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
Official Implementation of SWAD (NeurIPS 2021)

SWAD: Domain Generalization by Seeking Flat Minima (NeurIPS'21) Official PyTorch implementation of SWAD: Domain Generalization by Seeking Flat Minima.

Junbum Cha 97 Dec 20, 2022
百度2021年语言与智能技术竞赛机器阅读理解Pytorch版baseline

项目说明: 百度2021年语言与智能技术竞赛机器阅读理解Pytorch版baseline 比赛链接:https://aistudio.baidu.com/aistudio/competition/detail/66?isFromLuge=true 官方的baseline版本是基于paddlepadd

周俊贤 54 Nov 23, 2022
PyTorch implementations of algorithms for density estimation

pytorch-flows A PyTorch implementations of Masked Autoregressive Flow and some other invertible transformations from Glow: Generative Flow with Invert

Ilya Kostrikov 546 Dec 05, 2022
Code release for ICCV 2021 paper "Anticipative Video Transformer"

Anticipative Video Transformer Ranked first in the Action Anticipation task of the CVPR 2021 EPIC-Kitchens Challenge! (entry: AVT-FB-UT) [project page

Facebook Research 123 Dec 13, 2022
Pytorch implementation of Generative Models as Distributions of Functions 🌿

Generative Models as Distributions of Functions This repo contains code to reproduce all experiments in Generative Models as Distributions of Function

Emilien Dupont 117 Dec 29, 2022
Code for ICCV2021 paper SPEC: Seeing People in the Wild with an Estimated Camera

SPEC: Seeing People in the Wild with an Estimated Camera [ICCV 2021] SPEC: Seeing People in the Wild with an Estimated Camera, Muhammed Kocabas, Chun-

Muhammed Kocabas 187 Dec 26, 2022
Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition

Light-SERNet This is the Tensorflow 2.x implementation of our paper "Light-SERNet: A lightweight fully convolutional neural network for speech emotion

Arya Aftab 29 Nov 12, 2022
The official implementation of EIGNN: Efficient Infinite-Depth Graph Neural Networks (NeurIPS 2021)

EIGNN: Efficient Infinite-Depth Graph Neural Networks The official implementation of EIGNN: Efficient Infinite-Depth Graph Neural Networks (NeurIPS 20

Juncheng Liu 14 Nov 22, 2022
A stable algorithm for GAN training

DRAGAN (Deep Regret Analytic Generative Adversarial Networks) Link to our paper - https://arxiv.org/abs/1705.07215 Pytorch implementation (thanks!) -

195 Oct 10, 2022
Flower - A Friendly Federated Learning Framework

Flower - A Friendly Federated Learning Framework Flower (flwr) is a framework for building federated learning systems. The design of Flower is based o

Adap 1.8k Jan 01, 2023
The versatile ocean simulator, in pure Python, powered by JAX.

Veros is the versatile ocean simulator -- it aims to be a powerful tool that makes high-performance ocean modeling approachable and fun. Because Veros

TeamOcean 245 Dec 20, 2022
Hub is a dataset format with a simple API for creating, storing, and collaborating on AI datasets of any size.

Hub is a dataset format with a simple API for creating, storing, and collaborating on AI datasets of any size. The hub data layout enables rapid transformations and streaming of data while training m

Activeloop 5.1k Jan 08, 2023
Generative Modelling of BRDF Textures from Flash Images [SIGGRAPH Asia, 2021]

Neural Material Official code repository for the paper: Generative Modelling of BRDF Textures from Flash Images [SIGGRAPH Asia, 2021] Henzler, Deschai

Philipp Henzler 80 Dec 20, 2022
Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"

Dangers of Bayesian Model Averaging under Covariate Shift This repository contains the code to reproduce the experiments in the paper Dangers of Bayes

Pavel Izmailov 25 Sep 21, 2022
This Artificial Intelligence program can take a black and white/grayscale image and generate a realistic or plausible colorized version of the same picture.

Colorizer The point of this project is to write a program capable of taking a black and white / grayscale image, and generating a realistic or plausib

Maitri Shah 1 Jan 06, 2022
[NeurIPS-2020] Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID.

Self-paced Contrastive Learning (SpCL) The official repository for Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID

Yixiao Ge 286 Dec 21, 2022
Unadversarial Examples: Designing Objects for Robust Vision

Unadversarial Examples: Designing Objects for Robust Vision This repository contains the code necessary to replicate the major results of our paper: U

Microsoft 93 Nov 28, 2022
Heart Arrhythmia Classification

This program takes and input of an ECG in European Data Format (EDF) and outputs the classification for heartbeats into normal vs different types of arrhythmia . It uses a deep learning model for cla

4 Nov 02, 2022
[ICLR 2021] Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization

Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, Tengyu Ma

Kaidi Cao 29 Oct 20, 2022
Intrusion Detection System using ensemble learning (machine learning)

IDS-ML implementation of an intrusion detection system using ensemble machine learning methods Data set This project is carried out using the UNSW-15

4 Nov 25, 2022