Track model training in notebooks with MLflow

Often, you’ll start a new data science project by experimenting and training multiple models. To track your work and keep an overview of the models you train and how they perform, you can use MLflow tracking.

In this exercise, you’ll MLflow within a notebook running on a compute instance to log model training.

Before you start

You’ll need an Azure subscription in which you have administrative-level access.

Provision an Azure Machine Learning workspace

An Azure Machine Learning workspace provides a central place for managing all resources and assets you need to train and manage your models. You can interact with the Azure Machine Learning workspace through the studio, Python SDK, and Azure CLI.

You’ll use the Azure CLI to provision the workspace and necessary compute, and you’ll use the Python SDK to train a classification model with Automated Machine Learning.

Create the workspace and compute resources

To create the Azure Machine Learning workspace and a compute instance, you’ll use the Azure CLI. All necessary commands are grouped in a Shell script for you to execute.

In a browser, open the Azure portal at https://portal.azure.com/, signing in with your Microsoft account.
Select the [>_] (Cloud Shell) button at the top of the page to the right of the search box. This opens a Cloud Shell pane at the bottom of the portal.
Select Bash if asked. The first time you open the cloud shell, you will be asked to choose the type of shell you want to use (Bash or PowerShell).
Check that the correct subscription is specified and that No storage account required is selected. Select Apply.
In the terminal, enter the following commands to clone this repo:
```
 rm -r azure-ml-labs -f
 git clone https://github.com/MicrosoftLearning/mslearn-azure-ml.git azure-ml-labs
```
Use SHIFT + INSERT to paste your copied code into the Cloud Shell.
After the repo has been cloned, enter the following commands to change to the folder for this lab and run the setup.sh script it contains:
```
 cd azure-ml-labs/Labs/07
 ./setup.sh
```
Ignore any (error) messages that say that the extensions were not installed.
Wait for the script to complete - this typically takes around 5-10 minutes.

Clone the lab materials

When you’ve created the workspace and necessary compute resources, you can open the Azure Machine Learning studio and clone the lab materials into the workspace.

In the Azure portal, navigate to the Azure Machine Learning workspace named mlw-dp100-….
Select the Azure Machine Learning workspace, and in its Overview page, select Launch studio. Another tab will open in your browser to open the Azure Machine Learning studio.
Close any pop-ups that appear in the studio.
Within the Azure Machine Learning studio, navigate to the Compute page and verify that the compute instance you created in the previous section exist. The compute instance should be running.
In the Compute instances tab, find your compute instance, and select the Terminal application.
In the terminal, install the Python SDK and the MLflow library on the compute instance by running the following commands in the terminal:
```
 pip uninstall azure-ai-ml
 pip install azure-ai-ml
 pip install mlflow
```
Ignore any (error) messages that say that the packages couldn’t be found and uninstalled.
Run the following command to clone a Git repository containing a notebook, data, and other files to your workspace:
```
 git clone https://github.com/MicrosoftLearning/mslearn-azure-ml.git azure-ml-labs
```
When the command has completed, in the Files pane, click ↻ to refresh the view and verify that a new Users/your-user-name/azure-ml-labs folder has been created.

Track model training with MLflow

Now that you have all the necessary resources, you can run the notebook to configure and use MLflow when training models in a notebook.

Open the Labs/07/Track model training with MLflow.ipynb notebook.

Select Authenticate and follow the necessary steps if a notification appears asking you to authenticate.
Verify that the notebook uses the Python 3.8 - AzureML kernel.
Run all cells in the notebook.
Review the new job that’s created every time you train a model.

Note: When you train a model, the cell’s output will show a link to the job run. If the link returns an error, you can still review the job run by selecting Jobs on the left side panel.

Delete Azure resources

When you finish exploring Azure Machine Learning, you should delete the resources you’ve created to avoid unnecessary Azure costs.

Close the Azure Machine Learning studio tab and return to the Azure portal.
In the Azure portal, on the Home page, select Resource groups.
Select the rg-dp100-… resource group.
At the top of the Overview page for your resource group, select Delete resource group.
Enter the resource group name to confirm you want to delete it, and select Delete.