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:3.3.3-3.3.1-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.
In order to build dcgm-exporter ensure you have the following:
$ 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.
In an attempt to replace our modified nvidia_gpu_exporter the following changes were made to the dcgm exporter.
As our current exporter uses differently named metrics, sometimes with different units, e.g. mW vs W for power consumption or bytes instead of megabytes for memory use, added a way to add metrics that are based on metrics already DCGM collects. To use this feature take a standard metric as defined in default-counters.csv, e.g.:
DCGM_FI_DEV_FB_TOTAL, gauge, Frame buffer memory total (in MB).
and append (comma separated) new metric name, its description and multiplier, e.g.:
DCGM_FI_DEV_FB_TOTAL, gauge, Frame buffer memory total (in MB)., nvidia_gpu_memory_total_bytes, Total memory of the GPU device in bytes, 1048576
This feature relies on the existence of /run/gpustat/GPU-UUID (say /run/gpustat/GPU-8b4054a4-c830-20d4-1111-222222222222) or /run/gpustat/MIG-UUID (say /run/gpustat/MIG-2201f4b1-a001-5ae1-87df-c6ef1d8adfab) containing space separated jobid and uidnumber, e.g.:
[root@della-l01g2 ~]# cat /run/gpustat/MIG-2201f4b1-a001-5ae1-87df-c6ef1d8adfab
51234567 123456
This information will be appeneded as labels to appropriate metrics, e.g.:
DCGM_FI_DEV_FB_USED{gpu="0",UUID="GPU-d6dd33b9-e50e-997c-f303-c8f7312fa498",device="nvidia0",modelName="NVIDIA A100 80GB PCIe",GPU_I_PROFILE="1g.10gb",GPU_I_ID="7",Hostname="della-l01g1",DCGM_FI_DRIVER_VERSION="535.104.05",jobid="51234567",userid="123456"} 8543
as well as added as a separate metrics:
nvidia_gpu_jobId{minor_number="0",name="NVIDIA A100 80GB PCIe",uuid="GPU-d6dd33b9-e50e-997c-f303-c8f7312fa498"} 51234567
nvidia_gpu_jobUid{minor_number="0",name="NVIDIA A100 80GB PCIe",uuid="GPU-d6dd33b9-e50e-997c-f303-c8f7312fa498"} 123456
same as in our previously mentioned nvidia_gpu_exporter.