Deploy Vision Language Models (VLMs) on Azure AI

This example showcases how to deploy a Vision Language Model (VLM), i.e., a Large Language Model (LLM) with vision understanding, from the Hugging Face Collection in Azure AI Foundry Hub as an Azure ML Managed Online Endpoint, powered by Hugging Face’s Text Generation Inference (TGI). Additionally, this example also showcases how to run inference with both the Azure Python SDK, OpenAI Python SDK, and even how to locally run a Gradio application for chat completion with images.

TL;DR Text Generation Inference (TGI) is a solution developed by Hugging Face for deploying and serving LLMs and VLMs with high performance text generation. Azure AI Foundry provides a unified platform for enterprise AI operations, model builders, and application development. Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project lifecycle.

This example will specifically deploy Qwen/Qwen2.5-VL-32B-Instruct from the Hugging Face Hub (or see it on AzureML or on Azure AI Foundry) as an Azure ML Managed Online Endpoint on Azure AI Foundry Hub.

Qwen2.5-VL is one of the latest VLMs from Qwen, released after the impact and feedback from the previous Qwen2 VL release, with some key enhancements such as:

• Understand things visually: Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
• Being agentic: Qwen2.5-VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use.
• Understanding long videos and capturing events: Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of capturing event by pinpointing the relevant video segments.
• Capable of visual localization in different formats: Qwen2.5-VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes.
• Generating structured outputs: for data like scans of invoices, forms, tables, etc. Qwen2.5-VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc.

For more information, make sure to check their model card on the Hugging Face Hub.

Pre-requisites

To run the following example, you will need to comply with the following pre-requisites, alternatively, you can also read more about those in the Azure Machine Learning Tutorial: Create resources you need to get started.

Azure Account

A Microsoft Azure account with an active subscription. If you don’t have a Microsoft Azure account, you can now create one for free, including 200 USD worth of credits to use within the next 30 days after the account creation.

Azure CLI

The Azure CLI (az) installed on the instance that you’re running this example on, see the installation steps, and follow the steps of the preferred method based on your instance. Then log in into your subscription as follows:

az login

More information at Sign in with Azure CLI - Login and Authentication.

Azure CLI extension for Azure ML

Besides the Azure CLI (az), you also need to install the Azure ML CLI extension (az ml) which will be used to create the Azure ML and Azure AI Foundry required resources.

First you will need to list the current extensions and remove any ml-related extension before installing the latest one i.e., v2.

az extension list
az extension remove --name azure-cli-ml
az extension remove --name ml

Then you can install the az ml v2 extension as follows:

az extension add --name ml

More information at Azure Machine Learning (ML) - Install and setup the CLI (v2).

Azure Resource Group

An Azure Resource Group under the one you will create the Azure AI Foundry Hub-based project (note it will create an Azure AI Foundry resource as an Azure L Workspace, but not the other way around, meaning that the Azure AI Foundry Hub will be listed as an Azure ML workspace, but leveraging the Azure AI Foundry capabilities for Gen AI), and the rest of the required resources. If you don’t have one, you can create it as follow:

az group create --name huggingface-azure-rg --location eastus

Then, you can ensure that the resource group was created successfully by e.g. listing all the available resource groups that you have access to on your subscription:

az group list --output table

More information at Manage Azure resource groups by using Azure CLI.

Azure AI Foundry Hub-based project

An Azure AI Foundry Hub under the subscription and resource group aforementioned. If you don’t have one, you can create it as follows:

az ml workspace create \
    --kind hub \
    --name huggingface-azure-hub \
    --resource-group huggingface-azure-rg \
    --location eastus

Then, you can ensure that the workspace was created successfully by e.g. listing all the available workspaces that you have access to on your subscription:

az ml workspace list --filtered-kinds hub --query "[].{Name:name, Kind:kind}" --resource-group huggingface-azure-rg --output table

Once the Azure AI Foundry Hub is created, you need to create an Azure AI Foundry Project linked to that Hub, to do so you first need to obtain the Azure AI Foundry Hub ID of the recently created Hub as follows (replace the resource names with yours):

az ml workspace show \
    --name huggingface-azure-hub \
    --resource-group huggingface-azure-rg \
    --query "id" \
    -o tsv

That command will provide the ID as follows /subscriptions/<SUBSCRIPTION_ID>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.MachineLearningServices/workspaces/huggingface-azure-hub, meaning that you can also format it manually yourself with the appropriate replacements. Then you need to run the following command to create the Azure AI Foundry Project for that Hub as:

az ml workspace create \
    --kind project \
    --hub-id $(az ml workspace show --name huggingface-azure-hub --resource-group huggingface-azure-rg --query "id" -o tsv) \
    --name huggingface-azure-project \
    --resource-group huggingface-azure-rg \
    --location eastus

Finally, you can verify that it was correctly created with the following command:

az ml workspace list --filtered-kinds project --query "[].{Name:name, Kind:kind}" --resource-group huggingface-azure-rg --output table

More information at How to create and manage an Azure AI Foundry Hub and at How to create a Hub using the Azure CLI.

