dcgm-exporter | NVIDIA GPU metrics exporter for Prometheus leveraging DCGM | GPU library

by NVIDIA Go Version: 3.1.8-3.1.5 License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | dcgm-exporter Summary

dcgm-exporter is a Go library typically used in Hardware, GPU, Prometheus applications. dcgm-exporter has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

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

Support

Quality

Security

License

Reuse

Support

dcgm-exporter has a low active ecosystem.

It has 341 star(s) with 79 fork(s). There are 14 watchers for this library.

There were 1 major release(s) in the last 12 months.

There are 60 open issues and 38 have been closed. On average issues are closed in 12 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of dcgm-exporter is 3.1.8-3.1.5

Quality

dcgm-exporter has 0 bugs and 0 code smells.

Security

dcgm-exporter has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

dcgm-exporter code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

dcgm-exporter is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

dcgm-exporter releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed dcgm-exporter and discovered the below as its top functions. This is intended to give you an instant insight into dcgm-exporter implemented functionality, and help decide if they suit your requirements.

main is the main entrypoint .
Run executes the dcgm command
InitializeSystemInfo initializes the system information .
parseDeviceOptions parses DOSA device options
parseDeviceOptionsToken parses device options
extractCounters extracts the counters from a list of records .
ToString converts a FieldValue to a string
ToDeviceToPod converts a ListResourcesResponse to a PodInfo map
ToMetric converts a field value to metrics . Metric .
SetupDcgmFieldsWatch creates a set of watchers for the given devices .

Get all kandi verified functions for this library.

dcgm-exporter Key Features

No Key Features are available at this moment for dcgm-exporter.

dcgm-exporter Examples and Code Snippets

No Code Snippets are available at this moment for dcgm-exporter.

Community Discussions

Trending Discussions on dcgm-exporter

On GKE, dcgm-exporter pod fails to run if the nvidia.com/gpu resource is not allocated

QUESTION

On GKE, dcgm-exporter pod fails to run if the nvidia.com/gpu resource is not allocated

Asked 2020-Nov-23 at 03:40

I am trying to query GPU usage metrics of GKE pods.

Here is what I've done for test:

Created GKE cluster with two node pools, one of them has two cpu-only nodes and the other has one node with NVIDIA Tesla T4 GPU. All nodes are running Container-Optimized OS.
As written in https://cloud.google.com/kubernetes-engine/docs/how-to/gpus#installing_drivers, I ran kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/nvidia-driver-installer/cos/daemonset-preloaded.yaml.
kubectl create -f dcgm-exporter.yaml

...

ANSWER

Answered 2020-Nov-23 at 03:40

It worked with these:

Set privileged: true to securityContext.
Add volume mount "nvidia-install-dir-host".

Source https://stackoverflow.com/questions/64940013

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install dcgm-exporter

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