AWS DeepRacer - Hands-on Exercise 1 : Model Training Using AWS DeepRacer Console

Hands-on Exercise 1: Model Training Using AWS DeepRacer Console

 

This is the first of four exercises that you will encounter in this course. This first exercise guides you through building, training, and evaluating your first RL model using the AWS DeepRacer console. To access the instructions for three of these exercises, download and unzip this course package. For this particular exercise, find and open the relevant PDF file and follow the steps within to complete the exercise.

*Note: This exercise is designed to be completed in your AWS account. AWS DeepRacer is part of AWS Free Tier, so you can get started at no cost. For the first month after sign-up, you are offered a monthly free tier of 10 hours of Amazon SageMaker training and 60 simulation units of Amazon RoboMaker (enough to cover 10 hours of training). If you go beyond those free tier limits, you will accrue additional costs. For more information, see the AWS DeepRacer Pricing page.

 

Hands-on Exercise 1- Model Training Using AWS DeepRacer Console.pdf

0.23MB

 

 

 

 

Hands-on Exercise 2- Advanced Model Training Using AWS DeepRacer Console.pdf

0.25MB

 

 

For feedback, suggestions, or corrections, email us at aws-course-feedback@amazon.com.

 

 

Hands-on Exercise 3- Distributed AWS DeepRacer RL Training using Amazon SageMaker and AWS RoboMaker.pdf

0.46MB

 

SageMakerForDeepRacerSetup.yaml

0.01MB

 

AWSTemplateFormatVersion: "2010-09-09"
Description: 'AWS DeepRacer: Driven by Reinforcement Learning'
Parameters:
  SagemakerInstanceType:
    Description: 'Machine Learning instance type that should be used for Sagemaker Notebook'
    Type: String
    AllowedValues:
      - ml.t2.medium
      - ml.t2.large
      - ml.t2.xlarge
      - ml.t3.medium
      - ml.t3.large
      - ml.t3.xlarge
      - ml.m5.xlarge
    Default: ml.t3.medium
  CreateS3Bucket:
    Description: Create and use a bucket created via this template for model storage
    Default: True
    Type: String
    AllowedValues:
      - True
      - False
    ConstraintDescription: Must be defined at True|False.
  VPCCIDR:
    Description: 'CIDR Block for VPC (Do Not Edit)'
    Type: String
    Default: 10.96.0.0/16
  PUBSUBNETA:
    Description: 'Public Subnet A (Do Not Edit)'
    Type: String
    Default: 10.96.6.0/24
  PUBSUBNETB:
    Description: 'Public Subnet B (Do Not Edit)'
    Type: String
    Default: 10.96.7.0/24
  PUBSUBNETC:
    Description: 'Public Subnet C (Do Not Edit)'
    Type: String
    Default: 10.96.8.0/24
  PUBSUBNETD:
    Description: 'Public Subnet D (Do Not Edit)'
    Type: String
    Default: 10.96.9.0/24
  S3PathPrefix:
    Type: String
    Description: 'Bootstrap resources prefix'
    Default: 'awsu-spl-dev/spl-227'
  S3ResourceBucket:
    Type: String
    Description: 'Bootstrap S3 Bucket'
    Default: 'aws-training'
Conditions:
  CreateS3Bucket: !Equals [ !Ref CreateS3Bucket, True ]
  #  NoCreateS3Bucket: !Equals [ !Ref CreateS3Bucket, False ]
Resources:

# Defining the VPC Used for the sanbox ENV, and notebook instance
  VPC:
    Type: 'AWS::EC2::VPC'
    Properties:
      CidrBlock: !Ref VPCCIDR
      EnableDnsSupport: 'true'
      EnableDnsHostnames: 'true'
      Tags:
        - Key: Name
          Value: 'DeepRacer Sandbox'
# There is a few calls made to public to download supporting resources
  InternetGateway:
    Type: 'AWS::EC2::InternetGateway'
    DependsOn: VPC
    Properties:
      Tags:
        - Key: Name
          Value: 'DeepRacer Sandbox IGW'
# Attached this IGW to the sanbox VPC
  AttachGateway:
    Type: 'AWS::EC2::VPCGatewayAttachment'
    DependsOn:
      - VPC
      - InternetGateway
    Properties:
      VpcId: !Ref VPC
      InternetGatewayId: !Ref InternetGateway
# Default setting in the notebook is to use Public IP address to communicate
# between instances running the simulation, and the instances collecting and
# processing. A NatGW could have been used with added costs, but would allow for
# use of private IP address.

