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
A graph-to-sequence model for one-step retrosynthesis and reaction outcome prediction.

Graph2SMILES A graph-to-sequence model for one-step retrosynthesis and reaction outcome prediction. 1. Environmental setup System requirements Ubuntu:

29 Nov 18, 2022
Analysis code and Latex source of the manuscript describing the conditional permutation test of confounding bias in predictive modelling.

Git repositoty of the manuscript entitled Statistical quantification of confounding bias in predictive modelling by Tamas Spisak The manuscript descri

PNI - Predictive Neuroimaging Lab, University Hospital Essen, Germany 0 Nov 22, 2021
RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?

RaftMLP RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality? By Yuki Tatsunami and Masato Taki (Rikkyo University) [arxiv]

Okojo 20 Aug 31, 2022
The story of Chicken for Club Bing

Chicken Story tl;dr: The time when Microsoft banned my entire country for cheating at Club Bing. (A lot of the details are from memory so I've recreat

Eyal 142 May 16, 2022
git《Tangent Space Backpropogation for 3D Transformation Groups》(CVPR 2021) GitHub:1]

LieTorch: Tangent Space Backpropagation Introduction The LieTorch library generalizes PyTorch to 3D transformation groups. Just as torch.Tensor is a m

Princeton Vision & Learning Lab 482 Jan 06, 2023
A Deep Learning Framework for Neural Derivative Hedging

NNHedge NNHedge is a PyTorch based framework for Neural Derivative Hedging. The following repository was implemented to ease the experiments of our pa

GUIJIN SON 17 Nov 14, 2022
Implementation of C-RNN-GAN.

Implementation of C-RNN-GAN. Publication: Title: C-RNN-GAN: Continuous recurrent neural networks with adversarial training Information: http://mogren.

Olof Mogren 427 Dec 25, 2022
Train Dense Passage Retriever (DPR) with a single GPU

Gradient Cached Dense Passage Retrieval Gradient Cached Dense Passage Retrieval (GC-DPR) - is an extension of the original DPR library. We introduce G

Luyu Gao 92 Jan 02, 2023
A program to recognize fruits on pictures or videos using yolov5

Yolov5 Fruits Detector Requirements Either Linux or Windows. We recommend Linux for better performance. Python 3.6+ and PyTorch 1.7+. Installation To

Fateme Zamanian 30 Jan 06, 2023
Statsmodels: statistical modeling and econometrics in Python

About statsmodels statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics an

statsmodels 8.1k Jan 02, 2023
Official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition" in AAAI2022.

AimCLR This is an official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Reco

Gty 44 Dec 17, 2022
Official repository of PanoAVQA: Grounded Audio-Visual Question Answering in 360° Videos (ICCV 2021)

Pano-AVQA Official repository of PanoAVQA: Grounded Audio-Visual Question Answering in 360° Videos (ICCV 2021) [Paper] [Poster] [Video] Getting Starte

Heeseung Yun 9 Dec 23, 2022
Cluttered MNIST Dataset

Cluttered MNIST Dataset A setup script will download MNIST and produce mnist/*.t7 files: luajit download_mnist.lua Example usage: local mnist_clutter

DeepMind 50 Jul 12, 2022
Supervised Classification from Text (P)

MSc-Thesis Module: Masters Research Thesis Language: Python Grade: 75 Title: An investigation of supervised classification of therapeutic process from

Matthew Laws 1 Nov 22, 2021
LRBoost is a scikit-learn compatible approach to performing linear residual based stacking/boosting.

LRBoost is a sckit-learn compatible package for linear residual boosting. LRBoost combines a linear estimator and a non-linear estimator to leverage t

Andrew Patton 5 Nov 23, 2022
PyTorch implementation for "Mining Latent Structures with Contrastive Modality Fusion for Multimedia Recommendation"

MIRCO PyTorch implementation for paper: Latent Structures Mining with Contrastive Modality Fusion for Multimedia Recommendation Dependencies Python 3.

Big Data and Multi-modal Computing Group, CRIPAC 9 Dec 08, 2022
Repository for "Exploring Sparsity in Image Super-Resolution for Efficient Inference", CVPR 2021

SMSR Reposity for "Exploring Sparsity in Image Super-Resolution for Efficient Inference" [arXiv] Highlights Locate and skip redundant computation in S

Longguang Wang 225 Dec 26, 2022
An ML & Correlation platform for transforming disparate data points of interest into usable intelligence.

SSIDprobeCollector An ML & Correlation platform for transforming disparate data points of interest into usable intelligence. At a High level the platf

Bill Reyor 1 Jan 30, 2022
Implementation of the bachelor's thesis "Real-time stock predictions with deep learning and news scraping".

Real-time stock predictions with deep learning and news scraping This repository contains a partial implementation of my bachelor's thesis "Real-time

David Álvarez de la Torre 0 Feb 09, 2022
A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019).

APPNP ⠀ A PyTorch implementation of Predict then Propagate: Graph Neural Networks meet Personalized PageRank (ICLR 2019). Abstract Neural message pass

Benedek Rozemberczki 329 Dec 30, 2022