1. Deploying LLMs across AWS US West (Oregon) Availability Zones


    Deploying a large language model (LLM) across multiple Availability Zones (AZs) in AWS US West (Oregon) region would typically involve the following steps:

    1. Choose the Right Service: You would often use a managed container service such as ECS (Elastic Container Service) or EKS (Elastic Kubernetes Service) for running such models. This allows you to manage and scale your deployment easily.

    2. Set Up Networking: You would need to set up a VPC with subnets across multiple AZs to ensure high availability and fault tolerance.

    3. Create Computing Resources: Depending on the chosen service, you would need to create ECS tasks or EKS pods as computing units to run your LLMs.

    4. Load Balancing: To distribute traffic among your instances, you would set up a load balancer across the AZs.

    5. Auto Scaling: Set up auto-scaling to manage the scaling of your LLM instances automatically based on the predefined metrics or schedules.

    6. Monitoring and Logging: Use CloudWatch or other monitoring tools to keep an eye on your LLMs' health and performance.

    In the Pulumi code below, we’ll focus on setting up a high-level, fault-tolerant infrastructure that spans across multiple Availability Zones in the AWS US West (Oregon) region to deploy containerized LLMs:

    import pulumi import pulumi_aws as aws # Configure the AWS provider to use the US West (Oregon) region aws.config.region = 'us-west-2' # Create a VPC vpc = aws.ec2.Vpc('llm-vpc', cidr_block='', enable_dns_hostnames=True, enable_dns_support=True, tags={'Name': 'llm-vpc'}) # Create subnets across multiple Availability Zones subnet_1 = aws.ec2.Subnet('llm-subnet-1', vpc_id=vpc.id, cidr_block='', availability_zone='us-west-2a', tags={'Name': 'llm-subnet-1'}) subnet_2 = aws.ec2.Subnet('llm-subnet-2', vpc_id=vpc.id, cidr_block='', availability_zone='us-west-2b', tags={'Name': 'llm-subnet-2'}) # Create an Internet Gateway to allow communication with the internet igw = aws.ec2.InternetGateway('llm-igw', vpc_id=vpc.id, tags={'Name': 'llm-igw'}) # Create a Route Table to associate with the subnets route_table = aws.ec2.RouteTable('llm-route-table', vpc_id=vpc.id, routes=[aws.ec2.RouteTableRouteArgs( gateway_id=igw.id, destination_cidr_block='' )], tags={'Name': 'llm-route-table'}) # Associate the route table with the subnets route_table_assoc_1 = aws.ec2.RouteTableAssociation('llm-rta-1', route_table_id=route_table.id, subnet_id=subnet_1.id) route_table_assoc_2 = aws.ec2.RouteTableAssociation('llm-rta-2', route_table_id=route_table.id, subnet_id=subnet_2.id) # Create a Security Group to allow incoming traffic on a specific port sg = aws.ec2.SecurityGroup('llm-sg', vpc_id=vpc.id, description='Allow inbound traffic', ingress=[aws.ec2.SecurityGroupIngressArgs( from_port=80, to_port=80, protocol='tcp', cidr_blocks=[''] )], egress=[aws.ec2.SecurityGroupEgressArgs( from_port=0, to_port=0, protocol='-1', cidr_blocks=[''] )], tags={'Name': 'llm-sg'}) # Output the VPC and subnet IDs pulumi.export('vpc_id', vpc.id) pulumi.export('subnet_id_1', subnet_1.id) pulumi.export('subnet_id_2', subnet_2.id) # At this point you would continue to deploy containers with Load Balancers, # and Auto Scaling, but those are more advanced topics beyond the basics set here.

    This program sets up the base network infrastructure to deploy applications across multiple Availability Zones (AZs) within AWS US West (Oregon) region. It prepares:

    • A VPC to provide a virtual network in AWS.
    • Two subnets, each in a different Availability Zone, to deploy the LLMs in a highly available configuration.
    • An Internet Gateway to connect the VPC to the internet.
    • A Route Table to define rules for traffic routing.
    • A Security Group to control the inbound and outbound traffic.

    And exports the VPC and subnet IDs, which could be used to reference these resources in additional Pulumi code.

    This code is a starting point; to complete the deployment, you'll want to set up container services, task definitions, load balancers, and auto-scaling. You can do this using AWS ECS or EKS services as suggested earlier. Auto-scaling would involve AWS Auto Scaling Groups, which can automatically adjust the number of instances to handle the load.