Although not machine related it can help you with your EC2 environment if you are planning to use AWS ML, this set up was very handy for me to help me speed up my Python deployments to EC2 instances in no time.

#Set up a source repository In my case, I didn’t use any in particular but S3 Buckets, in this Create Bucket with policy from bucket-policy.txt, be sure to change the principal for your account

Create the service role for CodeDeploy to allow it read the EC2 tags Service Role/create-role.bat

Attach the managed policy AWSCodeDeployRole to allow the reading of EC2 tags Service Role/attach-role-policy.bat

Create the instance profile for the EC2 instance to be able to pull objects from S3 InstanceProfile/create-role.bat InstanceProfile/put-role-policy.bat InstanceProfile/create-instance-profile.bat

Create the EC2 instance with the Instance Profile associated, assign this EC2 instance the tags Key=Name,Value=CodeDeployDemo

Install the CodeDeploy Agent on the EC2 instance Set-ExecutionPolicy RemoteSigned Import-Module AWSPowerShell New-Item –Path “c:\temp” –ItemType “directory” -Force powershell.exe -Command Read-S3Object -BucketName aws-codedeploy-us-west-1 -Key latest/codedeploy-agent.msi -File c:\temp\codedeploy-agent.msi c:\temp\codedeploy-agent.msi /quiet /l c:\temp\host-agent-install-log.txt

Create the CodeDeploy Application create-application.bat

Create the deployment group specifying the ARN of the ServiceRole, be sure to change the ARN on the script create-deployment-group.bat

When a revision is ready to run this script to zip the folder and push it to the BT Bucket BTApplication/deploy-push.bat

To start the deployment of the uploaded object on S3 run: create-deployment.bat