Deploy YOLO-World to Azure Virtual Machines

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.

You can run inference on images with Roboflow Inference.

Create a new Python file and add the following code:

# import client from inference sdk
from inference_sdk import InferenceHTTPClient
# import PIL for loading image
from PIL import Image
# import os for getting api key from environment
import os

# set the project_id, model_version, image_url
project_id = "soccer-players-5fuqs"
model_version = 1
filename = "path/to/local/image.jpg"

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

# load the image
pil_image = Image.open(filename)

# run inference
results = client.infer(pil_image, model_id=f"{project_id}/{model_version}")

print(results)

Substitute the model name and version with the values associated with your Roboflow account and project, then run the script.

• Retrieve your Roboflow project name and version

This code will return a Python object with results from your model.

You can process these results and plot them on an image with the supervision Python package:

detections = sv.Detections.from_inference(results[0])

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()

annotated_image = box_annotator.annotate(
    scene=image, detections=detections)
annotated_image = label_annotator.annotate(
    scene=annotated_image, detections=detections)

sv.plot_image(image=annotated_image, size=(16, 16))

You can run inference on videos with Roboflow Inference and the InferencePipeline feature.

Create a new Python file and add the following code:

# Import the InferencePipeline object
from inference import InferencePipeline
# Import the built in render_boxes sink for visualizing results
from inference.core.interfaces.stream.sinks import render_boxes

# initialize a pipeline object
pipeline = InferencePipeline.init(
    model_id="rock-paper-scissors-sxsw/11", # Roboflow model to use
    video_reference=0, # Path to video, device id (int, usually 0 for built in webcams), or RTSP stream url
    on_prediction=render_boxes, # Function to run after each prediction
)
pipeline.start()
pipeline.join()

Substitute the model name and version with the values associated with your Roboflow account and project, then run the script.

• Retrieve your Roboflow project name and version

This code will run a model on frames from a webcam stream. To use RTSP, set the video_reference value to an RTSP stream URL. To use video, set the video_reference value to a video file path.

Predictions are annotated using the render_boxes helper function. You can specify any function to process each prediction in the on_prediction parameter.

To learn how to define your own callback function with custom logic, refer to the Define Custom Prediction Logic documentation.