1. Facilitating Cross-Service AI Workload Management with AWS Resource Groups


    In AWS, a Resource Group is a collection of resources that share one or more tags or portions of tags. This makes it easier to manage and automate tasks on these grouped resources within the AWS ecosystem. You might create a Resource Group to group all your AI workload-related resources, which can include things like Amazon EC2 instances, Amazon S3 buckets, AWS Lambda functions, and so on.

    Why is this beneficial? Grouping your services into a Resource Group allows for more streamlined management, including:

    • Simplified bulk actions with the AWS Command Line Interface (CLI) or with scripts.
    • Collective viewing metrics and alarms in Amazon CloudWatch.
    • Centralized management to apply updates or policies.

    Below I will show you how to create and manage an AWS Resource Group using Pulumi in Python. We'll create a simple Resource Group, which targets resources that share a specific tag. The tag will be workload-type: ai, which we'll assume is on every resource we want to include. Here's a step-by-step guide:

    1. First, I'll set up the aws-native provider for Pulumi, which we’ll use to interact with AWS resources. The aws-native provider offers access to the full breadth of AWS services with same-day support for new AWS features.

    2. Next, I will create a Resource Group named ai-workload-group that groups together all resources tagged with workload-type: ai.

    Here is the Pulumi program that accomplishes that:

    import pulumi import pulumi_aws_native as aws_native # Create a resource group for AI workload management ai_workload_group = aws_native.resourcegroups.Group("ai-workload-group", # The name is a user-defined identifier for the resource group. name="AIWorkloadManagementGroup", # Description for the resource group description="Resource group for AI workload management", # Tag-based resource query resource_query=aws_native.resourcegroups.GroupResourceQueryArgs( type="TAG_FILTERS_1_0", query=aws_native.resourcegroups.GroupResourceQueryQueryArgs( tag_filters=[ # Tag filter for grouping all resource with given tags aws_native.resourcegroups.GroupResourceQueryQueryTagFilterArgs( key="workload-type", values=["ai"], ), ], ), ) ) # Export the ARN of the resource group for reference pulumi.export('ai_workload_group_arn', ai_workload_group.arn)

    In this code:

    • We're importing pulumi and pulumi_aws_native, which are the Pulumi SDK and the AWS native Pulumi provider, respectively.
    • We're defining a resource of type aws_native.resourcegroups.Group, which corresponds to an AWS Resource Group.
    • The name parameter sets the name of the Resource Group.
    • The description parameter provides a brief description of the Resource Group’s purpose.
    • The resource_query parameter defines how AWS should identify resources to include in the group. In this case, we're using a tag filter TAG_FILTERS_1_0 to include all resources tagged with workload-type: ai.
    • Finally, we're exporting the Amazon Resource Name (ARN) of the Resource Group so that you can use it in other contexts if needed.

    This Pulumi program will create an AWS Resource Group that simplifies the management of all services related to your AI workload by grouping them based on a common tag. Now you can use this group for collective actions and management through the AWS Management Console or programmatic interfaces. Remember to run pulumi up to deploy these changes to your AWS account.