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NVIDIA GPU Monitoring Tools

Bindings

This Github repository contains Golang bindings for the following two libraries:

  • NVIDIA Management Library (NVML) is a C-based API for monitoring and managing NVIDIA GPU devices.
  • NVIDIA Data Center GPU Manager (DCGM) is a set of tools for managing and monitoring NVIDIA GPUs in cluster environments. It's a low overhead tool suite that performs a variety of functions on each host system including active health monitoring, diagnostics, system validation, policies, power and clock management, group configuration and accounting.

You will also find samples for both of these bindings in this repository.

DCGM exporter

This Github repository also contains the DCGM exporter software. It exposes GPU metrics exporter for Prometheus leveraging NVIDIA Data Center GPU Manager (DCGM).

Find the installation and run instructions here.

Quickstart

To gather metrics on a GPU node, simply start the dcgm-exporter container:

$ docker run -d --gpus all --rm -p 9400:9400 nvidia/dcgm-exporter:latest
$ curl localhost:9400/metrics
# HELP DCGM_FI_DEV_SM_CLOCK SM clock frequency (in MHz).
# TYPE DCGM_FI_DEV_SM_CLOCK gauge
# HELP DCGM_FI_DEV_MEM_CLOCK Memory clock frequency (in MHz).
# TYPE DCGM_FI_DEV_MEM_CLOCK gauge
# HELP DCGM_FI_DEV_MEMORY_TEMP Memory temperature (in C).
# TYPE DCGM_FI_DEV_MEMORY_TEMP gauge
...
DCGM_FI_DEV_SM_CLOCK{gpu="0", UUID="GPU-604ac76c-d9cf-fef3-62e9-d92044ab6e52"} 139
DCGM_FI_DEV_MEM_CLOCK{gpu="0", UUID="GPU-604ac76c-d9cf-fef3-62e9-d92044ab6e52"} 405
DCGM_FI_DEV_MEMORY_TEMP{gpu="0", UUID="GPU-604ac76c-d9cf-fef3-62e9-d92044ab6e52"} 9223372036854775794
...

Quickstart on Kubernetes

Note: Consider using the NVIDIA GPU Operator rather than the DCGM exporter directly.

Ensure you have already setup your cluster with the default runtime as NVIDIA. To gather metrics on your GPU nodes you can deploy the daemonset:

$ kubectl create -f https://raw.githubusercontent.com/NVIDIA/gpu-monitoring-tools/2.0.0-rc.11/dcgm-exporter.yaml

# Let's get the output of a random pod:
$ NAME=$(kubectl get pods -l "app.kubernetes.io/name=dcgm-exporter, app.kubernetes.io/version=2.0.0-rc.11" \
                         -o "jsonpath={ .items[0].metadata.name}")

$ kubectl port-forward $NAME 8080:9400 &
$ curl -sL http://127.0.01:8080/metrics
# HELP DCGM_FI_DEV_SM_CLOCK SM clock frequency (in MHz).
# TYPE DCGM_FI_DEV_SM_CLOCK gauge
# HELP DCGM_FI_DEV_MEM_CLOCK Memory clock frequency (in MHz).
# TYPE DCGM_FI_DEV_MEM_CLOCK gauge
# HELP DCGM_FI_DEV_MEMORY_TEMP Memory temperature (in C).
# TYPE DCGM_FI_DEV_MEMORY_TEMP gauge
...
DCGM_FI_DEV_SM_CLOCK{gpu="0", UUID="GPU-604ac76c-d9cf-fef3-62e9-d92044ab6e52"} 139
DCGM_FI_DEV_MEM_CLOCK{gpu="0", UUID="GPU-604ac76c-d9cf-fef3-62e9-d92044ab6e52"} 405
DCGM_FI_DEV_MEMORY_TEMP{gpu="0", UUID="GPU-604ac76c-d9cf-fef3-62e9-d92044ab6e52"} 9223372036854775794
...

