1. Fixed IP Retention for Re-deployed AI Instances


    In the context of cloud computing, fixed IP retention typically refers to persisting a static or dedicated IP address even after an instance or service is redeployed. This can be important for AI instances where a consistent IP is needed for callbacks, whitelisting, or when working with DNS records.

    To achieve this in a Pulumi-managed infrastructure, we would need to ensure that the IP address resource is separate from the instance deployment and associated with it, such that the IP address persists even when the instance is torn down and redeployed.

    The following program is an example of how one might ensure that an AI instance in AWS retains a fixed IP (Elastic IP in AWS terms) between deployments using Pulumi in Python. Note that this example is generic and would need to be adapted to your specific requirements, like the type of instance, the AMI used, and other configurations.

    import pulumi import pulumi_aws as aws # Step 1: Create an Elastic IP address. # Elastic IPs are static IP addresses designed for dynamic cloud computing. elastic_ip = aws.ec2.Eip("my-elastic-ip") # Step 2: Provision the AI instance. # This step assumes you have an AMI (Amazon Machine Image) with the desired AI software pre-installed. ai_instance = aws.ec2.Instance("ai-instance", instance_type="t2.medium", # Choose an appropriate instance type for your AI workload. ami="ami-123456") # This is a placeholder value, replace with your actual AMI ID. # Step 3: Associate the Elastic IP with the AI instance. # This resource is responsible for associating the Elastic IP to the instance we just provisioned. eip_association = aws.ec2.EipAssociation("eip-association", instance_id=ai_instance.id, allocation_id=elastic_ip.id) # Output the Elastic IP address so that it can be accessed if needed. pulumi.export("elastic_ip", elastic_ip.public_ip)


    1. Elastic IP: We start by creating an Elastic IP, which is a static IPv4 address designed for dynamic cloud computing. This IP address can be associated with any AWS instance and will persist separately from the lifecycle of that instance.

    2. AI Instance: The next step is setting up the actual instance that will run AI workloads. In this example, a t2.medium instance is created using a given AMI that should contain the AI software. Replace ami-123456 with the actual AMI ID that you wish to use.

    3. IP Association: Finally, once both the Elastic IP and the AI instance are created, we establish an association between them. This association ensures that the AI instance uses the Elastic IP as its public IP address.

    Note: The public_ip attribute of the elastic_ip resource will display the IP address after the resources are created. You can use this to access your AI instance.

    With this setup, even if you redeploy or recreate the AI instance, you can re-associate the same Elastic IP with the new instance, ensuring it retains the fixed IP address throughout redeployments. This approach provides you with the ability to maintain a consistent access point to your AI instance without having to update DNS records or whitelist entries.