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    1. pip install jupyterlab
        pip install tensorflow boto3 sagemaker
    2. Then type “jupyter Lab”, the notebook should open in the browser
        1. import tensorflow as tf
            msg = tf.constant('Ciao mondo')
            tf.print(msg)
    3. Check that everything works
    4. Now create a new Jupyter Notebook
        1. Download this: LINK (https://raw.githubusercontent.com/aws/amazon-sagemaker-examples/master/introduction_to_applying_machine_learning/breast_cancer_prediction/Breast%20Cancer%20Prediction.ipynb)
        2. Modify the Setup terminal

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EC2 Spot Workshop (EKS): https://ec2spotworkshops.com/using_ec2_spot_instances_with_eks.html

Spot Instance Advisor: https://aws.amazon.com/ec2/spot/instance-advisor/

Other best practices (e.g. Overprovisioner): https://aws.github.io/aws-eks-best-practices/cluster-autoscaling/#overprovisioning

DEMO

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import * as cdk from '@aws-cdk/core';
import * as neptune from '@aws-cdk/aws-neptune';


export class NeptuneTestStack extends cdk.Stack {
  constructor(scope: cdk.App, id: string, props?: cdk.StackProps) {
    super(scope, id, props);

    const dbClusterParameterGroup = new neptune.CfnDBClusterParameterGroup(this, 'neptune-ClusterParameterGroup', {
      description: 'test-description',

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import * as cdk from "@aws-cdk/core";
import * as glue from "@aws-cdk/aws-glue";

export class GlueStack extends cdk.Stack {
  constructor(scope: cdk.Construct, id: string, props?: cdk.StackProps) {
    super(scope, id, props);

    var workflow = new glue.CfnWorkflow(this, "glueWorkflow", {
      name: "testWorkflow",
    });

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const client = require('aws-iot-device-sdk');

// The options object to provision the MQTT client with.
// Update values between chevrons with the appropriate values.
const opts = {
  host: "<aws-iot-endpoint>",
  keyPath: "<path-to-private-key>",
  certPath: "<path-to-device-certificate>",
  caPath: "<path-to-root-ca>",
  // We are specifying that we want to connect on the

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const {toProxyOpts, proxyConnect } = require("./proxy-connect")

//  using it to connect to AWS IoT through a HTTP CONNECT proxy
const aws = require("aws-iot-device-sdk");

const proxyUrl = process.env.http_proxy || "http://localhost:3128/";

const opts = {
    keyPath: "assets/device.private.key",
    certPath: "assets/device.cert.pem",

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#!/usr/bin/env python
# Lambda Script for spot instances failover
# NOTE 1: Re-failover from on-demand to spot will be done if all desired ECS tasks are currently running
# NOTE 2: The maximum number of spot instances is taken from its DesiredCapacity
# WARNING: You cannot increase DesiredCapacity of on-demand from the console without changing this script,
#          however, you can increase DesiredCapacity of spot instances from the console and if it fails,
#          this script will start on-demand instead of spot instances
#
import boto3
import json

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$Username = "su"
$Password = "password"

$group = "Administrators"

$adsi = [ADSI]"WinNT://$env:COMPUTERNAME"
$existing = $adsi.Children | where {$_.SchemaClassName -eq 'user' -and $_.Name -eq $Username }

if ($existing -eq $null) {

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library(openxlsx)
library(plyr)

template <- "template.xlsx"
masterfile <-
  "master.xlsx"

dirpath <-
  "data/"

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var fs = require('fs');
var namesIn = "names.json"
var surnamesIn = "surnames.json"
var peopleOut = "peopleOut.json"

var namesFile = fs.readFileSync(namesIn, 'utf8');
var surnamesFile = fs.readFileSync(surnamesIn, 'utf8');

namesJson = JSON.parse(namesFile);
surnamesJson = JSON.parse(surnamesFile);