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Redis & OpenFaaS micro-tutorial

After you've completed this micro-tutorial you'll be making requests to your Redis cache from serverless functions with OpenFaaS. From there it's up to you to build something awesome.

Pre-reqs:

Deploy OpenFaaS and the faas-cli.

This guide is for OpenFaaS on Kubernetes, but if you're using Swarm that's OK - you'll just have to adapt some of the commands for setting up Redis. The OpenFaaS code will be the same.

	#!/bin/bash

	SOURCE=$1
	TARGET=eng
	IN=$2
	OUT=$3

	while read -r col1 rest; do
	printf '%s\t%s\n' "$col1" "$(indictrans -s $SOURCE -t $TARGET --ml --build-lookup <<<"$rest")"
	done < $IN > $OUT

	/**
	* Shuffle array
	* Fisher-Yates (aka Knuth) Shuffle
	*/
	let shuffle = (array) => {
	var currentIndex = array.length, temporaryValue, randomIndex;
	while (0 !== currentIndex) {
	randomIndex = Math.floor(Math.random() * currentIndex);
	currentIndex -= 1;
	temporaryValue = array[currentIndex];

	/**
	* Returns a random number between min (inclusive) and max (exclusive)
	*/
	let getRandomArbitrary => (min, max, fixed) {
	fixed=fixed=10;
	return (Math.random() * (max - min) + min).toFixed(fixed);
	}

	/**
	* Save or open HTML 5 Blob Object-URL
	* @see https://stackoverflow.com/questions/22724070/prompt-file-download-with-xmlhttprequest
	* @param blob Blob
	* @param fileBName String
	*/
	function saveOrOpenBlob(blob, fileName) {
	window.requestFileSystem = window.requestFileSystem \|\| window.webkitRequestFileSystem;
	window.requestFileSystem(window.TEMPORARY, 1024 * 1024, function (fs) {
	fs.root.getFile(fileName, { create: true }, function (fileEntry) {

	#!/bin/bash

	set -e

	CONTENTS=$(tesseract -c language_model_penalty_non_dict_word=0.8 --tessdata-dir /usr/local/share/tessdata/ "$1" stdout -l eng \| xml esc)

	hex=$((cat <<EOF
	<?xml version="1.0" encoding="UTF-8"?>
	<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
	<plist version="1.0">

	inp = Input(shape=(maxlen,), name="text_input") # featureized text comes in here
	x = Embedding(embedding_matrix.shape[0], embed_size, weights=[embedding_matrix], trainable=True)(inp)
	x = Dense(some_num_here, activation="relu")(x)

	extra_data = Input(shape=(1,), name="extra_data") # your continous features comes in here

	combined = concatenate([x, extra_data])

	# maybe some ReLu + Dropout here

	/* jsmin.c
	2008-08-03

	Copyright (c) 2002 Douglas Crockford (www.crockford.com)

	Permission is hereby granted, free of charge, to any person obtaining a copy of
	this software and associated documentation files (the "Software"), to deal in
	the Software without restriction, including without limitation the rights to
	use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
	of the Software, and to permit persons to whom the Software is furnished to do

	git init
	git add .
	git commit -m "First commit"
	git remote add origin $GIT_REMOTE_ORIGIN
	git remote -v
	git pull --allow-unrelated-histories origin master
	git push -u origin master

	root=/path/to/root
	project=/path/to/project