1. Resource Sharing for AI Training across Accounts in AWS Organizations


    To set up resource sharing for AI training across accounts in AWS Organizations, we utilize AWS Resource Access Manager (RAM) and AWS Organizations services. AWS RAM allows you to share your resources with any AWS account or within your AWS Organization. This could include sharing machine learning datasets, models, or compute resources.

    Below, I will guide you through the process of how to use Pulumi to share resources between accounts, and then how to organize these shared resources within AWS Organizations.

    First, we create an AWS RAM Resource Share and specify the resources we want to share. We define the AWS accounts within our organization with which we want to share these resources. Additionally, we will use AWS Organizations to create new accounts or manage policy-based access controls if necessary.

    Here's what each part of the process looks like in Pulumi using Python:

    1. Creating the Resource Share: We use aws.ram.ResourceShare to define what resources from our account we want to make available to others.

    2. Managing Accounts with AWS Organizations: With aws.organizations.Account, we can programmatically create new AWS accounts. If required, we can assign specific policies to these accounts using aws.organizations.Policy to control access.

    Let's write the Pulumi program in Python to accomplish the sharing:

    import pulumi import pulumi_aws as aws # We assume you have pre-configured the AWS provider and setup your AWS Organizations. # First, we'll create an AWS RAM resource share. # Replace 'shared_resource_arns' with the actual ARNs of the resources you want to share. resource_share = aws.ram.ResourceShare("aiTrainingResourceShare", name="AI-Training-Resource-Share", allow_external_principals=False, # Set to True if you want to share with accounts outside your organization. tags={ "Environment": "AI-Training" }, permission_arns=[ # Include the appropriate permission ARNs for the resources you are sharing. # For example, for sharing a specific EC2 AMI, include its Amazon Resource Name (ARN). "arn:aws:ec2:region:account-id:image/ami-id" ] ) # Output the Resource Share ARN for reference pulumi.export("resourceShareArn", resource_share.arn) # Now, let's create an AWS Organization Account to represent a team in the organization. # The email address should be unique for each account. team_account = aws.organizations.Account("aiTrainingTeamAccount", name="AI-Training-Team", email="ai-training-team@example.com", # Specify the parent ID if you're adding this account to a specific organizational unit (OU). # Otherwise, it will be added to the root OU. # parent_id="ou-parent-id" ) # Assign a policy to the team account, if needed. # Replace 'policy_content' with the actual JSON content of the policy. team_account_policy = aws.organizations.Policy("aiTrainingAccountPolicy", name="AI-Training-Account-Access-Policy", type="SERVICE_CONTROL_POLICY", content=policy_content # JSON string defining the policy ) # Attach the policy to the new account. policy_attachment = aws.organizations.PolicyAttachment("aiTrainingPolicyAttachment", policy_id=team_account_policy.id, target_id=team_account.id ) # Output the new account ID for reference pulumi.export("teamAccountId", team_account.id)

    This Pulumi program creates a resource share for AI training models and datasets while creating and managing policies for the team account within an AWS Organization.

    Now, let's assume you've already set up your Pulumi and AWS CLI with appropriate AWS access credentials and configured your default region. Once you have the program code ready:

    1. Save the code into a file named main.py.
    2. Run pulumi up to preview and deploy the changes.

    Ensure to replace placeholders like 'shared_resource_arns', 'policy_content', 'arn:aws:ec2:region:account-id:image/ami-id', and similar with the actual values you will use in your AWS setup.

    This program will create the necessary resources on AWS when you execute it, prompting for confirmation before making any changes. Please understand that operations involving AWS Organizations can make significant and potentially irreversible changes to your AWS environments, so proceed with caution and review AWS and Pulumi's documentation thoroughly.