95 lines
3.1 KiB
Markdown
95 lines
3.1 KiB
Markdown
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---
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comments: true
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description: Learn how to set up and run YOLOv5 on AzureML. Follow this quickstart guide for easy configuration and model training on an AzureML compute instance.
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keywords: YOLOv5, AzureML, machine learning, compute instance, quickstart, model training, virtual environment, Python, AI, deep learning
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---
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# YOLOv5 🚀 on AzureML
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This guide provides a quickstart to use YOLOv5 from an AzureML compute instance.
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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:
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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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## 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/ultralytics/docs/releases/download/0/create-compute-arrow.avif">
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## Open a Terminal
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Now from the Notebooks view, open a Terminal and select your compute.
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## Setup and run YOLOv5
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Now you can, create a virtual environment:
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```bash
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conda create --name yolov5env -y
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conda activate yolov5env
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conda install pip -y
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```
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Clone YOLOv5 repository with its submodules:
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```bash
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git clone https://github.com/ultralytics/yolov5
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cd yolov5
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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
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```
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Install the required dependencies:
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```bash
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pip install -r yolov5/requirements.txt
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pip install onnx>=1.10.0
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```
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Train the YOLOv5 model:
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```bash
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python train.py
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```
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Validate the model for [Precision](https://www.ultralytics.com/glossary/precision), [Recall](https://www.ultralytics.com/glossary/recall), and mAP
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```bash
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python val.py --weights yolov5s.pt
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```
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Run inference on images and videos:
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```bash
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python detect.py --weights yolov5s.pt --source path/to/images
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```
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Export models to other formats:
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```bash
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python detect.py --weights yolov5s.pt --source path/to/images
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```
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## Notes on using a notebook
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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.
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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.
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For example:
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```bash
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%%bash
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source activate newenv
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python val.py --weights yolov5s.pt
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```
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