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Deploy YOLO-NAS to Azure Virtual Machines

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How to Deploy YOLO-NAS to Azure Virtual Machines

In this guide, we are going to show how to deploy a

YOLO-NAS

model to

Azure Virtual Machines

using Roboflow Inference. Inference is a high-performance inference server with which you can run a range of vision models, from YOLOv8 to CLIP to CogVLM.

To deploy a

YOLO-NAS

model to

Azure Virtual Machines

, we will:

1. Set up our computing environment
2. Download the Roboflow Inference Server
3. Try out our model on an example image

Let's get started!

In this guide, we are going to show how to deploy a

YOLO-NAS

model to

Azure Virtual Machines

using the Roboflow Inference Server. This SDK works with

YOLO-NAS

models trained on both Roboflow and in custom training processes outside of Roboflow.

To deploy a

YOLO-NAS

model to

Azure Virtual Machines

, we will:

1. Train a model on (or upload a model to) Roboflow
2. Download the Roboflow Inference Server
3. Install the Python SDK to run inference on images
4. Try out the model on an example image

Let's get started!

Train a Model on or Upload a Model to Roboflow

If you want to upload your own model weights, first create a Roboflow account and create a new project. When you have created a new project, upload your project data, then generate a new dataset version. With that version ready, you can upload your model weights to Roboflow.

Download the Roboflow Python SDK:

pip install roboflow

Then, use the following script to upload your model weights:

from roboflow import Roboflow

home = "/path/to/project/folder"

rf = Roboflow(api_key=os.environ["ROBOFLOW_API_KEY"])
project = rf.workspace().project("PROJECT_ID")

project.version(PROJECT_VERSION).deploy(model_type="yolov5", model_path=f"/{home}/yolov5/runs/train/")

You will need your project name, version, API key, and model weights. The following documentation shows how to retrieve your API key and project information:

- Retrieve your Roboflow project name and version
- Retrieve your API key

Change the path in the script above to the path where your model weights are stored.

When you have configured the script above, run the code to upload your weights to Roboflow.

Now you are ready to start deploying your model.

Set up a Azure Virtual Machines Virtual Machine

Go to your Azure Virtual Machines homepage and create a Virtual Machine in Azure:

How you configure the virtual machine is dependent on how you plan to use the virtual machine so we will not cover specifics in this tutorial.

Roboflow Inference can run on both CPU (x86 and ARM) and NVIDIA GPU devices. But, you will need deploy a system with a GPU to deploy CogVLM. Choose an Azure deep learning operating system when you deploy your system. These operating systems often come with pre-built drivers for use in deep learning tasks like running multimodal models.

When your virtual machine is ready, a pop up will appear. Click “View resource” to view the virtual machine. Or, go back to the Virtual Machines homepage and select your newly-deployed virtual machine.

To sign into your virtual machine, first click “Connect”. Choose the authentication method that you prefer to log into your server. When you have logged in, you are ready to move on to the next step.

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Download the Roboflow Inference Server

The Roboflow Inference Server allows you to deploy computer vision models to a range of devices, including

Azure Virtual Machines

.

The Inference Server relies on Docker to run. If you don't already have Docker installed on the device(s) on which you want to run inference, install it by following the official Docker installation instructions.

Once you have Docker installed, run the following command to download the Roboflow Inference Server on your

Azure Virtual Machines

.

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pip install inference supervision

Now you have the Roboflow Inference Server running, you can use your model on

Azure Virtual Machines

Install the Roboflow Python SDK

The Roboflow Inference Server provides a HTTP API with a range of methods you can use to query your model and various popular models (i.e. SAM, CLIP). You can read more about all of the API methods available on the Roboflow Inference server in the Inference Server documentation.

The Roboflow Python SDK provides abstract convenience methods for interacting with the HTTP API. In this guide, we will use the Python SDK to run inference on a model. You can also query the HTTP API itself.

To install the Python SDK, run the following command:

pip install roboflow

Run Inference on an Image

Create a new Python file and add the following code:


from inference_sdk import InferenceHTTPClient,
# import os to get the ROBOFLOW_API_KEY from the environment
import os

# set the project_id, model_version, image_url
project_id = "logistics"
image_url = "forklift.jpeg"

model_version = 1

# create a client object
client = InferenceHTTPClient(
    api_url="http://localhost:9001",
    api_key=os.environ["ROBOFLOW_API_KEY"],
)

# run inference on the image
results = client.infer(image_url, model_id=f"{project_id}/{model_version}")

detections = sv.Detections.from_inference(results)

print(detections)

image = cv2.imread("forklift.jpeg")

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()
annotated_frame = box_annotator.annotate(
	scene=image.copy(),
	detections=detections,
)
annotated_frame = label_annotator.annotate(
    scene=annotated_frame,
    detections=detections,
)

cv2.imwrite("forklift_result.jpeg", annotated_frame)

Let's run the code on this image:

The model saves annotated predictions to a file. Here are the annotated predictions:

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Learn more about Roboflow for enterprise

Learn how to deploy models to other devices

Below, you can find our guides on how to deploy

YOLO-NAS

models to other devices.

Deploy YOLO-NAS to NVIDIA Jetson

Deploy YOLO-NAS to GCP Compute Engine

Deploy YOLO-NAS to AWS EC2

Deploy YOLO-NAS to Azure Virtual Machines

Documentation

The following resources are useful reference material for working with your model using Roboflow and the Roboflow Inference Server.