1. Scalable EC2 Instances for Distributed Machine Learning Training


    To build a scalable set of Amazon EC2 Instances for distributed machine learning (ML) training, we will use an Auto Scaling Group in AWS. This Auto Scaling Group will ensure that we have a dynamic number of instances available that can grow or shrink based on demand or specific triggers, such as CPU utilization.

    Auto Scaling is critical for ML workloads because it helps to ensure that there are enough compute resources available to process the training jobs efficiently while also avoiding unnecessary costs when the demand for resources is low.

    Here's a breakdown of what we'll set up:

    • Launch Template: Defines the configurations for our EC2 instances, including the instance type, AMI, and other settings.
    • Auto Scaling Group: Uses the launch template to determine how new instances should be configured and manages the scaling policies to automatically adjust the number of EC2 instances.

    Here is a Pulumi program that sets up an Auto Scaling Group:

    import pulumi import pulumi_aws as aws # Define the AMI (Amazon Machine Image). Usually, for ML workloads, you would use a Deep Learning AMI with pre-installed ML frameworks. ami_id = "ami-12345678" # Replace with the actual AMI ID for your ML workload # Define the size of the instances. This depends on the ML workload and the datasets being used. instance_type = "ml.p2.xlarge" # An example instance type suited for ML tasks # Create a new Launch Template for the EC2 instances # This will define the AMI, instance type, and other configuration for the instances that we want to launch. launch_template = aws.ec2.LaunchTemplate("ml-training-template", image_id=ami_id, instance_type=instance_type, tags={ "Name": "ML Training Instance", }) # Create an Auto Scaling Group to manage the fleet of EC2 instances auto_scaling_group = aws.autoscaling.Group("ml-training-autoscaling-group", max_size=10, # Define the maximum number of instances min_size=1, # Define the minimum number of instances desired_capacity=2, # Define the desired number of instances at the start vpc_zone_identifiers=["subnet-12345"], # List of subnet IDs where instances will be created launch_template={ "id": launch_template.id, "version": "$Latest" # Use the latest version of the launch template }, # Define scaling policies if needed target_group_arns=[], # If you are using a load balancer, specify its ARN here health_check_type="EC2", # Use EC2 instance health checks to determine instance health tags=[{ "key": "Name", "value": "ML Training", "propagate_at_launch": True }]) pulumi.export("autoscaling_group_name", auto_scaling_group.name)

    In this program, we create a launch template with the specified AMI and instance type. We're tagging the instances with a name to indicate that they're for ML training.

    We then create the Auto Scaling Group with a minimum size of 1, a desired capacity of 2 (which will automatically launch two instances when the stack is deployed), and a maximum size of 10. You can adjust these numbers based on the computing needs of your ML workload.

    Replace the ami_id placeholder with the ID of the AMI you want to use (for example, an AMI with machine learning tools pre-installed), and update the vpc_zone_identifiers with your subnet IDs where you want the instances to be launched.

    To execute this Pulumi program:

    1. Install Pulumi and configure AWS credentials.
    2. Save the code in a file named __main__.py.
    3. Run pulumi up in the same directory as your file to deploy your infrastructure.

    The exported value autoscaling_group_name can be used to reference the Auto Scaling Group in other parts of your Pulumi program or when querying the stack outputs after deployment.