1. Scaling Policies for GPU-Based Training Jobs


    Scaling policies for GPU-based training jobs are essential for optimizing the cost and performance of your machine learning workloads. They allow you to automatically adjust the number of GPU instances based on the computational needs of your training jobs, ensuring that your resources scale up when you need more computing power and scale down when you don't, to save costs.

    In this Pulumi program, we will use AWS as our cloud provider, leveraging their Auto Scaling services to dynamically adjust the number of EC2 instances with GPUs. Specifically, we'll use aws.autoscaling.Policy, which provides a way to define the scaling policies and aws.autoscaling.Group, the group of EC2 instances that will scale. We'll also use aws.ec2.LaunchTemplate to define the configuration of EC2 instances that the Auto Scaling group will use, including the desired GPU instance type.

    Here's the step-by-step on what will be done:

    1. Create an EC2 Launch Template specifying the GPU instance type and other configurations for the machine learning workloads.
    2. Create an Auto Scaling Group attached to the Launch Template created in step 1.
    3. Create Scaling Policies (both scale-out and scale-in) to attach to the Auto Scaling Group, which will define when to scale out (add more instances) or scale in (remove instances).

    The following is a Pulumi program that sets up the above resources:

    import pulumi import pulumi_aws as aws # Step 1: Create an EC2 Launch Template for GPU instances launch_template = aws.ec2.LaunchTemplate("gpu-instance-launch-template", image_id="ami-0abcdef1234567890", # Placeholder AMI, replace with your desired GPU AMI instance_type="p2.xlarge", # Placeholder instance type, select your desired GPU instance type key_name="my-key-pair", # Replace with your key pair for SSH access # Additional configurations like block device mappings, network interfaces, IAM roles, etc. ) # Step 2: Create Auto Scaling Group that utilizes the Launch Template auto_scaling_group = aws.autoscaling.Group("gpu-training-auto-scaling-group", vpc_zone_identifiers=["subnet-0bb1c79de3EXAMPLE"], # Replace with your subnet ID(s) desired_capacity=2, max_size=10, # Define the maximum number of instances the ASG can scale to min_size=1, # Define the minimum number of instances the ASG should maintain launch_template={ "id": launch_template.id, "version": "$Latest", }, # Add target tracking scaling policies for metrics like average GPU utilization or network input/output ) # Step 3: Create Scaling Policies for the Auto Scaling Group # Scale Out Policy: Increase the number of instances scale_out_policy = aws.autoscaling.Policy("scale-out", autoscaling_group_name=auto_scaling_group.name, policy_type="TargetTrackingScaling", target_tracking_configuration=aws.autoscaling.PolicyTargetTrackingConfigurationArgs( target_value=50.0, # Average GPU utilization (percentage) to trigger scaling out predefined_metric_specification=aws.autoscaling.PolicyPredefinedMetricSpecificationArgs( predefined_metric_type="GPUtilization" ), ), ) # Scale In Policy: Decrease the number of instances scale_in_policy = aws.autoscaling.Policy("scale-in", autoscaling_group_name=auto_scaling_group.name, policy_type="TargetTrackingScaling", target_tracking_configuration=aws.autoscaling.PolicyTargetTrackingConfigurationArgs( target_value=15.0, # Average GPU utilization (percentage) to trigger scaling in predefined_metric_specification=aws.autoscaling.PolicyPredefinedMetricSpecificationArgs( predefined_metric_type="GPUtilization" ), ), ) # Export the Auto Scaling Group name and Scaling Policies ARNs pulumi.export("auto_scaling_group_name", auto_scaling_group.name) pulumi.export("scale_out_policy_arn", scale_out_policy.arn) pulumi.export("scale_in_policy_arn", scale_in_policy.arn)

    Before running this program, ensure that you have the correct AMI ID for the GPU instances you wish to run, select the appropriate instance types, and fill in your specific VPC and subnet details.

    Running this Pulumi program will set up an AWS environment that can scale your GPU instances based on utilization. It is particularly useful for machine learning workloads, where computational demands can vary significantly over time.

    For more details on each of the AWS resource options used in this program, visit Pulumi's documentation: