1. Scalable EC2 Instances for Machine Learning Model Training


    To facilitate scalable EC2 Instances for Machine Learning Model Training on AWS, we will use an Auto Scaling Group to manage the EC2 instances. An Auto Scaling Group ensures that we have a cluster of EC2 instances that will scale up or down based on demands or defined policies. This is crucial for machine learning model training workloads, which may require more compute power during training and less during idle times.

    Within the Auto Scaling Group, we can define a launch template that specifies the instance type, image ID, and other configurations required for the EC2 instances. We can also define scaling policies based on CPU utilization or other metrics, ensuring our instances only scale when necessary, optimizing costs.

    Here is how we'll achieve this with Pulumi and Python:

    1. Create an EC2 launch configuration that defines the machine image, instance type, and other parameters.
    2. Define the Auto Scaling Group, setting minimum and maximum sizes, and specify the launch configuration.
    3. Set up scaling policies for the Auto Scaling Group to scale EC2 instance count up or down based on load.

    We prefer to use the aws package for Pulumi because it provides high-level components which simplify cloud resource management.

    Let's write the complete Pulumi program in Python:

    import pulumi import pulumi_aws as aws # Create an IAM role and attach the AWS managed policy for EC2. role = aws.iam.Role("ml-role", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "ec2.amazonaws.com" } }] }""" ) policy_attachment = aws.iam.RolePolicyAttachment("policy-attachment", role=role.name, policy_arn='arn:aws:iam::aws:policy/AmazonEC2ContainerServiceforEC2Role' ) # Create an EC2 launch configuration, specifying the instance type and AMI. # The instance type is chosen based on the requirements of the machine learning workload. launch_configuration = aws.ec2.LaunchConfiguration("ml-launch-configuration", image_id="ami-0c55b159cbfafe1f0", # Replace this AMI ID with the AMI that suits your ML workload instance_type="t2.medium", # Choose an instance type appropriate for machine learning tasks iam_instance_profile=role.name, ) # Define the Auto Scaling Group with a desired capacity that can auto-scale. autoscaling_group = aws.autoscaling.Group("ml-autoscaling-group", launch_configuration=launch_configuration.id, min_size=1, max_size=5, # This can be adjusted based on the maximum instances you'd require vpc_zone_identifiers=["subnet-0bb1c79de3EXAMPLE"], # Update with your VPC subnet IDs tags=[{ "key": "Name", "value": "ML_AutoScaling", "propagate_at_launch": True, }] ) # Create a scaling policy to increase the number of EC2 instances. scale_up_policy = aws.autoscaling.Policy("scale-up", scaling_adjustment=1, adjustment_type="ChangeInCapacity", cooldown=300, autoscaling_group_name=autoscaling_group.name, ) # Create a scaling policy to decrease the number of EC2 instances. scale_down_policy = aws.autoscaling.Policy("scale-down", scaling_adjustment=-1, adjustment_type="ChangeInCapacity", cooldown=300, autoscaling_group_name=autoscaling_group.name, ) # Output the Auto Scaling Group name. pulumi.export("autoscaling_group_name", autoscaling_group.name)

    In the above program:

    • We begin by defining an IAM role for our EC2 instances to allow them to carry out required actions.
    • The launch configuration ml-launch-configuration specifies the details of the EC2 instances which the Auto Scaling Group will manage. Notably, we include the AMI ID and the instance type. These should be chosen according to your machine learning workload's requirements and your budgetary considerations.
    • The ml-autoscaling-group Auto Scaling Group is configured with minimum and maximum size constraints, which define the scaling boundaries. The VPC subnet IDs indicate where the EC2 instances should be created.
    • We define two scaling policies: scale-up to increase the count of instances when needed, and scale-down to decrease the count when demand is low.
    • Finally, we export the Auto Scaling Group name as an output, which might be useful for querying or managing it outside of Pulumi.

    By running this Pulumi program, you will have a scalable infrastructure capable of adjusting the number of EC2 instances as needed for training your machine learning models. The scaling policies snap into action based on metrics or schedules that you can define according to your needs.