Automated Patch Management for AI Training Clusters

Question

Pulumi · Accepted Answer

Automated patch management is essential for maintaining the security and performance of AI training clusters. In cloud environments, such as AWS, automated patch management can be handled by a combination of Amazon EC2 instances for the computing nodes and Amazon SSM (Systems Manager) for orchestration of the patching process.

Pulumi, with its Infrastructure as Code (IaC) capabilities, can be used to automate the provisioning and management of these resources, ensuring that your AI training clusters are always up to date with the latest patches.

Below is a Pulumi program written in Python that sets up automated patch management for AI training clusters on AWS:

1. **EC2 Instances** - These are the virtual servers where your AI training workload will be running. Pulumi can provision these instances and configure them based on your requirements.
2. **SSM Patch Baseline** - This resource defines which patches are approved for installation on your instances. With Pulumi, you can define a custom patch baseline or use AWS's default one.
3. **SSM Maintenance Window** - This is a scheduled window to run maintenance tasks, such as installing patches. Pulumi allows you to define a maintenance window, including which targets the maintenance tasks should run on.
4. **SSM Maintenance Window Task** - This resource assigns the task of patching to the maintenance window. You can specify the patch baseline to use and the type of task, which in this case is `RUN_COMMAND`, for executing the patching command.

Let's go through the Pulumi Python program that sets up automated patch management for your AI training clusters:

```python
import pulumi
import pulumi_aws as aws

# Define the custom SSM Patch Baseline if needed, or use AWS's default one.
patch_baseline = aws.ssm.PatchBaseline("custom-patch-baseline",
    operating_system="AMAZON_LINUX_2",
    approval_rules=[aws.ssm.PatchBaselineApprovalRuleArgs(
        approve_after_days=4,
        patch_filter_group=aws.ssm.PatchBaselineApprovalRulePatchFilterGroupArgs(
            patch_filters=[
                aws.ssm.PatchBaselineApprovalRulePatchFilterGroupPatchFilterArgs(
                    key="PRODUCT",
                    values=["AmazonLinux2"]
                ),
                aws.ssm.PatchBaselineApprovalRulePatchFilterGroupPatchFilterArgs(
                    key="SEVERITY",
                    values=["CRITICAL"]
                ),
            ],
        ),
    )]
)

# Define the SSM Maintenance Window for when the patches should be installed.
maintenance_window = aws.ssm.MaintenanceWindow("patch-maintenance-window",
    schedule="cron(0 2 ? * SUN *)", # Runs every Sunday at 2:00 AM
    duration=3,                    # Maintenance window lasts for 3 hours
    cutoff=1,                      # Stop scheduling new tasks 1 hour before the end of the window
    allow_unassociated_targets=False # Do not allow instances that do not match the target criteria
)

# Define the EC2 Instances for the AI Training Cluster.
# Here we create a single instance for illustration purposes, but in practice,
# you might create an Auto Scaling group or a set of instances.
training_instance = aws.ec2.Instance("ai-training-cluster-instance",
    instance_type="t3.medium", # Choose an appropriate instance type
    ami="ami-0c55b159cbfafe1f0", # Replace with the correct AMI for your region and OS
    tags={
        "Patch Group": "train-cluster-patch-group" # Tag used to associate with the SSM Maintenance Window Target
    }
)

# Define the SSM Maintenance Window Target to specify which instances are targeted for patching.
maintenance_window_target = aws.ssm.MaintenanceWindowTarget("patch-target",
    window_id=maintenance_window.id,
    resource_type="INSTANCE",
    targets=[aws.ssm.MaintenanceWindowTargetTargetArgs(
        key="tag:Patch Group",
        values=["train-cluster-patch-group"]
    )]
)

# Define the SSM Maintenance Window Task to run patching during the maintenance window.
maintenance_window_task = aws.ssm.MaintenanceWindowTask("patch-task",
    window_id=maintenance_window.id,
    targets=[aws.ssm.MaintenanceWindowTaskTargetArgs(
        key="WindowTargetIds",
        values=[maintenance_window_target.id]
    )],
    task_type="RUN_COMMAND",
    task_arn="AWS-RunPatchBaseline", # AWS defined Automation task for patching
    service_role_arn=aws_iam_role.ssm_role.arn, # IAM Role with permissions for SSM to perform patching
    task_parameters={
        "Operation": "Install"
    },
    max_concurrency="2", # Define concurrency, example: "2 instances at a time"
    max_errors="1" # Define max errors before stopping the task, example: "1%"
)

# Export the URL of the instance, for example, to access logs or for SSH (if configured)
pulumi.export("instance_url", training_instance.public_dns)
```

**What's happening in the program:**

1. We create a custom `PatchBaseline` for Amazon Linux 2 with critical severity patches approved after 4 days.
2. We set up a `MaintenanceWindow` to run every Sunday at 2:00 AM that lasts for 3 hours and stops scheduling new tasks 1 hour before it ends.
3. An EC2 `Instance` is provisioned and tagged appropriately to be targeted for patching.
4. `MaintenanceWindowTarget` is defined to apply to instances with the specified tag.
5. `MaintenanceWindowTask` is created to execute the AWS-RunPatchBaseline task during the maintenance window, which applies the approved patches.

This IaC makes it easy to manage the patching of your AI training clusters, ensuring they remain secure and reliable without manual intervention. You can expand this setup by adding more instances or configuring auto-scaling groups as needed for your specific AI workloads.