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docs/en/guides/azureml-quickstart.md
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@ -1,10 +1,10 @@
---
comments: true
description: Learn how to run YOLOv8 on AzureML. Quickstart instructions for terminal and notebooks to harness Azure's cloud computing for efficient model training.
keywords: YOLOv8, AzureML, machine learning, cloud computing, quickstart, terminal, notebooks, model training, Python SDK, AI, Ultralytics
description: Learn how to run YOLO11 on AzureML. Quickstart instructions for terminal and notebooks to harness Azure's cloud computing for efficient model training.
keywords: YOLO11, AzureML, machine learning, cloud computing, quickstart, terminal, notebooks, model training, Python SDK, AI, Ultralytics
---
# YOLOv8 🚀 on AzureML
# YOLO11 🚀 on AzureML
## What is Azure?
@ -22,7 +22,7 @@ For users of YOLO (You Only Look Once), AzureML provides a robust, scalable, and
- 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 YOLOv8 object detection models using AzureML, either from a compute terminal or a notebook.
In the subsequent sections, you will find a quickstart guide detailing how to run YOLO11 object detection models using AzureML, either from a compute terminal or a notebook.
## Prerequisites
@ -49,8 +49,8 @@ Start your compute and open a Terminal:
Create your conda virtualenv and install pip in it:
```bash
conda create --name yolov8env -y
conda activate yolov8env
conda create --name yolo11env -y
conda activate yolo11env
conda install pip -y
```
@ -63,18 +63,18 @@ pip install ultralytics
pip install onnx>=1.12.0
```
### Perform YOLOv8 tasks
### Perform YOLO11 tasks
Predict:
```bash
yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
```
Train a detection model for 10 [epochs](https://www.ultralytics.com/glossary/epoch) with an initial learning_rate of 0.01:
```bash
yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
```
You can find more [instructions to use the Ultralytics CLI here](../quickstart.md#use-ultralytics-with-cli).
@ -92,11 +92,11 @@ Open the compute Terminal.
From your compute terminal, you need to create a new ipykernel that will be used by your notebook to manage your dependencies:
```bash
conda create --name yolov8env -y
conda activate yolov8env
conda create --name yolo11env -y
conda activate yolo11env
conda install pip -y
conda install ipykernel -y
python -m ipykernel install --user --name yolov8env --display-name "yolov8env"
python -m ipykernel install --user --name yolo11env --display-name "yolo11env"
```
Close your terminal and create a new notebook. From your Notebook, you can select the new kernel.
@ -105,21 +105,21 @@ Then you can open a Notebook cell and install the required dependencies:
```bash
%%bash
source activate yolov8env
source activate yolo11env
cd ultralytics
pip install -r requirements.txt
pip install ultralytics
pip install onnx>=1.12.0
```
Note that we need to use the `source activate yolov8env` for all the %%bash cells, to make sure that the %%bash cell uses environment we want.
Note that we need to use the `source activate yolo11env` for all the %%bash cells, to make sure that the %%bash cell uses environment we want.
Run some predictions using the [Ultralytics CLI](../quickstart.md#use-ultralytics-with-cli):
```bash
%%bash
source activate yolov8env
yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
source activate yolo11env
yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
```
Or with the [Ultralytics Python interface](../quickstart.md#use-ultralytics-with-python), for example to train the model:
@ -128,7 +128,7 @@ Or with the [Ultralytics Python interface](../quickstart.md#use-ultralytics-with
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load an official YOLOv8n model
model = YOLO("yolo11n.pt") # load an official YOLO11n model
# Use the model
model.train(data="coco8.yaml", epochs=3) # train the model
@ -137,47 +137,47 @@ 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 for running YOLOv8 tasks, as described in the terminal section above.
You can use either the Ultralytics CLI or Python interface for running YOLO11 tasks, as described in the terminal section above.
By following these steps, you should be able to get YOLOv8 running quickly on AzureML for quick trials. For more advanced uses, you may refer to the full AzureML documentation linked at the beginning of this guide.
By following these steps, you should be able to get YOLO11 running quickly on AzureML for quick trials. For more advanced uses, you may refer to the full AzureML documentation linked at the beginning of this guide.
## Explore More with AzureML
This guide serves as an introduction to get you up and running with YOLOv8 on AzureML. However, it only scratches the surface of what AzureML can offer. To delve deeper and unlock the full potential of AzureML for your machine learning projects, consider exploring the following resources:
This guide serves as an introduction to get you up and running with YOLO11 on AzureML. However, it only scratches the surface of what AzureML can offer. To delve deeper and unlock the full potential of AzureML for your machine learning projects, consider exploring the following resources:
- [Create a Data Asset](https://learn.microsoft.com/azure/machine-learning/how-to-create-data-assets): Learn how to set up and manage your data assets effectively within the AzureML environment.
