1. Auto-Scaling ML Model Servers with EC2 Launch Templates


    Auto-scaling is a method that allows you to dynamically adjust the number of active servers in a service fleet based on demand. In AWS, you can achieve auto-scaling by using an EC2 Launch Template along with Auto Scaling Groups. An EC2 Launch Template allows you to define the configuration of an EC2 instance that you can use to launch new instances whenever needed.

    Here's how you can use Pulumi to create an EC2 Launch Template and an Auto Scaling Group for auto-scaling ML model servers on AWS:

    1. EC2 Launch Template: Define the base configuration for EC2 instances. This includes the machine image (AMI), instance type, security groups, IAM roles, etc.

    2. Auto Scaling Group: Use the launch template to create an Auto Scaling Group, which will manage the scaling of EC2 instances. You define the minimum and maximum number of instances, as well as the desired capacity and scaling policies if necessary.

    We will go through a Pulumi program written in Python that sets this up. Make sure you have already configured the AWS provider in your environment by using the pulumi config set aws:region REGION_NAME.

    Now, let's create the program:

    import pulumi import pulumi_aws as aws # Step 1: Create an EC2 Launch Template. # This template includes a hypothetical AMI ID for an ML model server, the instance type, and key pair for SSH access. # You would replace the AMI ID with one that has your ML model server. ml_launch_template = aws.ec2.LaunchTemplate("mlLaunchTemplate", image_id="ami-0abcd1234abcd1234", # Replace with your ML model server AMI ID. instance_type="t3.medium", # Choose the instance type that fits your ML model's requirements. key_name="my-key-pair", # Replace with your key pair for SSH access. # The user_data script is executed on instance launch; you might configure the ML environment here. user_data=""" #!/bin/bash echo "Performing startup tasks for ML model server..." # Your ML model server startup tasks go here. """, # You may add additional configurations, such as block device mappings, # security group IDs, and more as required for your use case. ) # Step 2: Create an Auto Scaling Group using the launch template define above. ml_autoscaling_group = aws.autoscaling.Group("mlAutoscalingGroup", launch_template={ "id": ml_launch_template.id, "version": "$Latest", # You can specify a particular version of the launch template. }, desired_capacity=2, min_size=1, max_size=5, vpc_zone_identifiers=["subnet-0bb1c79de3EXAMPLE", "subnet-077e4EXAMPLE"], # Replace with your subnet IDs. # Define the auto-scaling policies based on CPU utilization or other relevant metrics for your ML server. target_group_arns=[], # Add target group ARNs if your ML servers are behind a load balancer. # Auto Scaling Group tags can be added as needed. tags=[ {"key": "Name", "value": "MLModelServer", "propagate_at_launch": True}, # Additional tags here. ] ) pulumi.export("autoscaling_group_name", ml_autoscaling_group.name) pulumi.export("launch_template_id", ml_launch_template.id)

    This program creates an EC2 Launch Template with specified configuration details, which will be used by the Auto Scaling Group. The Auto Scaling Group manages the number of EC2 instances that run your machine learning model servers. It handles the auto-scaling based on policies you define. For example, you might scale up the number of servers when CPU utilization exceeds 80% for more than five minutes, and scale down when CPU utilization is below 20%.

    The desired_capacity, min_size, max_size, and other parameters should be set according to the needs of your ML workload. You would also need to set up proper networking by providing a list of subnet IDs where the instances should be launched. Remember to set up corresponding security groups and IAM roles with the necessary permissions for your instances.

    With the Pulumi Infrastructure as Code (IAC), you can easily manage the resources as code, version it, and apply it across different environments, whether you're running a development stack, staging, or production.