4.0 KiB
| comments | description | keywords |
|---|---|---|
| true | Azure Machine Learning YOLOv8 quickstart | Ultralytics, YOLO, Deep Learning, Object detection, quickstart, Azure, AzureML |
YOLOv8 🚀 on AzureML
Note that this guide is only for quick trials from a compute terminal or from a Notebook. If you want to unlock the full power AzureML, you can find the documentation to:
- Create a data asset
- Create an AzureML job
- Register a model
- Train YOLOv8 with the AzureML Python SDK
- Train YOLOv8 with the Azureml cli
Prerequisites
You need an AzureML workspace.
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 virtualenv
Create your conda virtualenv and install pip in it:
conda create --name yolov8env -y
conda activate yolov8env
conda install pip -y
Install the required dependencies:
cd ultralytics
pip install -r requirements.txt
pip install ultralytics
pip install onnx>=1.12.0
Perform YOLOv8 tasks
Predict:
yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
Train a detection model for 10 epochs with an initial learning_rate of 0.01:
yolo train data=coco128.yaml model=yolov8n.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, you need to create a new ipykernel that will be used by your notebook to manage your dependencies:
conda create --name yolov8env -y
conda activate yolov8env
conda install pip -y
conda install ipykernel -y
python -m ipykernel install --user --name yolov8env --display-name "yolov8env"
Close your terminal and create a new notebook. From your Notebook, you can select the new kernel.
Then you can open a Notebook cell and install the required dependencies:
%%bash
source activate yolov8env
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.
Run some predictions using the Ultralytics CLI:
%%bash
source activate yolov8env
yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
Or with the Ultralytics Python interface, for example to train the model:
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load an official YOLOv8n model
# Use the model
model.train(data="coco128.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