1. Patch Management for AI Training Clusters using AWS SSM


    Patch management is an essential part of maintaining the security and performance of any cloud infrastructure. It involves regularly updating software on your systems to patch vulnerabilities, fix bugs, and improve functionality. AWS Systems Manager (SSM) is a powerful service that provides visibility and control over your AWS infrastructure and can automate the patching process.

    In this context, managing patches for AI training clusters likely involves a fleet of EC2 instances that run compute-intensive workloads. AWS SSM includes a feature called Patch Manager, which automates the process of patching managed instances with both security-related and other types of updates.

    Below is a Pulumi program written in Python that sets up AWS SSM to manage patches for an AI training cluster. This program will create the following resources:

    1. An SSM Patch Baseline: Defines which patches are approved for your instances.
    2. An SSM Patch Group: Groups together instances that should have the same patch baseline applied.
    3. An SSM Association: Associates the patch baseline with the patch group and specifies a schedule for patching.
    4. An SSM Maintenance Window (optional): Defines a specific time window during which patching should occur to avoid disrupting operations.

    Here's the program:

    import pulumi import pulumi_aws as aws # AWS Systems Manager Patch Manager requires SSM Agent to be installed and running # on each EC2 instance. Ensure your AI Training cluster instances have the agent # installed before using the following setup. # Create a Patch Baseline for your instances patch_baseline = aws.ssm.PatchBaseline("ai-training-patch-baseline", operating_system="AMAZON_LINUX_2", # Change as needed for your operating system approved_patches=["arn:aws:ssm:us-west-2:123456789012:patchbaseline/SOME-PATCH-BASELINE"], approval_rules=[ aws.ssm.PatchBaselineApprovalRuleArgs( approve_after_days=7, # Automatically approve patches 7 days after release compliance_level="CRITICAL", # Set the compliance severity level patch_filters=[ aws.ssm.PatchBaselineApprovalRulePatchFilterArgs( key="CLASSIFICATION", values=["CriticalUpdates"], ), ], ), ], ) # Create a Patch Group and associate it with the Patch Baseline patch_group = aws.ssm.PatchGroup("ai-training-patch-group", baseline_id=patch_baseline.id, # The ID of the patch baseline resource we created patch_group="ai-training-cluster-group", # Name of the patch group ) # Create an SSM Association for patching ssm_association = aws.ssm.Association("ai-training-patch-association", name="AWS-RunPatchBaseline", # Predefined SSM Document for patching targets=[ aws.ssm.AssociationTargetArgs( key="tag:PatchGroup", # Use tags to target the instances in the patch group values=["ai-training-cluster-group"], # Tag value matches the patch group name ), ], ) # Optionally, define a Maintenance Window if needed for controlled patch timings maintenance_window = aws.ssm.MaintenanceWindow("ai-training-maintenance-window", schedule="cron(0 4 ? * SUN *)", # Example cron expression for 4 AM every Sunday duration=3, # Maintenance window duration in hours cutoff=1, # Time before the end of the window to stop scheduling new tasks ) # Now, create a target mapping for Maintenance Window and Patch Group maintenance_window_target = aws.ssm.MaintenanceWindowTarget("ai-training-maintenance-target", window_id=maintenance_window.id, # The ID of the maintenance window resource resource_type="INSTANCE", targets=[ aws.ssm.MaintenanceWindowTargetTargetArgs( key="tag:PatchGroup", values=["ai-training-cluster-group"], ), ], ) # Link the Maintenance Window with the Patching Association maintenance_window_task = aws.ssm.MaintenanceWindowTask("ai-training-maintenance-task", window_id=maintenance_window.id, targets=[ aws.ssm.MaintenanceWindowTaskTargetArgs( key="WindowTargetIds", values=[maintenance_window_target.id], ), ], task_type="RUN_COMMAND", task_arn=ssm_association.name, # ARN of the patching association service_role_arn="arn:aws:iam::123456789012:role/MaintenanceWindowRole", # IAM Role with permissions max_concurrency="2", # How many targets to patch at the same time max_errors="1", # Stop patching if an error occurs on a target ) # Export the ARN of the created resources for reference pulumi.export('patch_baseline_arn', patch_baseline.arn) pulumi.export('patch_group_name', patch_group.patch_group) pulumi.export('ssm_association_id', ssm_association.id) pulumi.export('maintenance_window_id', maintenance_window.id)

    This program is intended for use as part of an existing Pulumi project configured with the proper AWS credentials. It assumes your compute instances are tagged correctly to be associated with the SSM patch group, and it uses a simple cron expression to define a weekly patching schedule.

    Remember to adjust the operating_system, AWS region, patch classification, IAM role ARNs, and other parameters as needed to fit your deployment. Review the AWS SSM documentation on Patch Manager and be mindful of the cron expression to ensure patching happens during a time that's appropriate for your workload and business requirements.