# If you are using the Prometheus operator
# Note on exporters here:
# https://github.com/coreos/prometheus-operator/blob/release-0.38/Documentation/user-guides/running-exporters.md

$ helm repo add stable https://kubernetes-charts.storage.googleapis.com
$ helm install stable/prometheus-operator --generate-name \
    --set "prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues=false"
$ kubectl create -f \
    https://raw.githubusercontent.com/NVIDIA/gpu-monitoring-tools/2.0.0-rc.11/service-monitor.yaml

# Note might take ~1-2 minutes for prometheus to pickup the metrics and display them
# You can also check in the WebUI the servce-discovery tab (in the Status category)
$ NAME=$(kubectl get svc -l app=prometheus-operator-prometheus -o jsonpath='{.items[0].metadata.name}')
$ kubectl port-forward $NAME 9090:9090 &
$ curl -sL http://127.0.01:9090/api/v1/query?query=DCGM_FI_DEV_MEMORY_TEMP"
{
	status: "success",
	data: {
		resultType: "vector",
		result: [
			{
				metric: {
					UUID: "GPU-604ac76c-d9cf-fef3-62e9-d92044ab6e52",
					__name__: "DCGM_FI_DEV_MEMORY_TEMP",
					...
					pod: "dcgm-exporter-fn7fm",
					service: "dcgm-exporter"
				},
				value: [
					1588399049.227,
					"9223372036854776000"
				]
			},
			...
		]
	}
}

Building From source and Running on Bare Metal

The dcgm-exporter is actually fairly straightforward to build and use. Ensure you have the following:

$ git clone https://github.com/NVIDIA/gpu-monitoring-tools.git
$ cd gpu-monitoring-tools
$ make binary
$ sudo make install
...
$ dcgm-exporter &
$ curl localhost:9400/metrics
# HELP DCGM_FI_DEV_SM_CLOCK SM clock frequency (in MHz).
# TYPE DCGM_FI_DEV_SM_CLOCK gauge
# HELP DCGM_FI_DEV_MEM_CLOCK Memory clock frequency (in MHz).
# TYPE DCGM_FI_DEV_MEM_CLOCK gauge
# HELP DCGM_FI_DEV_MEMORY_TEMP Memory temperature (in C).
# TYPE DCGM_FI_DEV_MEMORY_TEMP gauge
...
DCGM_FI_DEV_SM_CLOCK{gpu="0", UUID="GPU-604ac76c-d9cf-fef3-62e9-d92044ab6e52"} 139
DCGM_FI_DEV_MEM_CLOCK{gpu="0", UUID="GPU-604ac76c-d9cf-fef3-62e9-d92044ab6e52"} 405
DCGM_FI_DEV_MEMORY_TEMP{gpu="0", UUID="GPU-604ac76c-d9cf-fef3-62e9-d92044ab6e52"} 9223372036854775794
...

Changing the Metrics

With dcgm-exporter 2.0 you can configure which fields are collected by specifying a custom CSV file. You will find the default CSV file here and on your system or container at /etc/dcgm-exporter/default-counters.csv

The format of this file is pretty straightforward:

# Format,,
# If line starts with a '#' it is considered a comment,,
# DCGM FIELD, Prometheus metric type, help message

# Clocks,,
DCGM_FI_DEV_SM_CLOCK,  gauge, SM clock frequency (in MHz).
DCGM_FI_DEV_MEM_CLOCK, gauge, Memory clock frequency (in MHz).

A custom csv file can be specified using the -f option or --collectors as follows:

$ dcgm-exporter -f /tmp/custom-collectors.csv

Notes:

What about a Grafana Dashboard?

You can find the official NVIDIA dcgm-exporter dashboard here: https://grafana.com/grafana/dashboards/12239

You will also find the json file on this repo: https://github.com/NVIDIA/gpu-monitoring-tools/blob/2.0.0-rc.11/grafana/dcgm-exporter-dashboard.json

Pull requests are accepted!

Issues and Contributing

Checkout the Contributing document!