A very lightweight monitoring system for Raspberry Pi clusters running Kubernetes.

Related tags

Deep Learningomni
Overview

OMNI

A very lightweight monitoring system for Raspberry Pi clusters running Kubernetes.

omni

Why?

When I finished my Kubernetes cluster using a few Raspberry Pis, the first thing I wanted to do is install Prometheus + Grafana for monitoring, and so I did. But when I had all of it working I found a few drawbacks:

  • The Prometheus exporter pods use a lot of RAM
  • The Prometheus exporter pods use a considerable amount of CPU
  • Prometheus gathers way too much data that I don't really need.
  • The node where the main Prometheus pod is installed gets all of the information and saves it in its own database, constantly performing a lot of writes to the SD card. SD cards under lots of constant writing operations tend to die.

Last but not least, I like to learn how these things work.

Advantages

Omni has (what I consider) some advantages over the regular Prometheus + Grafana combo:

  • It uses almost no RAM (13 Mb)
  • It uses almost no CPU
  • It gathers only the information I need
  • All of the information is sent to an InfluxDB instance that could be outside of the cluster. This means that no information is persisted in the Pis, extending their SD card's lifetime.
  • InfluxDB acts as the database and the graph dashboard at the same time, so there is no need to also install Grafana (although you could if you wanted to).

Prerequisites

For Omni to work, you'll need to have a couple of things running first.

InfluxDB

It's a time series database (just like Prometheus) that has nice charts and UI overall.

One of the goals of this project is to avoid constant writing to the SD cards, so you have a few options for the placement of the database:

  1. Use InfluxDB's online service (there is even a free tier https://www.influxdata.com/influxdb-pricing/)
  2. Run an InfluxDB instance in a server outside the Pi cluster (this what I'm doing right now)
  3. If you have better storage in your cluster (like M.2, SSD, etc.) and don't have the SD card limitation, run InfluxDB in the same cluster.

Libraries

You'll need to have the libseccomp2.deb library installed in each of your nodes to avoid a Python error:

Fatal Python Error: pyinit_main: can't initialize time

(more info here)

To install it you can do it in two ways (only one is needed):

  • Ansible: all nodes at the same time

    Edit the file ansible-playbook-libs.yaml in this repo, add your hosts and run:

    ansible-playbook install-libs.yaml
  • SSH: one by one

    Connect into each of your nodes and run:

    wget http://ftp.us.debian.org/debian/pool/main/libs/libseccomp/libseccomp2_2.5.1-1_armhf.deb
    sudo dpkg -i libseccomp2_2.5.1-1_armhf.deb

Once you have it, everything should work ok.

Installation

Before deploying Omni you'll have to specify the attributes of your InfluxDB instance.

  1. Open omni-install.yaml and fill the variables with your InfluxDB instance information.

    NOTE: The attribute OMNI_DATA_RATE_SECONDS specifies the number of seconds between data reporting events that are sent to the InfluxDB server.

  2. Check that everything is running as expected:

kubectl get all -n omni-system

And you are done! 🎉

Contributions

Pull requests with improvements and new features are more than welcome.

Owner
Matias Godoy
Jack of all trades, master of none
Matias Godoy
Improving Convolutional Networks via Attention Transfer (ICLR 2017)

Attention Transfer PyTorch code for "Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Tran

Sergey Zagoruyko 1.4k Dec 23, 2022
Multi-Stage Progressive Image Restoration

Multi-Stage Progressive Image Restoration Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Sh

Syed Waqas Zamir 859 Dec 22, 2022
Image-to-image translation with conditional adversarial nets

pix2pix Project | Arxiv | PyTorch Torch implementation for learning a mapping from input images to output images, for example: Image-to-Image Translat

Phillip Isola 9.3k Jan 08, 2023
A repository for generating stylized talking 3D and 3D face

style_avatar A repository for generating stylized talking 3D faces and 2D videos. This is the repository for paper Imitating Arbitrary Talking Style f

Haozhe Wu 191 Dec 22, 2022
Semiconductor Machine learning project

Wafer Fault Detection Problem Statement: Wafer (In electronics), also called a slice or substrate, is a thin slice of semiconductor, such as a crystal

kunal suryawanshi 1 Jan 15, 2022
EdiBERT, a generative model for image editing

EdiBERT, a generative model for image editing EdiBERT is a generative model based on a bi-directional transformer, suited for image manipulation. The

16 Dec 07, 2022
Official git repo for the CHIRP project

CHIRP Project This is the official git repository for the CHIRP project. Pull requests are accepted here, but for the moment, the main repository is s

Dan Smith 77 Jan 08, 2023
GULAG: GUessing LAnGuages with neural networks

GULAG: GUessing LAnGuages with neural networks Classify languages in text via neural networks. Привет! My name is Egor. Was für ein herrliches Frühl

Egor Spirin 12 Sep 02, 2022
Code and data of the EMNLP 2021 paper "Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer"

StyleAttack Code and data of the EMNLP 2021 paper "Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer" Prepare Pois

THUNLP 19 Nov 20, 2022
BED: A Real-Time Object Detection System for Edge Devices

BED: A Real-Time Object Detection System for Edge Devices About this project Thi

Data Analytics Lab at Texas A&M University 44 Nov 18, 2022
Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation

Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation This is the official repository for our paper Neural Reprojection Error

Hugo Germain 78 Dec 01, 2022
HGCAE Pytorch implementation. CVPR2021 accepted.

Hyperbolic Graph Convolutional Auto-Encoders Accepted to CVPR2021 🎉 Official PyTorch code of Unsupervised Hyperbolic Representation Learning via Mess

Junho Cho 37 Nov 13, 2022
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).

Attention Walk ⠀⠀ A PyTorch Implementation of Watch Your Step: Learning Node Embeddings via Graph Attention (NIPS 2018). Abstract Graph embedding meth

Benedek Rozemberczki 303 Dec 09, 2022
PyTorch source code for Distilling Knowledge by Mimicking Features

LSHFM.detection This is the PyTorch source code for Distilling Knowledge by Mimicking Features. And this project contains code for object detection wi

Guo-Hua Wang 4 Dec 17, 2022
Self-supervised Multi-modal Hybrid Fusion Network for Brain Tumor Segmentation

JBHI-Pytorch This repository contains a reference implementation of the algorithms described in our paper "Self-supervised Multi-modal Hybrid Fusion N

FeiyiFANG 5 Dec 13, 2021
Recommendationsystem - Movie-recommendation - matrixfactorization colloborative filtering recommendation system user

recommendationsystem matrixfactorization colloborative filtering recommendation

kunal jagdish madavi 1 Jan 01, 2022
Source code and data from the RecSys 2020 article "Carousel Personalization in Music Streaming Apps with Contextual Bandits" by W. Bendada, G. Salha and T. Bontempelli

Carousel Personalization in Music Streaming Apps with Contextual Bandits - RecSys 2020 This repository provides Python code and data to reproduce expe

Deezer 48 Jan 02, 2023
Efficient Training of Audio Transformers with Patchout

PaSST: Efficient Training of Audio Transformers with Patchout This is the implementation for Efficient Training of Audio Transformers with Patchout Pa

165 Dec 26, 2022
Vision Transformer and MLP-Mixer Architectures

Vision Transformer and MLP-Mixer Architectures Update (2.7.2021): Added the "When Vision Transformers Outperform ResNets..." paper, and SAM (Sharpness

Google Research 6.4k Jan 04, 2023
Code of the paper "Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition"

SEW (Squeezed and Efficient Wav2vec) The repo contains the code of the paper "Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speec

ASAPP Research 67 Dec 01, 2022