This repository contains the DCGM-Exporter project. It exposes GPU metrics exporter for Prometheus leveraging NVIDIA DCGM.

Official documentation for DCGM-Exporter can be found on docs.nvidia.com.

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

docker run -d --gpus all --rm -p 9400:9400 nvcr.io/nvidia/k8s/dcgm-exporter:3.3.9-3.6.1-ubuntu22.04
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 DCGM-Exporter directly.

Ensure you have already setup your cluster with the default runtime as NVIDIA.

The recommended way to install DCGM-Exporter is to use the Helm chart:

helm repo add gpu-helm-charts \
  https://nvidia.github.io/dcgm-exporter/helm-charts

Update the repo:

helm repo update

And install the chart:

helm install \
    --generate-name \
    gpu-helm-charts/dcgm-exporter

Once the dcgm-exporter pod is deployed, you can use port forwarding to obtain metrics quickly:

kubectl create -f https://raw.githubusercontent.com/NVIDIA/dcgm-exporter/master/dcgm-exporter.yaml

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

kubectl port-forward $NAME 8080:9400 &

curl -sL http://127.0.0.1:8080/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",container="",namespace="",pod=""} 139
DCGM_FI_DEV_MEM_CLOCK{gpu="0", UUID="GPU-604ac76c-d9cf-fef3-62e9-d92044ab6e52",container="",namespace="",pod=""} 405
DCGM_FI_DEV_MEMORY_TEMP{gpu="0", UUID="GPU-604ac76c-d9cf-fef3-62e9-d92044ab6e52",container="",namespace="",pod=""} 9223372036854775794
...

To integrate DCGM-Exporter with Prometheus and Grafana, see the full instructions in the user guide. dcgm-exporter is deployed as part of the GPU Operator. To get started with integrating with Prometheus, check the Operator user guide.

Exporter supports TLS and basic auth using exporter-toolkit. To use TLS and/or basic auth, users need to use --web-config-file CLI flag as follows

dcgm-exporter --web-config-file=web-config.yaml

A sample web-config.yaml file can be fetched from exporter-toolkit repository. The reference of the web-config.yaml file can be consulted in the docs.

The DCGM-exporter can include High-Performance Computing (HPC) job information into its metric labels. To achieve this, HPC environment administrators must configure their HPC environment to generate files that map GPUs to HPC jobs.

These mapping files follow a specific format:

• Each file is named after a unique GPU ID (e.g., 0, 1, 2, etc.).
• Each line in the file contains JOB IDs that run on the corresponding GPU.

To enable GPU-to-job mapping on the DCGM-exporter side, users must run the DCGM-exporter with the --hpc-job-mapping-dir command-line parameter, pointing to a directory where the HPC cluster creates job mapping files. Or, users can set the environment variable DCGM_HPC_JOB_MAPPING_DIR to achieve the same result.

In order to build dcgm-exporter ensure you have the following:

• Golang >= 1.21 installed
• DCGM installed

git clone https://github.com/NVIDIA/dcgm-exporter.git
cd dcgm-exporter
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 you can configure which fields are collected by specifying a custom CSV file. You will find the default CSV file under etc/default-counters.csv in the repository, which is copied on your system or container to /etc/dcgm-exporter/default-counters.csv

The layout and format of this file is as follows:

# 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 2 commas (',')
• The complete list of counters that can be collected can be found on the DCGM API reference manual: https://docs.nvidia.com/datacenter/dcgm/latest/dcgm-api/dcgm-api-field-ids.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 under grafana/dcgm-exporter-dashboard.json

Pull requests are accepted!

This project uses docker buildx for multi-arch image creation. Follow the instructions on that page to get a working builder instance for creating these containers. Some other useful build options follow.

Builds local images based on the machine architecture and makes them available in 'docker images'

make local

Build the ubuntu image and export to 'docker images'

make ubuntu22.04 PLATFORMS=linux/amd64 OUTPUT=type=docker

Build and push the images to some other 'private_registry'

make REGISTRY=<private_registry> push

Checkout the Contributing document!

• Please let us know by filing a new issue
• You can contribute by opening a pull request

We ask that all community members and users of DCGM Exporter follow the standard NVIDIA process for reporting security vulnerabilities. This process is documented at the NVIDIA Product Security website. Following the process will result in any needed CVE being created as well as appropriate notifications being communicated to the entire DCGM Exporter community. NVIDIA reserves the right to delete vulnerability reports until they're fixed.

Please refer to the policies listed there to answer questions related to reporting security issues.