Add AzureML Quickstart Guides (#4772)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/guides/azureml-quickstart.md

@@ -0,0 +1,122 @@
+---
+comments: true
+description: Azure Machine Learning YOLOv8 quickstart
+keywords: 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](https://learn.microsoft.com/azure/machine-learning/how-to-create-data-assets)
+- [Create an AzureML job](https://learn.microsoft.com/azure/machine-learning/how-to-train-model)
+- [Register a model](https://learn.microsoft.com/azure/machine-learning/how-to-manage-models)
+- [Train YOLOv8 with the AzureML Python SDK](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azure-machine-learning-python-sdk-8268696be8ba)
+- [Train YOLOv8 with the Azureml cli](https://medium.com/@ouphi/how-to-train-the-yolov8-model-with-azureml-and-the-az-cli-73d3c870ba8e)
+
+## Prerequisites
+
+You need an [AzureML workspace](https://learn.microsoft.com/azure/machine-learning/concept-workspace?view=azureml-api-2).
+
+## Create a compute instance
+
+From your AzureML workspace, select Compute > Compute instances > New, select the instance with the resources you need.
+
+<img width="1741" alt="create-compute-arrow" src="https://github.com/ouphi/ultralytics/assets/17216799/3e92fcc0-a08e-41a4-af81-d289cfe3b8f2">
+
+## Quickstart from Terminal
+
+Start your compute and open a Terminal:
+
+![open-terminal](https://github.com/ouphi/ultralytics/assets/17216799/635152f1-f4a3-4261-b111-d416cb5ef357)
+
+### Create virtualenv
+
+Create your conda virtualenv and install pip in it:
+
+```bash
+conda create --name yolov8env -y
+conda activate yolov8env
+conda install pip -y
+```
+
+Install the required dependencies:
+
+```bash
+cd ultralytics
+pip install -r requirements.txt
+pip install ultralytics
+pip install onnx>=1.12.0
+```
+
+### Perform YOLOv8 tasks
+
+Predict:
+
+```bash
+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:
+
+```bash
+yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
+```
+
+You can find more [instructions to use the Ultralytics cli here](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-cli).
+
+## Quickstart from a Notebook
+
+### Create a new IPython kernel
+
+Open the compute Terminal.
+
+![open-terminal](https://github.com/ouphi/ultralytics/assets/17216799/635152f1-f4a3-4261-b111-d416cb5ef357)
+
+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 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
+%%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](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-cli):
+
+```bash
+%%bash
+source activate yolov8env
+yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
+```
+
+Or with the [Ultralytics Python interface](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-python), for example to train the model:
+
+```python
+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
+```

docs/guides/index.md

@@ -17,6 +17,7 @@ Here's a compilation of in-depth guides to help you master different aspects of
 * [K-Fold Cross Validation](kfold-cross-validation.md) 🚀 NEW: Learn how to improve model generalization using K-Fold cross-validation technique.
 * [Hyperparameter Tuning](hyperparameter-tuning.md) 🚀 NEW: Discover how to optimize your YOLO models by fine-tuning hyperparameters using the Tuner class and genetic evolution algorithms.
 * [Using YOLOv8 with SAHI for Sliced Inference](sahi-tiled-inference.md) 🚀 NEW: Comprehensive guide on leveraging SAHI's sliced inference capabilities with YOLOv8 for object detection in high-resolution images.
+* [AzureML Quickstart](azureml-quickstart.md) 🚀 NEW: Get up and running with Ultralytics YOLO models on Microsoft's Azure Machine Learning platform. Learn how to train, deploy, and scale your object detection projects in the cloud.
 