- [Initiate an AzureML Job](https://learn.microsoft.com/azure/machine-learning/how-to-train-model): Get a comprehensive understanding of how to kickstart your machine learning training jobs on AzureML.
- [Register a Model](https://learn.microsoft.com/azure/machine-learning/how-to-manage-models): Familiarize yourself with model management practices including registration, versioning, and deployment.
- [Train YOLOv8 with AzureML Python SDK](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azure-machine-learning-python-sdk-8268696be8ba): Explore a step-by-step guide on using the AzureML Python SDK to train your YOLOv8 models.
- [Train YOLOv8 with AzureML CLI](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azureml-and-the-az-cli-73d3c870ba8e): Discover how to utilize the command-line interface for streamlined training and management of YOLOv8 models on AzureML.
- [Train YOLO11 with AzureML Python SDK](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azure-machine-learning-python-sdk-8268696be8ba): Explore a step-by-step guide on using the AzureML Python SDK to train your YOLO11 models.
- [Train YOLO11 with AzureML CLI](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azureml-and-the-az-cli-73d3c870ba8e): Discover how to utilize the command-line interface for streamlined training and management of YOLO11 models on AzureML.
## FAQ
### How do I run YOLOv8 on AzureML for model training?
### How do I run YOLO11 on AzureML for model training?
Running YOLOv8 on AzureML for model training involves several steps:
Running YOLO11 on AzureML for model training involves several steps:
1. **Create a Compute Instance**: From your AzureML workspace, navigate to Compute > Compute instances > New, and select the required instance.
2. **Setup Environment**: Start your compute instance, open a terminal, and create a conda environment:
```bash
conda create --name yolov8env -y
conda activate yolov8env
conda create --name yolo11env -y
conda activate yolo11env
conda install pip -y
pip install ultralytics onnx>=1.12.0
```
3. **Run YOLOv8 Tasks**: Use the Ultralytics CLI to train your model:
3. **Run YOLO11 Tasks**: Use the Ultralytics CLI to train your model:
```bash
yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
```
For more details, you can refer to the [instructions to use the Ultralytics CLI](../quickstart.md#use-ultralytics-with-cli).
### What are the benefits of using AzureML for YOLOv8 training?
### What are the benefits of using AzureML for YOLO11 training?
AzureML provides a robust and efficient ecosystem for training YOLOv8 models:
AzureML provides a robust and efficient ecosystem for training YOLO11 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.
@ -185,9 +185,9 @@ AzureML provides a robust and efficient ecosystem for training YOLOv8 models:
These advantages make AzureML an ideal platform for projects ranging from quick prototypes to large-scale deployments. For more tips, check out [AzureML Jobs](https://learn.microsoft.com/azure/machine-learning/how-to-train-model).
### How do I troubleshoot common issues when running YOLOv8 on AzureML?
### How do I troubleshoot common issues when running YOLO11 on AzureML?
Troubleshooting common issues with YOLOv8 on AzureML can involve the following steps:
Troubleshooting common issues with YOLO11 on AzureML can involve the following steps:
- **Dependency Issues**: Ensure all required packages are installed. Refer to the `requirements.txt` file for dependencies.
- **Environment Setup**: Verify that your conda environment is correctly activated before running commands.
@ -202,7 +202,7 @@ Yes, AzureML allows you to use both the Ultralytics CLI and the Python interface
- **CLI**: Ideal for quick tasks and running standard scripts directly from the terminal.
```bash
yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
```
- **Python Interface**: Useful for more complex tasks requiring custom coding and integration within notebooks.
@ -210,18 +210,18 @@ Yes, AzureML allows you to use both the Ultralytics CLI and the Python interface
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
model.train(data="coco8.yaml", epochs=3)
```
Refer to the quickstart guides for more detailed instructions [here](../quickstart.md#use-ultralytics-with-cli) and [here](../quickstart.md#use-ultralytics-with-python).
### What is the advantage of using Ultralytics YOLOv8 over other [object detection](https://www.ultralytics.com/glossary/object-detection) models?
### What is the advantage of using Ultralytics YOLO11 over other [object detection](https://www.ultralytics.com/glossary/object-detection) models?
Ultralytics YOLOv8 offers several unique advantages over competing object detection models:
Ultralytics YOLO11 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](https://www.ultralytics.com/glossary/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 YOLOv8's features, visit the [Ultralytics YOLO](https://www.ultralytics.com/yolo) page for detailed insights.
To explore more about YOLO11's features, visit the [Ultralytics YOLO](https://www.ultralytics.com/yolo) page for detailed insights.