Jayakumar Munuswamy jaikumarm

Simple multi cloud Docker cluster using Docker Swarm

This guide explains how to create a simple multi cloud Docker cluster using Docker Swarm, Docker Machine and the three top cloud providers nowadays - Google Compute Engine, Microsoft Azure and AWS.

Prerequisites

This guide assumes you have a Linux host with Docker CE installed. If you are using Docker for Mac or Docker for Windows you can avoid the Docker Machine set up since it comes included.

Install Docker Machine

Exporting your 2FA tokens from Authy to transfer them into another 2FA application

IMPORTANT - Update regarding deprecation of Authy desktop apps

Past August 2024, Authy stopped supported the desktop version of their apps:
See Authy is shutting down its desktop app | The 2FA app Authy will only be available on Android and iOS starting in August for details.

And indeed, after a while, Authy changed something in their backend which now prevents the old desktop app from logging in. If you are already logged in, then you are in luck, and you can follow the instructions below to export your tokens.

If you are not logged in anymore, but can find a backup of the necessary files, then restore those files, and re-install Authy 2.2.3 following the instructions below, and it should work as expected.

/etc/ufw/applications.d/plexmediaserver

[plexmediaserver]
title=Plex Media Server (Standard)
description=The Plex Media Server
ports=32400/tcp|3005/tcp|5353/udp|8324/tcp|32410:32414/udp

[plexmediaserver-dlna]
title=Plex Media Server (DLNA)
description=The Plex Media Server (additional DLNA capability only)

	# Python 3 code

	import matplotlib.pyplot as mpl
	import scipy.cluster.hierarchy as sch,random
	import numpy as np
	import pandas as pd

	# Now, take corMat and covMat generated from R, input into Marcos' Python, and check output is the same...
	col_list = ["IGV.Close", "TLT.Close", "IAU.Close", "IYR.Close"]
	corr = pd.read_csv("corMat_23072021.csv", usecols=col_list)

	#
	# This small example shows you how to access JS-based requests via Selenium
	# Like this, one can access raw data for scraping,
	# for example on many JS-intensive/React-based websites
	#

	from time import sleep

	from selenium import webdriver
	from selenium.webdriver import DesiredCapabilities

	import math
	import asyncio
	import logging, sys
	import sqlite3
	from asyncio import sleep
	from logging.handlers import TimedRotatingFileHandler

	import aiohttp_cors
	import aiosqlite
	import socketio

	"""
	Python code for fractional differencing of pandas time series
	illustrating the concepts of the article "Preserving Memory in Stationary Time Series"
	by Simon Kuttruf

	While this code is dedicated to the public domain for use without permission, the author disclaims any liability in connection with the use of this code.
	"""

	import numpy as np
	import pandas as pd

	import numpy as np

	EPSILON = 1e-10


	def _error(actual: np.ndarray, predicted: np.ndarray):
	""" Simple error """
	return actual - predicted

	#!/bin/sh
	# OUTDATED: please refer to the link below for the latest version:
	# https://github.com/rancherlabs/support-tools/blob/master/extended-rancher-2-cleanup/extended-cleanup-rancher2.sh
	docker rm -f $(docker ps -qa)
	docker volume rm $(docker volume ls -q)
	cleanupdirs="/var/lib/etcd /etc/kubernetes /etc/cni /opt/cni /var/lib/cni /var/run/calico /opt/rke"
	for dir in $cleanupdirs; do
	echo "Removing $dir"
	rm -rf $dir
	done

	"""
	Clean and simple Keras implementation of network architectures described in:
	- (ResNet-50) [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf).
	- (ResNeXt-50 32x4d) [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf).

	Python 3.
	"""

	from keras import layers
	from keras import models