Setup and installation

In this example, the Azure Machine Learning SDK for Python will be used to create the endpoint and the deployment, as well as to invoke the deployed API. Along with it, you will also need to install azure-identity to authenticate with your Azure credentials via Python.

%pip install azure-ai-ml azure-identity --upgrade --quiet

More information at Azure Machine Learning SDK for Python.

Then, for convenience setting the following environment variables is recommended as those will be used along the example for the Azure ML Client, so make sure to update and set those values accordingly as per your Microsoft Azure account and resources.

%env LOCATION eastus
%env SUBSCRIPTION_ID <YOUR_SUBSCRIPTION_ID>
%env RESOURCE_GROUP <YOUR_RESOURCE_GROUP>
%env AI_FOUNDRY_HUB_PROJECT <YOUR_AI_FOUNDRY_HUB_PROJECT>

Finally, you also need to define both the endpoint and deployment names, as those will be used throughout the example too:

import os
from uuid import uuid4

os.environ["ENDPOINT_NAME"] = f"qwen-vl-endpoint-{str(uuid4())[:8]}"
os.environ["DEPLOYMENT_NAME"] = f"qwen-vl-deployment-{str(uuid4())[:8]}"

!echo $ENDPOINT_NAME
!echo $DEPLOYMENT_NAME

Authenticate to Azure ML

Initially, you need to authenticate into the Azure AI Foundry Hub via Azure ML with the Azure ML Python SDK, which will be later used to deploy Qwen/Qwen2.5-VL-32B-Instruct as an Azure ML Managed Online Endpoint in your Azure AI Foundry Hub.

import os
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

client = MLClient(
    credential=DefaultAzureCredential(),
    subscription_id=os.getenv("SUBSCRIPTION_ID"),
    resource_group_name=os.getenv("RESOURCE_GROUP"),
    workspace_name=os.getenv("AI_FOUNDRY_HUB_PROJECT"),
)

Create and Deploy Azure AI Endpoint

Before creating the Managed Online Endpoint, you need to build the model URI, which is formatted as it follows azureml://registries/<REGISTRY_NAME>/models/<MODEL_ID>/labels/latest (even if the URI contains azureml it’s the same as in Azure AI Foundry, since the model catalog is shared), that means that the REGISTRY_NAME should be set to “HuggingFace” as you intend to deploy a model from the Hugging Face Collection, and the MODEL_ID won’t be the Hugging Face Hub ID, but rather the ID with hyphen replacements for both backslash (/) and underscores (_) with hyphens (-), and then into lower case, as follows:

model_id = "Qwen/Qwen2.5-VL-32B-Instruct"

model_uri = (
    f"azureml://registries/HuggingFace/models/{model_id.replace('/', '-').replace('_', '-').lower()}/labels/latest"
)
model_uri

Note that you will need to verify in advance that the URI is valid, and that the given Hugging Face Hub Model ID exists on Azure, since Hugging Face is publishing those models into their collection, meaning that some models may be available on the Hugging Face Hub but not yet on the Azure Model Catalog (you can request adding a model following the guide Request a model addition).

Alternatively, you can use the following snippet to verify if a model is available on the Azure Model Catalog programmatically:

import requests

response = requests.get(f"https://generate-azureml-urls.azurewebsites.net/api/generate?modelId={model_id}")
if response.status_code != 200:
    print(
        "[{response.status_code=}] {model_id=} not available on the Hugging Face Collection in Azure ML Model Catalog"
    )

Then you can create the Managed Online Endpoint specifying its name (note that the name must be unique per entire region, not only within a single subscription, resource group, workspace, etc., so it’s a nice practice to add some sort of unique name to it in case multi-region deployments are intended) via the ManagedOnlineEndpoint Python class.

Also note that by default the ManagedOnlineEndpoint will use the key authentication method, meaning that there will be a primary and secondary key that should be sent within the Authentication headers as a Bearer token; but also the aml_token authentication method can be used, read more about it at Authenticate clients for online endpoints.

