YOLO26 🚀 on AzureML

What is Azure?

Azure is Microsoft's cloud computing platform, designed to help organizations move their workloads to the cloud from on-premises data centers. With the full spectrum of cloud services including those for computing, databases, analytics, machine learning, and networking, users can pick and choose from these services to develop and scale new applications, or run existing applications, in the public cloud.

What is Azure Machine Learning (AzureML)?

Azure Machine Learning (AzureML) is a fully managed cloud service for building, training, and deploying machine learning models at scale. It provides automated machine learning, drag-and-drop model training, and a Python SDK for full programmatic control over your models.

How Does AzureML Benefit YOLO Users?

AzureML lets you train and deploy Ultralytics YOLO26 models in the cloud, from quick prototypes to large-scale runs. With it you can:

Easily manage large datasets and computational resources for training.
Utilize built-in tools for data preprocessing, feature selection, and model training.
Collaborate more efficiently with capabilities for MLOps (Machine Learning Operations), including but not limited to monitoring, auditing, and versioning of models and data.

In the subsequent sections, you will find a quickstart guide detailing how to run YOLO26 object detection models using AzureML, either from a compute terminal or a notebook.

Prerequisites

Before you can get started, make sure you have access to an AzureML workspace. If you don't have one, you can create a new AzureML workspace by following Azure's official documentation. This workspace acts as a centralized place to manage all AzureML resources.

Create a Compute Instance

From your AzureML workspace, select Compute > Compute instances > New, select the instance with the resources you need.

Quickstart from Terminal

Start your compute and open a Terminal:

Create a Virtual Environment

Create a conda virtual environment and install pip in it:

bash

conda create --name yolo26env -y python=3.12
conda activate yolo26env
conda install pip -y

!!! warning "Python version"

Python 3.13 currently has dependency issues on AzureML, so use Python 3.12 instead.

Install the required dependencies:

bash

pip install ultralytics onnx

Perform YOLO26 Tasks

Predict:

bash

yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'

Train a detection model for 10 epochs with an initial learning_rate of 0.01:

bash

yolo train data=coco8.yaml model=yolo26n.pt epochs=10 lr0=0.01

You can find more instructions to use the Ultralytics CLI here.

Quickstart from a Notebook

Create a New IPython Kernel

Open the compute Terminal.

From your compute terminal, create a new ipykernel using Python 3.12 that will be used by your notebook to manage dependencies:

bash

conda create --name yolo26env -y python=3.12
conda activate yolo26env
conda install pip -y
conda install ipykernel -y
python -m ipykernel install --user --name yolo26env --display-name "yolo26env"

Close your terminal and create a new notebook. From your notebook, select the newly created kernel.

Then open a notebook cell and install the required dependencies:

bash

%%bash
source activate yolo26env
pip install ultralytics onnx

!!! note "Activate the environment in every cell"

Run `source activate yolo26env` at the top of every `%%bash` cell so the cell uses the intended environment.

Run some predictions using the Ultralytics CLI:

bash

%%bash
source activate yolo26env
yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'

Or with the Ultralytics Python interface, for example to train the model:

python

from ultralytics import YOLO

# Load a model
model = YOLO("yolo26n.pt")  # load an official YOLO26n model

# Use the model
model.train(data="coco8.yaml", epochs=3)  # train the model
metrics = model.val()  # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
path = model.export(format="onnx")  # export the model to ONNX format

You can use either the Ultralytics CLI or Python interface to run YOLO26 tasks. The Python example above also exports the trained model to ONNX for deployment.

By following these steps, you can get YOLO26 running quickly on AzureML. For more advanced workflows, see the AzureML documentation.

Explore More with AzureML

This guide covers the basics of running YOLO26 on AzureML. To go further, explore these resources:

Create a Data Asset: Set up and manage your data assets within the AzureML environment.
Initiate an AzureML Job: Kickstart your machine learning training jobs on AzureML.
Register a Model: Manage model registration, versioning, and deployment.
Modal Quickstart: Run YOLO26 on Modal's serverless GPU cloud as an alternative to AzureML.

FAQ

How do I run YOLO26 on AzureML for model training?

To run YOLO26 on AzureML for training, create a compute instance, set up a Conda environment, install Ultralytics, and run the training command:

Create a Compute Instance: From your AzureML workspace, navigate to Compute > Compute instances > New, and select the required instance.
Set Up the Environment: Start your compute instance, open a terminal, and create a Conda environment with Python 3.12 (Python 3.13 currently has dependency issues on AzureML):
bash
```
conda create --name yolo26env -y python=3.12
conda activate yolo26env
conda install pip -y
pip install ultralytics onnx
```
Run YOLO26 Tasks: Use the Ultralytics CLI to train your model:
bash
```
yolo train data=coco8.yaml model=yolo26n.pt epochs=10 lr0=0.01
```

For more details, you can refer to the instructions to use the Ultralytics CLI.

What are the benefits of using AzureML for YOLO26 training?

AzureML provides a robust and efficient ecosystem for training YOLO26 models:

Scalability: Easily scale your compute resources as your data and model complexity grows.
MLOps Integration: Utilize features like versioning, monitoring, and auditing to streamline ML operations.
Collaboration: Share and manage resources within teams, enhancing collaborative workflows.

These advantages make AzureML an ideal platform for projects ranging from quick prototypes to large-scale deployments. For more tips, check out AzureML Jobs.

How do I troubleshoot common issues when running YOLO26 on AzureML?

To troubleshoot YOLO26 on AzureML, verify your dependencies are installed, confirm your Conda environment is activated, and ensure your compute instance has enough resources:

Dependency Issues: Ensure all required packages are installed with pip install ultralytics onnx.
Environment Setup: Verify that your conda environment is correctly activated before running commands.
Resource Allocation: Make sure your compute instances have sufficient resources to handle the training workload.

For additional guidance, review our YOLO Common Issues documentation.

Can I use both the Ultralytics CLI and Python interface on AzureML?

Yes, AzureML allows you to use both the Ultralytics CLI and the Python interface seamlessly:

CLI: Ideal for quick tasks and running standard scripts directly from the terminal.
bash
```
yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'
```
Python Interface: Useful for more complex tasks requiring custom coding and integration within notebooks.
python
```
from ultralytics import YOLO

model = YOLO("yolo26n.pt")
model.train(data="coco8.yaml", epochs=3)
```

For step-by-step instructions, refer to the CLI quickstart guide and the Python quickstart guide.

What is the advantage of using Ultralytics YOLO26 over other object detection models?

Ultralytics YOLO26 offers several unique advantages over competing object detection models:

Speed: Faster inference and training times compared to models like Faster R-CNN and SSD.
Accuracy: High accuracy in detection tasks with features like anchor-free design and enhanced augmentation strategies.
Ease of Use: Intuitive API and CLI for quick setup, making it accessible both to beginners and experts.

To explore more about YOLO26's features, visit the Ultralytics YOLO page for detailed insights.