How to use Azure ML environments

This project shows four different ways to define an environment in Azure ML: using a curated environment, extending a curated environment, extending a base-image, and extending a container from Docker Hub.

Setup

• You need to have an Azure subscription. You can get a free subscription to try it out.
• Create a resource group.
• Create a new machine learning workspace by following the "Create the workspace" section of the documentation. Keep in mind that you'll be creating a "machine learning workspace" Azure resource, not a "workspace" Azure resource, which is entirely different!
• Install the Azure CLI by following the instructions in the documentation.
• Install the ML extension to the Azure CLI by following the "Installation" section of the documentation.
• Install and activate the conda environment by executing the following commands:

conda env create -f environment.yml
conda activate aml_environment

• Within VS Code, go to the Command Palette clicking "Ctrl + Shift + P," type "Python: Select Interpreter," and select the environment that matches the name of this project.
• In a terminal window, log in to Azure by executing az login.
• Set your default subscription by executing az account set -s "<YOUR_SUBSCRIPTION_NAME_OR_ID>". You can verify your default subscription by executing az account show, or by looking at ~/.azure/azureProfile.json.
• Set your default resource group and workspace by executing az configure --defaults group="<YOUR_RESOURCE_GROUP>" workspace="<YOUR_WORKSPACE>". You can verify your defaults by executing az configure --list-defaults or by looking at ~/.azure/config.
• You can now open the Azure Machine Learning studio, where you'll be able to see and manage all the machine learning resources we'll be creating.
• Optionally, you can install the Azure Machine Learning extension for VS Code. Log in by clicking on "Azure" in the left-hand menu, and then clicking on "Sign in to Azure."

Train locally

Under "Run and Debug" on VS Code's left navigation, choose the "Train locally" run configuration and press F5.

Create environments in the cloud and use them to train in the cloud

cd aml_environment

Create the compute cluster.

az ml compute create -f cloud/cluster-gpu.yml

Create the dataset.

az ml data create -f cloud/data.yml

Create the environments and run the training jobs that use the environments.

az ml job create -f cloud/environment_1/job.yml

az ml environment create -f cloud/environment_2/environment.yml
az ml job create -f cloud/environment_2/job.yml

az ml environment create -f cloud/environment_3/environment.yml
az ml job create -f cloud/environment_3/job.yml

az ml environment create -f cloud/environment_4/environment.yml
az ml job create -f cloud/environment_4/job.yml