The deployment, created via the ManagedOnlineDeployment Python class, will define the actual model deployment that will be exposed via the previously created endpoint. The ManagedOnlineDeployment will expect: the model i.e., the previously created URI azureml://registries/HuggingFace/models/qwen-qwen2.5-vl-32b-instruct/labels/latest, the endpoint_name, and the instance requirements being the instance_type and the instance_count.

Every model in the Hugging Face Collection is powered by an efficient inference backend, and each of those can run on a wide variety of instance types (as listed in Supported Hardware); in this case, a NVIDIA H100 GPU will be used i.e., Standard_NC40ads_H100_v5.

from azure.ai.ml.entities import ManagedOnlineEndpoint, ManagedOnlineDeployment

endpoint = ManagedOnlineEndpoint(name=os.getenv("ENDPOINT_NAME"))

deployment = ManagedOnlineDeployment(
    name=os.getenv("DEPLOYMENT_NAME"),
    endpoint_name=os.getenv("ENDPOINT_NAME"),
    model=model_uri,
    instance_type="Standard_NC40ads_H100_v5",
    instance_count=1,
)

client.begin_create_or_update(endpoint).wait()

client.online_deployments.begin_create_or_update(deployment).wait()

Once deployed, via either the Azure AI Foundry or the Azure ML Studio you’ll be able to inspect the endpoint details, the real-time logs, how to consume the endpoint, and even use the, still on preview, monitoring feature.

Find more information about it at Azure ML Managed Online Endpoints

Send requests to the Azure AI Endpoint

Finally, now that the Azure AI Endpoint is deployed, you can send requests to it. In this case, since the task of the model is image-text-to-text (also known as chat-completion with image support) you can either use the default scoring endpoint, being /generate which is the standard text generation endpoint without chat capabilities (as leveraging the chat template or having an OpenAI-compatible OpenAPI interface), or alternatively just benefit from the fact that Text Generation Inference (TGI) i.e., the inference engine in which the model is running on top, exposes OpenAI-compatible routes as /v1/chat/completions.

Azure Python SDK

You can invoke the Azure ML Endpoint on the scoring route, in this case /generate (more information about it in the Qwen/Qwen2.5-VL-32B-Instruct page in either AzureML or Azure AI Foundry catalogs), via the Azure Python SDK with the previously instantiated azure.ai.ml.MLClient (or instantiate a new one if working from a different session).

import json
import os
import tempfile

with tempfile.NamedTemporaryFile(mode="w+", delete=True, suffix=".json") as tmp:
    json.dump(
        {
            "inputs": "![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png)What is this a picture of?\n\n",
            "parameters": {"max_new_tokens": 128},
        },
        tmp,
    )

    tmp.flush()

    response = client.online_endpoints.invoke(
        endpoint_name=os.getenv("ENDPOINT_NAME"),
        deployment_name=os.getenv("DEPLOYMENT_NAME"),
        request_file=tmp.name,
    )

print(json.loads(response))

OpenAI Python SDK

Since Text Generation Inference (TGI) also exposes OpenAI-compatible routes, you can also leverage the OpenAI Python SDK to send requests to the deployed Azure AI Endpoint.

%pip install openai --upgrade --quiet

To use the OpenAI Python SDK with Azure ML Managed Online Endpoints, you need to first retrieve:

• api_url with the /v1 route (that contains the v1/chat/completions endpoint that the OpenAI Python SDK will send requests to)
• api_key which is the API Key in Azure AI or the primary key in Azure ML (unless a dedicated Azure ML Token is used instead)

api_key = client.online_endpoints.get_keys(os.getenv("ENDPOINT_NAME")).primary_key
api_url = client.online_endpoints.get(os.getenv("ENDPOINT_NAME")).scoring_uri.replace("/generate", "/v1")

api_url = f"https://{os.getenv('ENDPOINT_NAME')}.{os.getenv('LOCATION')}.inference.ml.azure.com/v1"

Then you can use the OpenAI Python SDK normally, making sure to include the extra header azureml-model-deployment header that contains the Azure AI / ML Deployment name.

