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# DCGM-Exporter
This repository contains the DCGM-Exporter project. It exposes GPU metrics exporter for [Prometheus](https://prometheus.io/) leveraging [NVIDIA DCGM](https://developer.nvidia.com/dcgm).
### Documentation
Official documentation for DCGM-Exporter can be found on [docs.nvidia.com](https://docs.nvidia.com/datacenter/cloud-native/gpu-telemetry/dcgm-exporter.html).
### Quickstart
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:3.2.5-3.1.7-ubuntu20.04
$ 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
...
```
### Quickstart on Kubernetes
Note: Consider using the [NVIDIA GPU Operator](https://github.com/NVIDIA/gpu-operator) rather than DCGM-Exporter directly.
Ensure you have already setup your cluster with the [default runtime as NVIDIA](https://github.com/NVIDIA/nvidia-container-runtime#docker-engine-setup).
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](https://docs.nvidia.com/datacenter/cloud-native/kubernetes/dcgme2e.html#gpu-telemetry).
`dcgm-exporter` is deployed as part of the GPU Operator. To get started with integrating with Prometheus, check the Operator [user guide](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/getting-started.html#gpu-telemetry).
### Building from Source
In order to build dcgm-exporter ensure you have the following:
- [Golang >= 1.14 installed](https://golang.org/)
- [DCGM installed](https://developer.nvidia.com/dcgm)
```
$ 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
...
```
### Changing Metrics
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
### 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 under `grafana/dcgm-exporter-dashboard.json`
Pull requests are accepted!
### Building the containers
This project uses [docker buildx](https://docs.docker.com/buildx/working-with-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 ubuntu20.04 PLATFORMS=linux/amd64 OUTPUT=type=docker
```
Build and push the images to some other 'private_registry'
```
make REGISTRY=<private_registry> push
```
## Issues and Contributing
[Checkout the Contributing document!](CONTRIBUTING.md)
* Please let us know by [filing a new issue](https://github.com/NVIDIA/dcgm-exporter/issues/new)
* You can contribute by opening a [pull request](https://github.com/NVIDIA/dcgm-exporter)
### Reporting Security Issues
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](https://www.nvidia.com/en-us/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.
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