Sundaram Surampudi sksundaram-learning

Electron apps

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Wagtail tutorial

Thanks to [Serafeim Papastefanos] for authoring this tutorial. Please note that the installation process is in flux; most of the steps here should soon be unnecessary.

[Wagtail] is a new Open Source [Django]-based CMS. In this 20 minute tutorial we will see how you can create a blog from scratch using Wagtail. If you want to see some more examples of usage please take a look at the [wagtaildemo] GitHub project.

To follow this tutorial you will need to have [Python] 2.7 installed with a working version of [pip] and [virtualenv].

Installing the wagtail dependencies

Kubernetes on (vanilla) Raspbian Lite

Yes - you can create a Kubernetes cluster with Raspberry Pis with the default operating system called Raspbian. This means you can carry on using all the tools and packages you're used to with the officially-supported OS.

This is part of a blog post Serverless Kubernetes home-lab with your Raspberry Pis written by Alex Ellis.

Copyright disclaimer: Please provide a link to the post and give attribution to the author if you plan to use this content in your own materials.

Pre-reqs:

Master: Dependencies

apt update && apt upgrade -y
curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -

cat <<EOF > /etc/apt/sources.list.d/kubernetes.list
deb http://apt.kubernetes.io/ kubernetes-xenial main
EOF

	/* PostgreSQL Multi Node Serial */
	caslib pgdvd datasource=(
	srctype="postgres",
	username="casdm",
	password="xxxxxx",
	server="sasdb.race.sas.com",
	database="dvdrental",
	schema="public",
	numreadnodes=3) ;

	.vagrant

	# Bundler
	bin/*
	.bundle/*

	.kitchen/
	.kitchen.local.yml

	Berksfile.lock

	Installing Dremio 1.4 on Ubuntu 16

	##Install links / references
	https://www.dremio.com/tutorials/recommender-scikit-learn-dremio-postgres-mongodb/
	https://www.dremio.com/tutorials/dremio-oracle-aws/
	https://docs.dremio.com/deployment/standalone-tarball.html


	## First, install Java 1.8+, 64 bit.
	sudo apt-get update

	R to python useful data wrangling snippets

	The dplyr package in R makes data wrangling significantly easier.
	The beauty of dplyr is that, by design, the options available are limited.
	Specifically, a set of key verbs form the core of the package.
	Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe.
	Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R.
	The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

	dplyr is organised around six key verbs

	declare @tableName varchar(200)
	declare @columnName varchar(200)
	declare @nullable varchar(50)
	declare @datatype varchar(50)
	declare @maxlen int

	declare @sType varchar(50)
	declare @sProperty varchar(200)

	DECLARE table_cursor CURSOR FOR

	#!/bin/bash
	# Update
	apt-get update && apt-get upgrade -y
	# Install python+packages and curl
	apt-get install -y python3 python3-pip python3-numpy python3-scipy python3-matplotlib ipython3 ipython3-notebook python3-pandas python3-nose curl wget
	# Update pip
	python3 -m easy_install -U pip

	# Correctinng python file
	# which causes stuck during package installation