1. High-performance Compute Instances for LLMs with AWS EBS

    Python

    When you are creating high-performance compute instances on AWS, specifically for tasks like training or serving large language models (LLMs), you'll need to take into account both the compute power required and the associated storage that the models will need. For compute, you will likely use EC2 instances with high CPU and memory specifications, while for storage, Amazon Elastic Block Store (EBS) provides the necessary performance.

    Below is a Pulumi Python program that demonstrates how to provision high-performance EC2 instances and attach an EBS volume for storage. For this example, we'll use aws.ec2.Instance for creating an EC2 instance with the instance type suitable for high-performance computing and attach an aws.ec2.EbsVolume to provide the storage necessary to hold large datasets or models.

    Here is a brief explanation of each resource used in the program:

    • aws.ec2.Instance: This resource is an Elastic Compute Cloud (EC2) instance that acts as a virtual server for running applications on AWS. We'll be specifying a powerful instance type to ensure it's suited for high-performance tasks.

    • aws.ec2.EbsVolume: An Elastic Block Store (EBS) volume that acts as a network-attached drive. It can be attached to an EC2 instance to provide block-level storage that can be scaled out independently from the instance itself.

    • aws.ec2.VolumeAttachment: This resource attaches an EBS volume to an EC2 instance.

    Now, let's look at the actual program that creates these resources. We'll assume that you have already configured the AWS provider within Pulumi and that you have appropriate AWS credentials configured.

    import pulumi import pulumi_aws as aws # Define the EC2 instance. compute_instance = aws.ec2.Instance("HighPerformanceComputeInstance", instance_type="m5.12xlarge", # Instance type geared towards high-performance compute tasks. ami="ami-0c55b159cbfafe1f0", # Specify a proper AMI for your use case. This AMI should be for your required OS. key_name="my-key-pair", # Replace with your key pair for SSH access. tags={ "Name": "HighPerformanceComputeInstance", }, ) # Define the EBS volume. ebs_volume = aws.ec2.EbsVolume("HighPerformanceEBSVolume", size=200, # Size in GiB. Adjust as needed, more size for larger datasets or models. type="io1", # io1 is designed for high-performance IO-intensive workloads. iops=10000, # IOPS (Input/Output Operations Per Second) - Adjust as per workload. availability_zone=compute_instance.availability_zone, # Must be created in the same AZ as the instance. tags={ "Name": "HighPerformanceEBSVolume", }, ) # Attach the EBS volume to the compute instance. volume_attachment = aws.ec2.VolumeAttachment("HighPerformanceVolumeAttachment", instance_id=compute_instance.id, volume_id=ebs_volume.id, device_name="/dev/sdh", # Device name. Make sure it doesn't overlap with existing device names. ) # Export the IP address of the instance so we can SSH into it. pulumi.export("instance_ip", compute_instance.public_ip) # Export the volume ID for reference. pulumi.export("volume_id", ebs_volume.id)

    This program creates an EC2 instance and an EBS volume, both designed for high-performance requirements. It then attaches the volume to the instance so that it can be mounted inside the instance's operating system, providing performant storage for LLMs.

    Please ensure to replace "ami-0c55b159cbfafe1f0" with the correct AMI for your specific applications or LLMs, as well as "my-key-pair" with your own SSH key pair.

    You may also need additional configurations like security groups or different specifications like another EC2 instance type or EBS volume types according to your specific LLM requirements. Always check AWS documentation and Pulumi's AWS EC2 documentation and AWS EBS documentation to make the best choices for your scenario.


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