This project shows how to train a Fashion MNIST model using an Azure ML pipeline, and how to deploy it using an online managed endpoint. It demonstrates how to create Azure ML resources using the following three methods: the Azure ML Studio UI, the Azure ML Python SDK, and the Azure ML CLI. It uses MLflow for tracking and model representation.

To learn more about the code in this repo, check out the accompanying blog post: https://bea.stollnitz.com/blog/aml-pipeline-mixed/

• 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_pipeline_mixed

• 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 --use-device-code.
• 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.
• Add a config.json file to the root of your project (or somewhere in the parent folder hierarchy) containing your Azure subscription ID, resource group, and workspace: { "subscription_id": "<YOUR_SUBSCRIPTION_ID>", "resource_group": "<YOUR_RESOURCE_GROUP>", "workspace_name": "<YOUR_WORKSPACE>" }
• 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.
• Install the Azure Machine Learning extension for VS Code, and log in to it by clicking on "Azure" in the left-hand menu, and then clicking on "Sign in to Azure."

• Under "Run and Debug" on VS Code's left navigation, choose the "Train locally" run configuration and press F5.
• Do the same for the "Test locally" run configuration.
• You can analyze the metrics logged in the "mlruns" directory with the following command:

mlflow ui

Create the compute cluster using the Azure ML Studio UI. Go to "Compute", "Compute clusters", "New."

• Select a Location, such as "West US 2."
• Select a Virtual machine tier, such as "Dedicated."
• Select a Virtual machine type, such as "CPU."
• Select a Virtual machine size, such as "Standard_DS4_v2."
• Click Next.
• Enter a Compute name, such as "cluster-cpu."
• Select a Minimum number of nodes, such as 0.
• Select a Maximum number of nodes, such as 4.
• Click Create.

Look at the "Compute clusters" page. Your newly created cluster should be listed there.

Create the dataset using the Azure ML Studio UI. Go to "Data", "Create", "From local files."

• Enter a Name, such as "data-fashion-mnist."
• Change the "Dataset type" to "File."
• Click Next.
• Click "Browse," "Browse folder," and select the "data" folder generated when you ran the "train.py" file locally.
• Click Upload.
• Click Next.
• Click Create.

Select the "Train in the cloud" run configuration and press F5. Go to the Azure ML Studio and wait until the Job completes.

When the pipeline is done running, it prints the run id at the end. For example:

Execution Summary
=================
RunId: blue_soca_jkf490bx8y

Set the shell variable run_id to that run id. For example:

run_id=blue_soca_jkf490bx8y

Create the Azure ML model using the CLI.

cd aml_pipeline_mixed
az ml model create --name model-pipeline-mixed --version 1 --path "azureml://jobs/$run_id/outputs/model_dir" --type mlflow_model

You don't need to download the trained model, but here's how you would do it if you wanted to:

az ml job download --name $run_id --output-name "model_dir"

Create the deployment and endpoint using the CLI:

az ml online-endpoint create -f cloud/endpoint.yml
az ml online-deployment create -f cloud/deployment.yml --all-traffic

Invoke the endpoint.

az ml online-endpoint invoke --name endpoint-pipeline-mixed --request-file test_data/images_azureml.json

Clean up the endpoint to avoid getting charged.

az ml online-endpoint delete --name endpoint-pipeline-mixed -y

• Train with the SDK
• Train with the CLI