README.md

AzureOAIBalancer

AzureOAIBalancer is a Terraform repository for automating the deployment of a load-balanced Azure OpenAI environment across multiple regions. It aims to increase throughput and manage high request volumes for any OpenAI model by distributing workloads evenly.

Utilizing a random integer-based load balancer, it's advisable to implement a backoff strategy on the client side to handle RateLimitError. In the event of encountering a rate limit error, retrying the request should lead to a random redirection to a different deployment, bypassing the rate limit obstacle.

Includes Swagger UI

Screens

Setup Instructions

Prerequisites

Before you begin, ensure you have the following prerequisites met:

• Terraform installed on your local machine.
• An Azure subscription and the Azure CLI installed and configured.
• Access to the OpenAI models in the desired Azure regions.

Configuration Steps

1. Set the Azure Subscription ID

   Locate the var.tf file in your repository, and set your Azure subscriptionId. This will be used across all Terraform configurations to deploy resources in your Azure account.

   variable "subscription_id" {
     description = "The Subscription ID used for the Azure resources."
     type        = string
   }

2. Modify Cognitive Account Locations

   In the var.tf file, update the cognitive_accounts object with the locations you require. Make sure that you have access to the OpenAI models in these regions.

   variable "cognitive_accounts" {
     description = "A map of cognitive account locations and their details."
     type        = map(object({
       location           : string
       custom_subdomain   : string
     }))
     default = {
       "canadaeast" = {
         location         = "CanadaEast"
         custom_subdomain = "canadaeast"
       },
       "swedencentral" = {
         location         = "SwedenCentral"
         custom_subdomain = "swedencentral"
       },
       "switzerlandnorth" = {
         location         = "SwitzerlandNorth"
         custom_subdomain = "switzerlandnorth"
       }
     }
   }

3. Configure the Backend

   If you are using a remote backend for Terraform state, such as Azure Blob Storage, configure the backend "azurerm" section in your Terraform configurations. If you're not using a remote backend, this section can be removed.

   terraform {
     backend "azurerm" {
       resource_group_name  = "YourResourceGroupName"
       storage_account_name = "YourStorageAccountName"
       container_name       = "YourContainerName"
       key                  = "terraform.tfstate"
     }
   }

4. Deployment

   After configuring the necessary files, you can proceed with the Terraform deployment process:

   • Initialize Terraform to download the required providers.

     terraform init

   • Plan the deployment to review the changes Terraform will make.

     terraform plan

   • Apply the configuration to create the resources in Azure.

     terraform apply

For additional details on managing and troubleshooting your deployment, refer to the Terraform documentation.