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AWSTemplateFormatVersion: 2010-09-09
Description: Amazon EKS - Node Group
Parameters:
  KeyName:
    Description: The EC2 Key Pair to allow SSH access to the instances
    Type: 'AWS::EC2::KeyPair::KeyName'
  NodeImageId:
    Description: AMI id for the node instances.
    Type: 'AWS::EC2::Image::Id'
  NodeInstanceType:

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#!/bin/bash

set -e

sudo -u ec2-user -i <<'EOP'
## INSTALL THEIA IDE FROM SOURCE
EC2_HOME=/home/ec2-user
mkdir ${EC2_HOME}/theia && cd ${EC2_HOME}/theia
### begin by installing NVM, NodeJS v10, and Yarn
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.33.5/install.sh | bash

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#!/bin/bash
#
# This script takes about 5 minutes to run and will delay the availability of
# your SageMaker notebook.
# v1.3, release 05may2020
#
# THIS SCRIPT INSTALLS:
# - THEIA IDE w/ Jupyter Server proxy
# - UPGRADES JUPYTERLAB
# - INSTALL VOILA (https://github.com/voila-dashboards/voila)

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#!/bin/bash

###
# 
# The following script queries AWS CloudTrail for any events matching $USERNAME which occurred with the permissions
# associated with $ROLE_ARN.  All events that have occcurred as of $START_TIME (Unix timestamp in seconds) will
# be retrieved and the IAM actions of those events printed to STDOUT in a sorted and de-duplicated list.
#
# Sample Output:
#

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"""
This script is a simple FashionMNIST training script which uses MXNet's.
It has been orchestrated with SageMaker Debugger hook to allow saving tensors during training.
Here, the hook has been created using its constructor to allow running this locally for your experimentation.
When you want to run this script in SageMaker, it is recommended to create the hook from json file.
Please see scripts in either /examples/tensorflow/sagemaker_byoc or /examples/tensorflow/sagemaker_official_container
folder based on your use case.
"""
# Standard Library
import argparse

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Replace bastion hosts with AWS Session Manager https://reinvent2019.aws-management.tools/mgt406/en/

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Lab 1: Getting started

Create a Jupyter notebook

1. Visit https://github.com/awslabs/amazon-sagemaker-examples, at the bottom of the page you will find a link to create an Amazon SageMaker notebook
2. Follow the instructions using an ml.m5.2xlarge instance type.
3. When the notebook has been created click Open JupyterLab.

Clone the lab materials

Clone the Amazon SageMaker Examples to your notebook from GitHub.

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#!/bin/bash

# The following shell script uses Amazon Athena to query AWS CloudTrail logs for any occurrences of the
# action sagemaker:ListNotebookInstances.  The query returns the user identity who invoked the API, along
# with the region where the API was called.  A simple count of the number of invokes is outputted as a
# result.

SQL="SELECT useridentity.arn, eventname, eventsource, awsregion, sourceipaddress, errorcode, eventtime FROM cloudtrail_logs_account_logging WHERE eventsource = 'sagemaker.amazonaws.com' AND eventname in ('ListNotebookInstances') AND eventtime > '2020-04-15' LIMIT 3;"

EXEC_ID=$(aws athena start-query-execution --query-string "$SQL" --result-configuration OutputLocation=s3://my-s3-logging-bucket/queries --query 'QueryExecutionId' --output text)

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{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "VisualEditor0",
            "Effect": "Deny",
            "Action": "ec2:RunInstances",
            "Resource": "*"
        }
    ]

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#!/bin/bash

set -e

AWS_REGION=eu-west-2
SOURCE_DATABASE_NAME=database-1
RESTORE_TIME='2020-10-28T22:02:01+00:00'

NEW_DATABASE_NAME="rds-db-$RANDOM"