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123 lines
4.0 KiB
Markdown
123 lines
4.0 KiB
Markdown
---
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comments: true
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description: Azure Machine Learning YOLOv8 quickstart
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keywords: Ultralytics, YOLO, Deep Learning, Object detection, quickstart, Azure, AzureML
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---
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# YOLOv8 🚀 on AzureML
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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:
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- [Create a data asset](https://learn.microsoft.com/azure/machine-learning/how-to-create-data-assets)
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- [Create an AzureML job](https://learn.microsoft.com/azure/machine-learning/how-to-train-model)
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- [Register a model](https://learn.microsoft.com/azure/machine-learning/how-to-manage-models)
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- [Train YOLOv8 with the AzureML Python SDK](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azure-machine-learning-python-sdk-8268696be8ba)
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- [Train YOLOv8 with the Azureml cli](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azureml-and-the-az-cli-73d3c870ba8e)
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## Prerequisites
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You need an [AzureML workspace](https://learn.microsoft.com/azure/machine-learning/concept-workspace?view=azureml-api-2).
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## Create a compute instance
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From your AzureML workspace, select Compute > Compute instances > New, select the instance with the resources you need.
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<img width="1741" alt="create-compute-arrow" src="https://github.com/ouphi/ultralytics/assets/17216799/3e92fcc0-a08e-41a4-af81-d289cfe3b8f2">
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## Quickstart from Terminal
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Start your compute and open a Terminal:
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### Create virtualenv
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Create your conda virtualenv and install pip in it:
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```bash
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conda create --name yolov8env -y
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conda activate yolov8env
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conda install pip -y
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```
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Install the required dependencies:
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```bash
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cd ultralytics
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pip install -r requirements.txt
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pip install ultralytics
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pip install onnx>=1.12.0
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```
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### Perform YOLOv8 tasks
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Predict:
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```bash
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yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
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```
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Train a detection model for 10 epochs with an initial learning_rate of 0.01:
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```bash
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yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
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```
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You can find more [instructions to use the Ultralytics cli here](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-cli).
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## Quickstart from a Notebook
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### Create a new IPython kernel
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Open the compute Terminal.
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From your compute terminal, you need to create a new ipykernel that will be used by your notebook to manage your dependencies:
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```bash
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conda create --name yolov8env -y
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conda activate yolov8env
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conda install pip -y
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conda install ipykernel -y
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python -m ipykernel install --user --name yolov8env --display-name "yolov8env"
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```
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Close your terminal and create a new notebook. From your Notebook, you can select the new kernel.
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Then you can open a Notebook cell and install the required dependencies:
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```bash
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%%bash
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source activate yolov8env
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cd ultralytics
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pip install -r requirements.txt
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pip install ultralytics
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pip install onnx>=1.12.0
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```
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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.
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Run some predictions using the [Ultralytics CLI](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-cli):
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```bash
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%%bash
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source activate yolov8env
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yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
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```
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Or with the [Ultralytics Python interface](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-python), for example to train the model:
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load an official YOLOv8n model
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# Use the model
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model.train(data="coco128.yaml", epochs=3) # train the model
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metrics = model.val() # evaluate model performance on the validation set
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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path = model.export(format="onnx") # export the model to ONNX format
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```
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