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AWS Research Workshops

This repo provides a managed SageMaker jupyter notebook with a number of notebooks for hands on workshops in data lakes, AI/ML, Batch, IoT, and Genomics.

Quickstart

To get the AWS Research Workshop Notebook up and running in your own AWS account, follow these steps (if you do not have an AWS account, please see How do I create and activate a new Amazon Web Services account?):

1. Save the research-env.yml to local file system
2. Log into the AWS console if you are not already
3. Choose Launch Stack to open the AWS CloudFormation console and create a new stack.
   
4. Continue through the CloudFormation wizard steps
   1. Name your stack, i.e. ResearchWorkshopNotebook
   2. Select "Upload a template file" and use research-env.yml as the template file
   3. After reviewing, check the blue box for creating IAM resources.
5. Choose Create stack. This will take ~20 minutes to complete.
6. The output of the CloudFormation stack creation will provide a Notebook URL (in the Outputs section of your stack details

Workshops

Please review and complete all prerequisites before attempting these workshops.

Prerequisites

AWS Account

In order to complete these workshops you'll need a valid, usable AWS Account with Admin permissions. The code and instructions in these workshops assume only one student is using a given AWS account at a time. If you try sharing an account with another student, you'll run into naming conflicts for certain resources.

Use a personal account or create a new AWS account to ensure you have the neccessary access. This should not be an AWS account from the company you work for.

If you are doing this workshop as part of an AWS sponsored event, you will receive credits to cover the costs.

Browser

We recommend you use the latest version of Chrome or Firefox to complete this workshop.

Text Editor

For any workshop module that requires use of the AWS Command Line Interface (see above), you also will need a plain text editor for writing scripts. Any editor that inserts Windows or other special characters potentially will cause scripts to fail.

IAM Role for Notebook Instance

A new IAM Role will be required for the workshops. The Notebook Instance requires sagemaker.amazonaws.com and glue.amazonaws.com trust permissions and the AdministratorAccess policy to access the required services in the workshops. Follow the instruction below to create the role in Python or use the research-env.yml file in CloudFormation to launch the notebook.

import logging
import os
import time
import argparse
import botocore.session
import botocore.exceptions

def create_role(iam, policy_name, assume_role_policy_document, inline_policy_name=None, policy_str=None):
    """Creates a new role if there is not already a role by that name"""
    if role_exists(iam, policy_name):
        logging.info('Role "%s" already exists. Assuming correct values.', policy_name)
        return get_role_arn(iam, policy_name)
    else:
        response = iam.create_role(RoleName=policy_name,
                                   AssumeRolePolicyDocument=assume_role_policy_document)
        
        if policy_str is not None:
            iam.put_role_policy(RoleName=policy_name,
                            PolicyName=inline_policy_name, PolicyDocument=policy_str)
        logging.info('response for creating role = "%s"', response)
        return response['Role']['Arn']

def role_exists(iam, role_name):
    """Checks if the role exists already"""
    try:
        iam.get_role(RoleName=role_name)
    except botocore.exceptions.ClientError:
        return False
    return True

def get_role_arn(iam, role_name):
    """Gets the ARN of role"""
    response = iam.get_role(RoleName=role_name)
    return response['Role']['Arn']

iam = boto3.client('iam')

role_doc = {
        "Version": "2012-10-17", 
        "Statement": [
            {"Sid": "", 
             "Effect": "Allow", 
             "Principal": {
                 "Service": [
                     "sagemaker.amazonaws.com",
                     "glue.amazonaws.com"
                 ]
             }, 
             "Action": "sts:AssumeRole"
        }]
    }

inline_policy = {
        "Version": "2012-10-17",
        "Statement": [
            {
                "Action": [
                    "*",
                    "*"
                ],
                "Resource": [
                    "*"
                ],
                "Effect": "Allow"
            }
        ]
    }

role_arn = workshop.create_role(iam, firehose_role_name, json.dumps(role_doc), firehose_policy_name, json.dumps(inline_policy))
print(role_arn)

Launching Research Notebook Instance

SageMaker provides hosted Jupyter notebooks that require no setup, so you can begin processing your training data sets immediately. With a few clicks in the SageMaker console, you can create a fully managed notebook instance, pre-loaded with useful libraries for machine learning.

Accessing the Notebook Instance

1. Wait for the server status to change to InService. This will take several minutes but likely less.

2. Click Open. You will now see the Jupyter homepage for your notebook instance.

License Summary

This sample code is made available under a modified MIT license. See the LICENSE file.