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How to train a model in Azure ML using components and a pipeline

This project shows how to train a Fashion MNIST model using a Azure ML components and a pipeline, and how to deploy it using a managed online endpoint. It uses MLflow for tracking and model representation.

Blog post

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

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_pipeline_cli
  • 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.
  • 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."

Training and inference on your development machine

  • 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 look at the logs generated by the code in these files with the following command:

mlflow ui
  • Make a local prediction using the trained mlflow model. You can use either csv or json files:
cd aml_pipeline_cli
mlflow models predict --model-uri "model" --input-path "test_data/images.csv" --content-type csv --env-manager local
mlflow models predict --model-uri "model" --input-path "test_data/images.json" --content-type json --env-manager local

Training and deployment in the cloud

Create the compute cluster.

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

Create the dataset we'll use to train the model.

az ml data create -f cloud/data.yml

Create the components.

az ml component create -f cloud/train.yml
az ml component create -f cloud/test.yml

Create and run the pipeline.

run_id=$(az ml job create -f cloud/pipeline-job.yml --query name -o tsv)

Go to the Azure ML Studio and wait until the Job completes. 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 Azure ML model from the output.

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

Create the endpoint.

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-cli --request-file test_data/images_azureml.json

Clean up the endpoint, to avoid getting charged.

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

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