  DCGM_FI_PROF_NVLINK_RX_BYTES, gauge, The number of bytes of active NvLink rx (read) data including both header and payload.

To verify that DCGM Exporter is emitting metrics on the expected endpoints, do the following:

Set up port-forwarding with the following command:

 kubectl -n gmp-public port-forward POD_NAME 9400

Access the endpoint localhost:9400/metrics by using the browser or the curl utility in another terminal session.

You can customize the ConfigMap section to select which GPU metrics to emit.

Alternatively, consider using the official Helm chart to install DCGM Exporter.

To apply configuration changes from a local file, run the following command:

 kubectl apply -n NAMESPACE_NAME -f FILE_NAME

You can also use Terraform to manage your configurations.

Define a PodMonitoring resource

For target discovery, the Managed Service for Prometheus Operator requires a PodMonitoring resource that corresponds to DCGM Exporter in the same namespace.

You can use the following PodMonitoring configuration:

# Copyright 2023 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. apiVersion: monitoring.googleapis.com/v1 kind: ClusterPodMonitoring metadata:  name: nvidia-dcgm-exporter  labels:  app.kubernetes.io/name: nvidia-dcgm-exporter  app.kubernetes.io/part-of: google-cloud-managed-prometheus spec:  selector:  matchLabels:  app.kubernetes.io/name: nvidia-dcgm-exporter  endpoints:  - port: metrics  interval: 30s  targetLabels:  metadata: []

To apply configuration changes from a local file, run the following command:

 kubectl apply -n NAMESPACE_NAME -f FILE_NAME

You can also use Terraform to manage your configurations.

Verify the configuration

You can use Metrics Explorer to verify that you correctly configured DCGM Exporter. It might take one or two minutes for Cloud Monitoring to ingest your metrics.

To verify the metrics are ingested, do the following:

In the Google Cloud console, go to the Metrics explorer page:
Go to Metrics explorer

If you use the search bar to find this page, then select the result whose subheading is Monitoring.
In the toolbar of the query-builder pane, select the button whose name is either MQL or PromQL.
Verify that PromQL is selected in the Language toggle. The language toggle is in the same toolbar that lets you format your query.

Enter and run the following query:

 DCGM_FI_DEV_GPU_UTIL{cluster="CLUSTER_NAME", namespace="gmp-public"}

Troubleshooting

For information about troubleshooting metric ingestion problems, see Problems with collection from exporters in Troubleshooting ingestion-side problems.