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.
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.
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
...
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"
]
},
...
]
}
}
The dcgm-exporter is actually fairly straightforward to build and use. Ensure you have the following:
- Golang >= 1.14 installed
- DCGM installed
- On DGX, the NVIDIA Fabric Manager up and running
$ 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
...
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:
- Always make sure your entries have 3 commas (',')
- The complete list of counters that can be collected can be found on the DCGM API reference website: https://docs.nvidia.com/datacenter/dcgm/1.7/dcgm-api/group__dcgmFieldIdentifiers.html
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!
Checkout the Contributing document!
- Please let us know by filing a new issue
- You can contribute by opening a pull request
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