Periodic AI Model Retraining Scheduling via AWS SSM

Question

Pulumi · Accepted Answer

To achieve periodic AI model retraining using AWS Simple Systems Manager (SSM), we would use a combination of AWS services orchestrated through Pulumi. AWS SSM, particularly the Maintenance Window and Association resources, allows us to define and schedule tasks that can be run across AWS resources, including EC2 instances or ECS clusters, which can be used for model training jobs.

The general idea is to leverage the SSM Maintenance Window to define a time frame in which operations on the instance should be performed, such as retraining an AI model. The Maintenance Window Task is then associated with this window and would define the specific actions to execute—such as running a script to retrain the model.

The SSM Association resource allows us to automatically apply SSM Documents, which can be scripts or commands, to a set of instances at a specified frequency.

Below you'll find a Python program written using Pulumi that sets up:

1. An SSM Maintenance Window to schedule when the model training should occur.
2. An SSM Maintenance Window Task that specifies what script to run for the model retraining.
3. An IAM role that the SSM Task can assume to perform actions on other AWS services if necessary.

Please ensure that you have the AWS CLI configured and Pulumi installed and set up before running this program.

```python
import pulumi
import pulumi_aws as aws

# IAM role for SSM to assume during maintenance tasks - you might need additional policies for actual model training
ssm_role = aws.iam.Role("ssmRole",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [{
            "Effect": "Allow",
            "Principal": {"Service": "ssm.amazonaws.com"},
            "Action": "sts:AssumeRole"
        }]
    }"""
)

# Add an inline policy to the role to grant the necessary permissions for SSM
# Replace policy with actual one according to the AI model training requirements.
ssm_role_policy = aws.iam.RolePolicy("ssmRolePolicy",
    role=ssm_role.id,
    policy="""{
        "Version": "2012-10-17",
        "Statement": [{
            "Effect": "Allow",
            "Action": "*",
            "Resource": "*"
        }]
    }"""
)

# Create an SSM Maintenance Window to define when model retraining should be initiated
maintenance_window = aws.ssm.MaintenanceWindow("exampleMaintenanceWindow",
    schedule="cron(0 4 ? * SUN *)", # This cron expression means every Sunday at 4 AM
    duration=3, # Duration in hours
    cutoff=1, # Hours before the end of the Maintenance Window that the system stops scheduling new tasks
)

# Define the SSM Maintenance Window Task to perform the model retraining
# Replace `targets_key` and `targets_values` with your instance tags or other target specifications
maintenance_window_task = aws.ssm.MaintenanceWindowTask("exampleMaintenanceWindowTask",
    window_id=maintenance_window.id,
    targets=[{
        "key": "InstanceIds",
        "values": ["i-1234567890abcdef0"],
    }],
    task_type="RUN_COMMAND",
    service_role_arn=ssm_role.arn,
    max_concurrency="1",
    max_errors="1",
    priority=1,
    task_arn="AWS-RunShellScript", # Or your custom SSM Document ARN if needed
    task_invocation_parameters={
        "run_command_parameters": {
            "comment": "Model retraining script execution",
            "document_hash": "YourHashHere", # Optional: Use this if you specify the document_version
            "document_version": "$DEFAULT", # Or custom version
            "parameters": {
                "commands": [
                    # Command to start model retraining
                    # Replace the following command with the actual command to kick off your AI model training
                    "/path/to/your/model/retraining/script.sh",
                ],
            },
            "timeout_seconds": 6000, # Adjust this based on the expected script execution time
        },
    },
)

pulumi.export("maintenance_window_id", maintenance_window.id)
pulumi.export("maintenance_window_task_id", maintenance_window_task.id)
```

In this program:

- We create an IAM role to give our SSM task the appropriate permissions.
- The schedule for the `maintenance_window` is set up using a cron expression that specifies when the window opens. This example schedules the task to run every Sunday at 4 AM. You'll need to update this to suit your schedule.
- The `maintenance_window_task` is the definition of what will occur during the window, targeting specific instances and running the specified commands.
- The `task_arn` parameter represents the Amazon Resource Name (ARN) for the SSM Document that encapsulates the commands to be run. For our purposes, we're assuming the simplest case using the built-in `AWS-RunShellScript` document.
- Replace `/path/to/your/model/retraining/script.sh` with the path to your AI model retraining script or the command that triggers the model training process.
- The `task_invocation_parameters` allows us to fine-tune how the command should be executed, including specifying the command timeout.

When you run this through Pulumi, it will provision the necessary AWS resources to periodically schedule your AI model's retraining. It's important to adjust the IAM role permissions and the SSM task details according to your specific use case and the requirements of the AI model training job.

Remember to install your Pulumi Python SDK using pip and ensure that your AWS credentials are properly configured for programmatic access.