1. Elastic Training Clusters for Deep Learning on EC2


    To create elastic training clusters for deep learning on Amazon EC2 using Pulumi, you would typically need to set up the following:

    1. Amazon EC2 instances to serve as the nodes in your training cluster.
    2. An Autoscaling Group to manage the scaling of EC2 instances based on training needs.
    3. Appropriate IAM roles and policies to give your instances the required permissions.
    4. Networking components such as a VPC, subnets, and security groups.

    Below, I'll provide a Pulumi Python program that demonstrates how to create an EC2 autoscaling group optimized for deep learning, with instances that have GPUs attached.

    This program will:

    • Create a new Virtual Private Cloud (VPC).
    • Create subnets within the VPC across multiple availability zones for high availability.
    • Create an Internet Gateway and attach it to the VPC for internet access.
    • Create a Security Group within the VPC to allow specific traffic to the instances.
    • Launch an EC2 autoscaling group with instances optimized for deep learning (e.g., using the p2.xlarge instance type which has a GPU attached).
    • Create an IAM role and attach policies that will allow the EC2 instances to pull training data from an S3 bucket.

    Now follow along with the comments in the code to understand each step:

    import pulumi import pulumi_aws as aws # Step 1: Create a VPC for our cluster. This provides an isolated network environment. vpc = aws.ec2.Vpc("deep_learning_vpc", cidr_block="", tags={"Name": "deep_learning_vpc"}) # Step 2: Create subnets across different availability zones for high availability. # In a real-world scenario, you would dynamically determine the availability zones. subnet_1 = aws.ec2.Subnet("deep_learning_subnet_1", vpc_id=vpc.id, cidr_block="", availability_zone="us-west-2a", tags={"Name": "deep_learning_subnet_1"}) subnet_2 = aws.ec2.Subnet("deep_learning_subnet_2", vpc_id=vpc.id, cidr_block="", availability_zone="us-west-2b", tags={"Name": "deep_learning_subnet_2"}) # Step 3: Create an Internet Gateway for the VPC so that our cluster can access the internet. internet_gateway = aws.ec2.InternetGateway("deep_learning_igw", vpc_id=vpc.id, tags={"Name": "deep_learning_igw"}) # Step 4: Create a Security Group that allows inbound SSH and specific ports your deep learning application might need. security_group = aws.ec2.SecurityGroup("deep_learning_sg", vpc_id=vpc.id, description="Allow SSH and DL traffic", ingress=[ {"protocol": "tcp", "from_port": 22, "to_port": 22, "cidr_blocks": [""]}, ], tags={"Name": "deep_learning_sg"}) # Step 5: Create an IAM Role with needed policies for EC2 instances. iam_role = aws.iam.Role("deep_learning_instance_role", assume_role_policy=json.dumps({ "Version": "2012-10-17