While I have not tried this in azure, my understanding is that you can deploy a Linux vm with A100 in azure (T4or V100 may not work for all use cases, but will be a cheaper option). Once you have a Linux vm with GPU, you can choose how you would like to host the model(s). You can write some code and expose the LLM via an API ( I like Fast chat, but there are other options as well). Heck you can even use ooba if you like. Just make sure to check the license for what you use.

Asked gpt-4 as I was curious myself, this would be the path:

Developing a Python script to deploy custom Large Language Models (LLMs) like Falcon, Deepsake, etc., from Hugging Face into Azure involves several steps. Here’s a high-level approach to guide you:

1. User Interface for Model Selection

• Develop a web interface where users can select their desired LLM from a list. This interface will communicate with your backend server, which will handle the deployment process.

2. Backend Server Script

• Write a Python script on the server that receives the model choice from the user interface.
• Use the Hugging Face transformers library to access the chosen model.
• The script should authenticate with Azure using Azure SDK for Python.

3. Automating Deployment in Azure

• Use Azure Resource Manager templates or Azure CLI scripts integrated into your Python script for deploying the necessary Azure resources (like Azure Kubernetes Service, Azure Container Instances, or Azure Virtual Machines, depending on the model size and expected load).
• Containerize the chosen LLM using Docker. The Dockerfile should include steps to install necessary dependencies, including the transformers library, and download the chosen model.
• Push the Docker container to Azure Container Registry.

4. Deploying the Container

• Automate the deployment of the container to the chosen Azure service (like AKS or ACI) through your Python script.
• Configure the deployment to expose an endpoint that your users can interact with to utilize the LLM for their applications.

5. Security and Compliance

• Ensure that the deployment script adheres to data security policies. This might include configuring network security groups, private endpoints, and ensuring encrypted data transmission.

6. Monitoring and Management

• Implement logging and monitoring to track the usage and performance of the deployed models.
• Consider adding features to scale the service based on demand.

Example Python Script Structure:

import azure.identity
import azure.mgmt.resource
import azure.mgmt.containerinstance
import requests

def deploy_model_to_azure(model_name):
    # Authenticate with Azure
    credentials = azure.identity.DefaultAzureCredential()
    subscription_id = 'your-subscription-id'

    # Code to create and configure Azure resources
    # ...

    # Containerize and push the model
    docker_image = containerize_model(model_name)
    push_to_azure_registry(docker_image)

    # Deploy the container
    deploy_container_to_azure(docker_image)
    # ...

def containerize_model(model_name):
    # Code to create a Docker image with the selected model
    # ...
    return docker_image_name

def push_to_azure_registry(image_name):
    # Code to push Docker image to Azure Container Registry
    # ...

def deploy_container_to_azure(image_name):
    # Code to deploy the container to Azure Kubernetes Service or Container Instances
    # ...

# Example usage
deploy_model_to_azure('falcon-model-name')

Points to Consider:

• Scalability: Ensure your deployment can handle multiple simultaneous deployments and manage resources efficiently.
• Customization: Allow for custom configurations by users, such as compute size, memory, etc.
• Error Handling: Robust error handling for issues during deployment.