jthomas

{
  "name": "tf-js",
  "version": "1.0.0",
  "main": "script.js",
  "license": "MIT",
  "dependencies": {
    "@tensorflow-models/mobilenet": "^0.2.2",
    "@tensorflow/tfjs": "^0.12.3",
    "@tensorflow/tfjs-node": "^0.1.9",
    "jpeg-js": "^0.3.4"

jthomas

OpenWhisk Workshop

Hello 👋.

This workshop will teach you how to develop serverless applications, composed of loosely coupled microservice-like functions, using an open-source serverless platform.

Starting with getting the development environment set up, it'll move onto creating, deploying and invoking serverless functions for multiple runtimes. Once you are comfortable creating serverless functions, the next step is to connect functions to events, like message queues, allowing microservices to fire automatically. Finally, we'll demonstrate how to expose serverless functions as public API endpoints, allowing to build serverless web applications.

Welcome to the future of cloud development, you'll never want to manage another server again 😎.

jthomas

var iterations = 0

var print_board = function (columns) {
  var n = columns.length, row = 0, col = 0
  while (row < n) {
    while (col < n) {
      process.stdout.write(columns[row] === col ? 'Q ' : '# ')
      col++
    }

jthomas

'use strict';

const { Worker } = require('worker_threads');
const os = require('os')
const threadCount = os.cpus().length

const compute_primes = async (start, range) => {
  return new Promise((resolve, reject) => {
    let primes = []
    console.log(`adding worker (${start} => ${start + range})`)

jthomas

const INDEX_DOCUMENT = 'index.html'
const ERROR_DOCUMENT = '404.html'

addEventListener('fetch', event => {
  event.respondWith(handleRequest(event.request))
})

/**
 * @param {Request} request
 */

jthomas

{
  "annotations": {
    "list": []
  },
  "editable": true,
  "gnetId": null,
  "graphTooltip": 0,
  "hideControls": false,
  "id": 2309,
  "links": [],

jthomas

const tf = require('@tensorflow/tfjs')
const mobilenet = require('@tensorflow-models/mobilenet');
require('@tensorflow/tfjs-node')

const jpeg = require('jpeg-js');

const NUMBER_OF_CHANNELS = 3
const MODEL_PATH = 'mobilenet/model.json'

let mn_model

jthomas

$ emcc -s WASM=1 -s SIDE_MODULE=1 -s EXPORTED_FUNCTIONS="['_add']" -O1 add.c -o add.wasm
$ zip action.zip index.js add.wasm package.json
updating: index.js (deflated 52%)
updating: add.wasm (deflated 7%)
updating: package.json (deflated 15%)
$ ibmcloud wsk action create wasm action.zip --kind nodejs:10
ok: created action wasm
$ ibmcloud wsk action invoke wasm -r -p a 2 -p b 2
{

jthomas

"use strict";

// What percentage of invocations should randomly fail?
const ERROR_RATE = 0.25

// Random delay to results being returned (0 -> 10 seconds)
const DELAY_MS = Math.random() * 10000

function should_fail () {
  return Math.random() < ERROR_RATE

jthomas

"use strict";

const ERROR_RATE = 0.25

function should_fail () {
  return Math.random() < ERROR_RATE
}

function main(params) {
  if (!params.a || !params.b) throw new Error('Missing input parameters (a or b).')