 ## Contribute to Our Guides
 

docs/yolov5/environments/azureml_quickstart_tutorial.md

@@ -0,0 +1,95 @@
+---
+comments: true
+description: Azure Machine Learning YOLOv5 quickstart
+keywords: Ultralytics, YOLO, Deep Learning, Object detection, quickstart, Azure, AzureML
+---
+
+# YOLOv5 🚀 on AzureML
+
+This guide provides a quickstart to use YOLOv5 from an AzureML compute instance.
+
+Note that this guide is a quickstart for quick trials. If you want to unlock the full power AzureML, you can find the documentation to:
+
+- [Create a data asset](https://learn.microsoft.com/azure/machine-learning/how-to-create-data-assets)
+- [Create an AzureML job](https://learn.microsoft.com/azure/machine-learning/how-to-train-model)
+- [Register a model](https://learn.microsoft.com/azure/machine-learning/how-to-manage-models)
+
+## Prerequisites
+
+You need an [AzureML workspace](https://learn.microsoft.com/azure/machine-learning/concept-workspace?view=azureml-api-2).
+
+## Create a compute instance
+
+From your AzureML workspace, select Compute > Compute instances > New, select the instance with the resources you need.
+
+<img width="1741" alt="create-compute-arrow" src="https://github.com/ouphi/ultralytics/assets/17216799/3e92fcc0-a08e-41a4-af81-d289cfe3b8f2">
+
+## Open a Terminal
+
+Now from the Notebooks view, open a Terminal and select your compute.
+
+![open-terminal-arrow](https://github.com/ouphi/ultralytics/assets/17216799/c4697143-7234-4a04-89ea-9084ed9c6312)
+
+## Setup and run YOLOv5
+
+Now you can, create a virtual environment:
+
+```bash
+conda create --name yolov5env -y
+conda activate yolov5env
+conda install pip -y
+```
+
+Clone YOLOv5 repository with its submodules:
+
+```bash
+git clone https://github.com/ultralytics/yolov5
+cd yolov5
+git submodule update --init --recursive # Note that you might have a message asking you to add your folder as a safe.directory just copy the recommended command   
+```
+
+Install the required dependencies:
+
+```bash
+pip install -r yolov5/requirements.txt
+pip install onnx>=1.10.0
+```
+
+Train the YOLOv5 model:
+
+```bash
+python train.py
+```
+
+Validate the model for Precision, Recall, and mAP
+
+```bash
+python val.py --weights yolov5s.pt
+```
+
+Run inference on images and videos:
+
+```bash
+python detect.py --weights yolov5s.pt --source path/to/images
+```
+
+Export models to other formats:
+
+```bash
+python detect.py --weights yolov5s.pt --source path/to/images
+```
+
+## Notes on using a notebook
+
+Note that if you want to run these commands from a Notebook, you need to [create a new Kernel](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-terminal?view=azureml-api-2#add-new-kernels)
+and select your new Kernel on the top of your Notebook.
+
+If you create Python cells it will automatically use your custom environment, but if you add bash cells, you will need to run `source activate <your-env>` on each of these cells to make sure it uses your custom environment.
+
+For example:
+
+```bash
+%%bash
+source activate newenv
+python val.py --weights yolov5s.pt
+```

docs/yolov5/index.md

@@ -57,6 +57,7 @@ YOLOv5 is designed to be run in the following up-to-date, verified environments,
   GPU: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
 - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](environments/google_cloud_quickstart_tutorial.md)
 - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](environments/aws_quickstart_tutorial.md)
+- **Azure** Azure Machine Learning. See [AzureML Quickstart Guide](environments/azureml_quickstart_tutorial.md)
 - **Docker Image**. See [Docker Quickstart Guide](environments/docker_image_quickstart_tutorial.md) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
 
 ## Status

mkdocs.yml

@@ -217,6 +217,7 @@ nav:
       - K-Fold Cross Validation: guides/kfold-cross-validation.md
       - Hyperparameter Tuning: guides/hyperparameter-tuning.md
       - SAHI Tiled Inference: guides/sahi-tiled-inference.md
+      - AzureML Quickstart: guides/azureml-quickstart.md
   - Integrations:
       - integrations/index.md
       - OpenVINO: integrations/openvino.md
@@ -234,6 +235,7 @@ nav:
       - Environments:
           - Amazon Web Services (AWS): yolov5/environments/aws_quickstart_tutorial.md
           - Google Cloud (GCP): yolov5/environments/google_cloud_quickstart_tutorial.md
+          - AzureML: yolov5/environments/azureml_quickstart_tutorial.md
           - Docker Image: yolov5/environments/docker_image_quickstart_tutorial.md
       - Tutorials:
           - Train Custom Data: yolov5/tutorials/train_custom_data.md

ultralytics/utils/callbacks/base.py

@@ -214,7 +214,7 @@ def add_integration_callbacks(instance):
         callbacks_list.extend([clear_cb, comet_cb, dvc_cb, mlflow_cb, neptune_cb, tune_cb, tb_cb, wb_cb])
 
     # Load export callbacks (patch to avoid CoreML protobuf error)
-    if 'Exporter' in instance.__class__.__name__ and instance.args.format in ('coreml', 'mlmodel'):
+    if 'Exporter' in instance.__class__.__name__:
         from .tensorboard import callbacks as tb_cb
         callbacks_list.append(tb_cb)