Via the OpenAI Python SDK it can either be set within each call to chat.completions.create via the extra_headers parameter as commented below, or via the default_headers parameter when instantiating the OpenAI client (which is the recommended approach since the header needs to be present on each request, so setting it just once is preferred).

import os
from openai import OpenAI

openai_client = OpenAI(
    base_url=api_url,
    api_key=api_key,
    default_headers={"azureml-model-deployment": os.getenv("DEPLOYMENT_NAME")},
)

completion = openai_client.chat.completions.create(
    model="Qwen/Qwen2.5-VL-32B-Instruct",
    messages=[
        {"role": "system", "content": "You are an assistant that responds like a pirate."},
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png"
                    },
                },
            ],
        },
    ],
    max_tokens=128,
    # extra_headers={"azureml-model-deployment": os.getenv("DEPLOYMENT_NAME")},
)
print(completion)

cURL

Alternatively, you can also just use cURL to send requests to the deployed endpoint, with the api_url and api_key values programmatically retrieved in the OpenAI snippet and now set as environment variables so that cURL can use those, as it follows:

os.environ["API_URL"] = api_url
os.environ["API_KEY"] = api_key

!curl -sS $API_URL/chat/completions \
    -H "Authorization: Bearer $API_KEY" \
    -H "Content-Type: application/json" \
    -H "azureml-model-deployment: $DEPLOYMENT_NAME" \
    -d '{ \
"messages":[ \
    {"role":"system","content":"You are an assistant that replies like a pirate."}, \
    {"role":"user","content": [ \
        {"type":"text","text":"What is in this image?"}, \
        {"type":"image_url","image_url":{"url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png"}} \
    ]} \
], \
"max_tokens":128 \
}' | jq

You can also just go to the Azure AI Endpoint in either the Azure AI Foundry under “My assets -> Models + endpoints” or in the Azure ML Studio via “Endpoints”, and retrieve both the URL (note that it will default to the /generate endpoint, but to use the OpenAI-compatible layer you need to use the /v1/chat/completions endpoint instead) and the API Key values, as well as the Azure AI / ML Deployment name for the given model, and then send the request as follows after replacing the values:

curl -sS <API_URL>/v1/chat/completions \
    -H "Authorization: Bearer <PRIMARY_KEY>" \
    -H "Content-Type: application/json" \
    -H "azureml-model-deployment: $DEPLOYMENT_NAME" \
    -d '{ \
"messages":[ \
    {"role":"system","content":"You are an assistant that replies like a pirate."}, \
    {"role":"user","content": [ \
        {"type":"text","text":"What is in this image?"}, \
        {"type":"image_url","image_url":{"url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png"}} \
    ]} \
], \
"max_tokens":128 \
}' | jq

Gradio

Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it. You can also leverage the OpenAI Python SDK to build a simple multimodal (text and images) ChatInterface that you can use within the Jupyter Notebook cell where you are running it.

%pip install gradio --upgrade --quiet

See below an example on how to leverage Gradio’s ChatInterface, or find more information about it at Gradio ChatInterface Docs.

import os
import base64
from typing import Dict, Iterator, List, Literal

import gradio as gr
from openai import OpenAI

openai_client = OpenAI(
    base_url=os.getenv("API_URL"),
    api_key=os.getenv("API_KEY"),
    default_headers={"azureml-model-deployment": os.getenv("DEPLOYMENT_NAME")},
)


def predict(
    message: Dict[str, str | List[str]], history: List[Dict[Literal["role", "content"], str]]
) -> Iterator[str]:
    content = []
    if message["text"]:
        content.append({"type": "text", "text": message["text"]})

    for file_path in message.get("files", []):
        with open(file_path, "rb") as image_file:
            base64_image = base64.b64encode(image_file.read()).decode("utf-8")
            content.append(
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/png;base64,{base64_image}"},
                }
            )

    messages = history.copy()
    messages.append({"role": "user", "content": content})

    stream = openai_client.chat.completions.create(
        model="Qwen/Qwen2.5-VL-32B-Instruct",
        messages=messages,
        stream=True,
    )
    buffer = ""
    for chunk in stream:
        if chunk.choices[0].delta.content:
            buffer += chunk.choices[0].delta.content
            yield buffer


demo = gr.ChatInterface(
    predict,
    textbox=gr.MultimodalTextbox(label="Input", file_types=[".jpg", ".png", ".jpeg"], file_count="multiple"),
    multimodal=True,
    type="messages",
)

demo.launch()

Release resources

Once you are done using the Azure AI Endpoint / Deployment, you can delete the resources as it follows, meaning that you will stop paying for the instance on which the model is running and all the attached costs will be stopped.

client.online_endpoints.begin_delete(name=os.getenv("ENDPOINT_NAME")).result()

Conclusion

Throughout this example you learnt how to create and configure your Azure account for Azure ML and Azure AI Foundry, how to then create a Managed Online Endpoint running an open model from the Hugging Face Collection in the Azure AI Foundry Hub / Azure ML Model Catalog, how to send inference requests to it afterwards with different alternatives, how to build a simple Gradio chat interface around it, and finally, how to stop and release the resources.

If you have any doubt, issue or question about this example, feel free to open an issue and we’ll do our best to help!