1. EC2 Instances for Distributed Deep Learning Training


    To set up EC2 Instances for distributed deep learning training on AWS using Pulumi, you will need to create several components:

    1. EC2 Instances: This is where your deep learning training code will actually run. You'll need to choose an appropriate instance type that supports the type of deep learning frameworks you're using, such as instances with GPU support.

    2. Security Group: This acts as a virtual firewall that controls the traffic to the EC2 instances. You'll have to ensure the security group allows traffic on required ports for distributed training (for example, SSH for remote access and any framework-specific ports).

    3. IAM Role: The instances will need an IAM role with the necessary permissions to access other AWS services, like S3 for data storage.

    4. Key Pair: For secure SSH access to the instances, you should create an AWS key pair.

    Below is a Python program written using Pulumi to create these resources. This example assumes you've chosen an Amazon Machine Image (AMI) that has the necessary deep learning frameworks pre-installed, and you've determined the instance type according to your workload. Please replace 'ami-12345678' with the actual AMI ID for your deep learning AMI, and 't2.micro' with the instance type you wish to use, like one from the p3 or g4 family for instances that come with GPUs.

    import pulumi import pulumi_aws as aws # Number of instances you want to create for the training cluster. cluster_size = 4 # Specify the AMI ID for your deep learning AMI. ami_id = 'ami-12345678' # Replace 't2.micro' with the actual instance type you need. instance_type = 't2.micro' # Create an AWS key pair for SSH access (You would normally import an existing key pair) key_pair = aws.ec2.KeyPair("keyPair", public_key="ssh-rsa AAAAB3NzaC...yourexistingpublickey...") # Create a security group that allows inbound SSH traffic sec_group = aws.ec2.SecurityGroup('deep-learning-sg', description='Enable SSH access', ingress=[ { 'protocol': 'tcp', 'from_port': 22, 'to_port': 22, 'cidr_blocks': [''] } ]) # Create an IAM role and attach the necessary policies iam_role = aws.iam.Role('deep-learning-role', assume_role_policy="""{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": {"Service": "ec2.amazonaws.com"}, "Action": "sts:AssumeRole" } ] }""") # Attach a policy to the IAM role for S3 access s3_policy_attachment = aws.iam.RolePolicyAttachment('deep-learning-s3-access', role=iam_role.name, policy_arn=aws.iam.ManagedPolicy.AMAZON_S3_FULL_ACCESS) # Create the EC2 instances instances = [] for i in range(cluster_size): instance = aws.ec2.Instance(f'deep-learning-instance-{i}', ami=ami_id, instance_type=instance_type, key_name=key_pair.key_name, security_groups=[sec_group.name], iam_instance_profile=aws.iam.InstanceProfile('deep-learning-profile', role=iam_role.name).name, tags={ 'Name': f"deep_learning_instance_{i}" }) instances.append(instance) # Export the public IPs of the instances for i, instance in enumerate(instances): pulumi.export(f'instance_{i}_public_ip', instance.public_ip)

    This program performs the following actions:

    • It creates the number of EC2 instances specified by cluster_size, configured with specified AMI and instance type.
    • It sets up a security group to allow SSH access (TCP traffic on port 22) from any IP address (
    • It generates an IAM role with a trust relationship allowing EC2 to assume the role.
    • It attaches a managed policy to the role for full S3 access. You might want to customize this policy for tighter security based on actual requirements.
    • It uses the IAM role to create instance profiles for the EC2 instances, which grant the instances permissions defined by the role.
    • It creates a key pair for SSH access. You can replace the dummy public key here with your actual public SSH key.
    • Finally, it exports the public IP addresses of the instances so that you can access them, for example, via SSH.

    Please note that in a real-world scenario, you would probably want more fine-grained security group rules, a custom policy instead of full S3 access, and existing key pairs instead of creating new ones. Also, for the actual distributed deep learning training, you might need additional setup inside the instances to configure the deep learning environment.