1. High Performance Computing Clusters on EC2 for AI


    High Performance Computing (HPC) clusters provide the vast amounts of compute power often necessary for running complex AI algorithms and simulations at scale. To deploy an HPC cluster on AWS EC2, one common approach is to use EC2 instances optimized for compute performance, alongside other AWS services which facilitate high-speed networking, and the management of compute jobs.

    The following Pulumi program provisions an HPC cluster using EC2 instances with a focus on performance. We will use some AWS services, like Placement Groups and Auto Scaling Groups, to enhance our cluster's performance.

    • EC2 Instances: AWS provides instance types that are optimal for HPC applications. We can use 'C' (Compute Optimized), 'P' (Accelerated Computing), or 'H' (High Disk Throughput) instances depending on the specific needs of the AI workload.

    • Placement Groups: This is a logical grouping of instances within a single Availability Zone that enables applications to participate in a low-latency, 10 Gbps network. This is crucial for HPC applications that require high network throughput and low-latency inter-node communication.

    • Auto Scaling Groups: While not always a requirement for HPC clusters, this AWS service can automatically scale the number of instances based on criteria you define, ensuring that the cluster has the resources it needs as workloads change.

    • Elastic File System (EFS): A scalable file storage used by EC2 instances for data that needs to be accessed and shared at high speeds.

    Here's a Pulumi Python program which sets up the HPC cluster:

    import pulumi import pulumi_aws as aws # Configure the AWS region to where resources will be deployed aws_region = aws.config.region # Create an EC2 Placement Group for cluster configuration to ensure instances are located close together # in the same Availability Zone placement_group = aws.ec2.PlacementGroup("hpc-placement-group", strategy="cluster") # Find the latest Amazon Linux 2 AMI for HPC ami = aws.ec2.get_ami(most_recent=True, owners=["amazon"], filters=[{"name": "name", "values": ["amzn2-ami-hvm-*-x86_64-gp2"]}]) # Create an Auto Scaling Group for managing the EC2 instances auto_scaling_group = aws.autoscaling.Group("hpc-auto-scaling-group", vpc_zone_identifiers=[aws_subnet.id], # replace aws_subnet with your subnet ID desired_capacity=2, max_size=5, min_size=1, health_check_type="EC2", force_delete=True, placement_group=placement_group.id, launch_configuration=aws.autoscaling.LaunchConfiguration("hpc-launch-configuration", image_id=ami.id, instance_type="c5.large", # select an appropriate instance type key_name="my-key-pair" # replace with your key pair ).id) # Create an Elastic File System (EFS) to be shared among EC2 instances efs_file_system = aws.efs.FileSystem("hpc-efs") # Output the DNS names for the Elastic File System pulumi.export('efs_dns_name', efs_file_system.dns_name) # Output the EC2 Autoscaling Group name pulumi.export('autoscaling_group_name', auto_scaling_group.name)


    • The placement group is essential for ensuring that instances are grouped together in a low-latency network, which is important for distributed AI workloads that benefit from fast node-to-node communication.

    • The AMI is chosen based on Amazon Linux 2, which is a common choice for EC2 instances running HPC workloads due to its stability and performance optimizations.

    • The Auto Scaling Group is configured with some desired, minimum, and maximum capacities. It is associated with launch configurations that dictate which AMI to use, the type of instance, and the key pair.

    • An Elastic File System (EFS) is created for shared storage, which instances can mount and use to store large datasets or model files that require high-throughput access.

    To apply this Pulumi program:

    1. You must have Pulumi installed and configured with the appropriate AWS credentials.
    2. Save the script to a file, say hpc_cluster.py.
    3. Run pulumi up from the directory where the script is saved, which will start provisioning the resources as outlined in the script.

    Make sure you replace placeholder values like aws_subnet and my-key-pair with the actual values from your environment. You may also need to adjust instance types and other resource parameters to fit your specific AI workloads and performance requirements.