# Found in testing that not all ML instance types may not be deployed or avaliable
# in all AZ's within a given region. We are using the newest instance family of T3
  PublicSubnetA:
    Type: 'AWS::EC2::Subnet'
    DependsOn: VPC
    Properties:
      VpcId: !Ref VPC
      CidrBlock: !Ref PUBSUBNETA
      AvailabilityZone: !Select
        - '0'
        - !GetAZs ''
      Tags:
        - Key: Name
          Value: 'Deepracer Sandbox - Public Subnet - A'
  PublicSubnetB:
    Type: 'AWS::EC2::Subnet'
    DependsOn: VPC
    Properties:
      VpcId: !Ref VPC
      CidrBlock: !Ref PUBSUBNETB
      AvailabilityZone: !Select
        - '1'
        - !GetAZs ''
      Tags:
        - Key: Name
          Value: 'Deepracer Sandbox Public Subnet - B'
  PublicSubnetC:
    Type: 'AWS::EC2::Subnet'
    DependsOn: VPC
    Properties:
      VpcId: !Ref VPC
      CidrBlock: !Ref PUBSUBNETC
      AvailabilityZone: !Select
        - '2'
        - !GetAZs ''
      Tags:
        - Key: Name
          Value: 'Deepracer Sandbox Public Subnet - C'
  PublicSubnetD:
    Type: 'AWS::EC2::Subnet'
    DependsOn: VPC
    Properties:
      VpcId: !Ref VPC
      CidrBlock: !Ref PUBSUBNETD
      AvailabilityZone: !Select
        - '3'
        - !GetAZs ''
      Tags:
        - Key: Name
          Value: 'Deepracer Sandbox Public Subnet - D'
# Define the Public Routing Table
  PublicRouteTable:
    Type: 'AWS::EC2::RouteTable'
    DependsOn:
      - VPC
      - AttachGateway
    Properties:
      VpcId: !Ref VPC
      Tags:
        - Key: Name
          Value: 'Deepracer Sandbox Public Routing Table'
# And add in the default route to 0.0.0.0/0
  PublicRouteIGW:
    Type: 'AWS::EC2::Route'
    DependsOn:
      - PublicRouteTable
      - InternetGateway
    Properties:
      RouteTableId: !Ref PublicRouteTable
      DestinationCidrBlock: 0.0.0.0/0
      GatewayId: !Ref InternetGateway
# Attach the routing table to each of the subnets
  PublicRouteTableAssociationA:
    Type: 'AWS::EC2::SubnetRouteTableAssociation'
    Properties:
      SubnetId: !Ref PublicSubnetA
      RouteTableId: !Ref PublicRouteTable
  PublicRouteTableAssociationB:
    Type: 'AWS::EC2::SubnetRouteTableAssociation'
    Properties:
      SubnetId: !Ref PublicSubnetB
      RouteTableId: !Ref PublicRouteTable
  PublicRouteTableAssociationC:
    Type: 'AWS::EC2::SubnetRouteTableAssociation'
    Properties:
      SubnetId: !Ref PublicSubnetC
      RouteTableId: !Ref PublicRouteTable
  PublicRouteTableAssociationD:
    Type: 'AWS::EC2::SubnetRouteTableAssociation'
    Properties:
      SubnetId: !Ref PublicSubnetD
      RouteTableId: !Ref PublicRouteTable
# Define a S3 endpoint for all the S3 traffic during training
  S3Endpoint:
    Type: AWS::EC2::VPCEndpoint
    Properties:
      VpcId: !Ref VPC
      RouteTableIds:
        - !Ref PublicRouteTable
      ServiceName: !Join
        - ''
        - - com.amazonaws.
          - !Ref 'AWS::Region'
          - .s3
      PolicyDocument:
        Version: 2012-10-17
        Statement:
          - Effect: Allow
            Principal: '*'
            Action:
              - 's3:*'
            Resource:
              - '*'
# This exercise is going to need a bucket to store any file generated from training
# There is a conditions to evaluate if the PRAM is true, else this resource would
# not be created.
  SandboxBucket:
    Type: 'AWS::S3::Bucket'
    DeletionPolicy: Retain
    Condition: CreateS3Bucket
    Properties:
      BucketName:
        Fn::Join:
          - "-"
          - - deepracer-trainingexercise
            - Ref: AWS::Region
            - Ref: AWS::AccountId
# Sagemaker is going to be making calls to Robomaker to launch the sim, and
# Sagemaker to launch the training insance. This requries AWS credentals. A
# Principal of sagemaker and robomaker needs to be assiged as both service will
# assuming this role. Default Sagemaker full access and s3 access is needed.
  SageMakerNotebookInstanceRole:
    Type: 'AWS::IAM::Role'
    Properties:
      AssumeRolePolicyDocument:
        Version: 2012-10-17
        Statement:
          - Effect: Allow
            Principal:
              Service:
                - sagemaker.amazonaws.com
                - robomaker.amazonaws.com
            Action:
              - 'sts:AssumeRole'
      ManagedPolicyArns:
        - 'arn:aws:iam::aws:policy/AmazonSageMakerFullAccess'
      Path: /
      Policies:
        - PolicyName: DeepRacerPolicy
          PolicyDocument:
            Version: 2012-10-17
            Statement:
              - Effect: Allow
                Action: [ 's3:*',
                          'iam:GetRole' ]
                Resource: '*'
# This is how the notebook gets loaded on to sagemaker. There is a zip file with
# with the needed files, and a second http call to pull down the notebook.
# This is only done "OnCreate" - when the sagemaker instance is first deployed
# You can can the script get run "OnStart" (when a sagemaker instance changes
# from a stopped state to a running state). This would automaticlly update file
# to be the latest form source, but could over write changes applied during
# your testing
  SageMakerNotebookInstanceLifecycleConfig:
    Type: 'AWS::SageMaker::NotebookInstanceLifecycleConfig'
    Properties:
  #    OnStart:
  #      - Content:
  #          Fn::Base64:
  #            #!/bin/bash
  #            !Sub |
  #            cd SageMaker
  #            chown ec2-user:ec2-user -R /home/ec2-user/SageMaker

      OnCreate:
        - Content:
            Fn::Base64:
              !Sub |
              cd SageMaker
              curl -O https://us-west-2-${S3ResourceBucket}.s3.amazonaws.com/${S3PathPrefix}/scripts/rl_deepracer_robomaker_coach.ipynb
              curl -O https://us-west-2-${S3ResourceBucket}.s3.amazonaws.com/${S3PathPrefix}/scripts/rl_deepracer_robomaker_coach.zip
              unzip rl_deepracer_robomaker_coach.zip
              chown ec2-user:ec2-user -R /home/ec2-user/SageMaker
# Security Group for sagemaker instance running in this VPC
  SagemakerInstanceSecurityGroup:
    Type: AWS::EC2::SecurityGroup
    Properties:
      GroupDescription: Sagemaker Security Group
      VpcId: !Ref VPC
      SecurityGroupIngress:
      - IpProtocol: tcp
        FromPort: 1
        ToPort: 65535
        CidrIp: !Ref VPCCIDR
      - IpProtocol: udp
        FromPort: 1
        ToPort: 65535
        CidrIp: !Ref VPCCIDR
      SecurityGroupEgress:
      - IpProtocol: tcp
        FromPort: 1
        ToPort: 65535
        CidrIp: !Ref VPCCIDR
      - IpProtocol: udp
        FromPort: 1
        ToPort: 65535
        CidrIp: !Ref VPCCIDR
# Creating the Sagemaker Notebook Instance
  SageMakerNotebookInstance:
    Type: 'AWS::SageMaker::NotebookInstance'
    Properties:
      #NotebookInstanceName: 'DeepracerSagemakerSandbox'
      NotebookInstanceName: !Join ["-", ["DeepRacerSagemakerSandbox", !Ref "AWS::StackName"]]
      SecurityGroupIds:
        - !GetAtt
          - SagemakerInstanceSecurityGroup
          - GroupId
      InstanceType: !Ref SagemakerInstanceType
      SubnetId: !Ref PublicSubnetA
      Tags:
        - Key: Name
          Value: 'DeepRacer Sandbox'
      LifecycleConfigName: !GetAtt
          - SageMakerNotebookInstanceLifecycleConfig
          - NotebookInstanceLifecycleConfigName
      RoleArn: !GetAtt
          - SageMakerNotebookInstanceRole
          - Arn
Outputs:
  # Display the name of the bucekt that was created from this CFN Stack
    ModelBucket:
      Condition: CreateS3Bucket
      Value: !Ref SandboxBucket
  # URL to get to the Sagemaker UI, and find the Jupyter button. 
    SagemakerNotebook:
      Value:
        !Sub |
          https://console.aws.amazon.com/sagemaker/home?region=${AWS::Region}#/notebook-instances/${SageMakerNotebookInstance.NotebookInstanceName}