#
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 Endpoints. 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.

> Note that this example will go through the Python SDK / Azure CLI programmatic deployment, if you’d rather prefer using the one-click deployment experience, please check One-click deployments from the Hugging Face Hub on Azure AI.

TL;DR 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.

> Note that you can select any VLM available on the Hugging Face Hub with the “Deploy to AzureML” option enabled, or directly select any of the LLMs available in either the Azure ML or Azure AI Foundry Hub Model Catalog under the “HuggingFace” collection (note that for Azure AI Foundry the Hugging Face Collection will only be available for Hub-based projects).

##
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.

- An Azure account with an active subscription.
- The Azure CLI installed and logged in.
- The Azure Machine Learning extension for the Azure CLI.
- An Azure Resource Group.
- A project based on an Azure AI Foundry Hub.

For more information, please go through the steps in Set up Azure AI.

##
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:

> Note that endpoint names must to be globally unique per region i.e., even if you don’t have any endpoint named that way running under your subscription, if the name is reserved by another Azure customer, then you won’t be able to use the same name. Adding a timestamp or a custom identifier is recommended to prevent running into HTTP 400 validation issues when trying to deploy an endpoint with an already locked / reserved name. Also the endpoint name must be between 3 and 32 characters long.

    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]}"

##
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.

> On standard Azure ML deployments you’d need to create the `MLClient` using the Azure ML Workspace as the `workspace_name` whereas for Azure AI Foundry, you need to provide the Azure AI Foundry Hub name as the `workspace_name` instead, and that will deploy the endpoint under the Azure AI Foundry too.

    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/HuggingFace/models/<MODEL_ID>/labels/latest` where the `MODEL_ID` won’t be the Hugging Face Hub ID but rather its name on Azure, 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

> To check if a model from the Hugging Face Hub is available in Azure, you should read about it in Supported Models. If not, you can always Request a model addition in the Hugging Face collection on Azure).

Then you need to create the ManagedOnlineEndpoint via the Azure ML Python SDK as follows.

> 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). Since for models and inference engines require a GPU-accelerated instance, you might need to request a quota increase as per Manage and increase quotas and limits for resources with Azure Machine Learning.

    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()

> In Azure AI Foundry the endpoint will only be listed within the “My assets -> Models + endpoints” tab once the deployment is created, not before as in Azure ML where the endpoint is shown even if it doesn’t contain any active or in-progress deployments.

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

> Note that whilst the Azure AI Endpoint creation is relatively fast, the deployment will take longer since it needs to allocate the resources on Azure so expect it to take ~10-15 minutes, but it could as well take longer depending on the instance provisioning and availability.

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 the inference engine in which the model is running on top exposes OpenAI-compatible routes as `/v1/chat/completions`.

> Note that below only some of the options are listed, but you can send requests to the deployed endpoint as long as you send the HTTP requests with the `azureml-model-deployment` header set to the name of the Azure AI Deployment (not the Endpoint), and have the necessary authentication token / key to send requests to the given endpoint; then you can send HTTP request to all the routes that the backend engine is exposing, not only to the scoring route.

###
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).

> Since in this case you are deploying a Vision Language Model (VLM) with Text Generation Inference (TGI), to leverage the vision capabilities through the `/generate` endpoint you will need to include either the image URL or the base64 encoding of the image formatted in Markdown as e.g. `![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png)What is this a picture of?\n\n` or `![](data:image/png;base64,...)What is this a picture of?\n\n`, respectively.

> More information at Vision Language Model Inference in TGI.

    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))

> Note that the Azure ML Python SDK requires a path to a JSON file when invoking the endpoints, meaning that whatever payload you want to send to the endpoint will need to be first converted into a JSON file, whilst that only applies to the requests sent via the Azure ML Python SDK.

###
OpenAI Python SDK

Since the inference engine in which the model is running on top 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)

    from urllib.parse import urlsplit

    api_key = client.online_endpoints.get_keys(os.getenv("ENDPOINT_NAME")).primary_key

    url_parts = urlsplit(client.online_endpoints.get(os.getenv("ENDPOINT_NAME")).scoring_uri)
    api_url = f"{url_parts.scheme}://{url_parts.netloc}"

> Alternatively, you can also build the API URL manually as it follows, since the URIs are globally unique per region, meaning that there will only be one endpoint named the same way within the same region:

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

> Or just retrieve it from either the Azure AI Foundry or the Azure ML Studio.

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=f"{api_url}/v1",
        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/v1/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

Alternatively, 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 Deployment name for the given model.

###
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.

> Ideally you could deploy the Gradio Chat Interface connected to your Azure ML Managed Online Endpoint as an Azure Container App as described in Tutorial: Build and deploy from source code to Azure Container Apps. If you’d like us to show you how to do it for Gradio in particular, feel free to open an issue requesting 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!

> 📍 Find the complete example on GitHub here!

Update on GitHub