Getting Started with Azure Machine Learning Python SDK¶
This documentation provides examples and guidance on how to use the Azure Machine Learning Python SDK.
Prerequisites¶
An Azure subscription. If you don’t have an Azure subscription, create a free account before you begin.
A terminal and Python >=3.6, <3.9.
Set up¶
Clone the Azure Machine Learning examples repository and install required packages:
git clone https://github.com/Azure/azureml-examples --depth 1
cd azureml-examples/python-sdk
pip install --upgrade -r requirements.txt
To create or setup a workspace with the assets used in these examples, run the setup script.
If you do not have an Azure ML workspace, run python setup-workspace.py –subscription-id $ID, where $ID is your Azure subscription id. A resource group, Azure ML workspace, and other necessary resources will be created in the subscription.
If you have an Azure ML Workspace, install the Azure ML CLI and run az ml folder attach -w $WS -g $RG, where $WS and $RG are the workspace and resource group names.
Run python setup-workspace.py -h to see other arguments.
Getting started¶
To get started, see the introductory tutorial which uses Azure ML to:
run a “hello world” job on cloud compute, demonstrating the basics
run a series of PyTorch training jobs on cloud compute, demonstrating mlflow tracking & using cloud data
These concepts are sufficient to understand all examples in this repository, which are listed below.
Examples¶
You can find the examples in the Azure Machine Learning examples repository.
Note
The examples provided in the repository are for illustrative purposes and may not represent best practices for structuring Python applications.