1. High-Performance Computing Clusters for AI on AWS EC2


    Creating High-Performance Computing (HPC) clusters for AI workloads on AWS involves setting up a network of EC2 instances finely tuned for computational tasks. HPC clusters typically require fast networking, powerful CPUs, GPUs for machine learning tasks, and potentially large amounts of memory and storage.

    In building an HPC cluster using Pulumi with AWS, we'll focus on the following aspects:

    1. EC2 Instances: Serve as the compute nodes in the HPC cluster.
    2. Networking: A private network where these instances can communicate quickly and securely.
    3. Storage: EBS volumes or S3 buckets for storing large datasets and AI models.
    4. Security: Security groups to tightly control network access between instances.

    Here is a Pulumi program written in Python that provisions an EC2 instance suitable for HPC workloads. This includes a high-performance instance type, necessary storage, and network configurations.

    Please note that actual HPC setups can be quite complex, and the ideal configuration will depend on the specific workload and requirements. This example will set up a simple cluster with just two instances; in a real-world scenario, you would likely have many more, and possibly different instance types for different tasks.

    import pulumi import pulumi_aws as aws # Define the VPC for our HPC cluster to ensure our network is isolated vpc = aws.ec2.Vpc("hpc_vpc", cidr_block="", instance_tenancy="default", # For HPC, 'dedicated' tenancy might be more appropriate, depending on needs enable_dns_support=True, enable_dns_hostnames=True) # Create an internet gateway to allow communication with the VPC igw = aws.ec2.InternetGateway("hpc_igw", vpc_id=vpc.id) # Create a subnet which will contain our EC2 instances subnet = aws.ec2.Subnet("hpc_subnet", vpc_id=vpc.id, cidr_block="", map_public_ip_on_launch=True) # Setup a security group for the HPC cluster allowing SSH and internal communications security_group = aws.ec2.SecurityGroup("hpc_sg", vpc_id=vpc.id, description="Allow SSH and internal communication", ingress=[ {"protocol": "tcp", "from_port": 22, "to_port": 22, "cidr_blocks": [""]}, {"protocol": "-1", "from_port": 0, "to_port": 0, "cidr_blocks": [vpc.cidr_block]}, ], egress=[ {"protocol": "-1", "from_port": 0, "to_port": 0, "cidr_blocks": [""]}, ]) # Launch an EC2 instance for our HPC cluster # Note: The instance type 'c5n.18xlarge' is chosen for its high performance computing capabilities. # For AI workloads needing GPU, you might select a 'p3' or 'g4' instance type. instance_1 = aws.ec2.Instance("hpc_instance_1", instance_type="c5n.18xlarge", vpc_security_group_ids=[security_group.id], ami="ami-0a2363a9cff180a64", # Replace this with the appropriate AMI for your region and OS subnet_id=subnet.id) instance_2 = aws.ec2.Instance("hpc_instance_2", instance_type="c5n.18xlarge", vpc_security_group_ids=[security_group.id], ami="ami-0a2363a9cff180a64", # Replace this with the appropriate AMI for your region and OS subnet_id=subnet.id) # Export the IP addresses of the EC2 instances so that we can SSH into them pulumi.export("instance_1_public_ip", instance_1.public_ip) pulumi.export("instance_2_public_ip", instance_2.public_ip)

    Let's go through the code step by step:

    1. We first create a dedicated Virtual Private Cloud (VPC) to house our HPC cluster. This gives us a private network where we can control access and configure networking as needed.

    2. Next, we set up an Internet Gateway, which allows our VPC to communicate with the internet, useful if our instances need to download software updates or collaborate with services outside the VPC.

    3. We then create a Subnet within our VPC. This is where our EC2 instances will reside. The map_public_ip_on_launch attribute allows our instances to receive a public IP address that we can use to access them.

    4. A Security Group acting like a virtual firewall is created to control the traffic allowed into and out of the EC2 instances. This setup permits SSH access and unrestricted internal communication within the VPC.

    5. We launch two EC2 instances with the c5n.18xlarge instance type. This instance type is designed for compute-intensive workloads and provides high network performance, which is beneficial for HPC clusters. Make sure to replace the AMI ID with one that matches your region and the operating system you wish to use.

    By the end, we export the public IP addresses of the instances so they can be accessed from outside AWS (such as for SSH).

    This is a foundational setup, and considerations like attaching Elastic IPs, scaling into auto-scaling groups, allocating EBS volumes for persistent data, and setting up AWS Load Balancers would be natural next steps for a production-ready HPC cluster.