Run a training script as a command job in Azure Machine Learning

A notebook is ideal for experimentation and development. Once you’ve developed a machine learning model and it’s ready for production, you’ll want to train it with a script. You can run a script as a command job.

In this exercise, you’ll test a script and then run it as a command job.

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 run a command job.

Create the workspace and compute resources

To create the Azure Machine Learning workspace, a compute instance, and a compute cluster, 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/08
 ./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 and cluster you created in the previous section exist. The compute instance should be running, the cluster should be idle and have 0 nodes running.
In the Compute instances tab, find your compute instance, and select the Terminal application.
In the terminal, install the Python SDK on the compute instance by running the following commands in the terminal:
```
 pip uninstall azure-ai-ml
 pip install azure-ai-ml
```
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 notebooks, 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.

Convert a notebook to a script

Using a notebook attached to a compute instance is ideal for experimentation and development as it allows you to immediately run code you’ve written and review its output. To move from development to production, you’ll want to use scripts. As a first step, you can use the Azure Machine Learning studio to convert your notebook to a script.

Open the Labs/08/src/Train classification model.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 to explore the code and train a model.
Select the ☰ icon at the top of the notebook to view the notebook menu.
Expand Export as, and select Python (.py) to convert the notebook to a Python script.
Name the new file train-classification-model.
Once the new file is created, the script should automatically be opened. Explore the file and note that it contains the same code as the notebook.
Select the ▷▷ icon at the top of the notebook to save and run the script in the terminal.
The script is initiated by the command python train-classification-model.py and the output should be shown below the command.

Test a script with the terminal

After converting a notebook to a script, you may want to further refine it. One best practice when working with scripts, is to use functions. When your script consists of functions, it’s easier to unit test your code. When you use functions, your script will consist of blocks of code, each block performing a specific task.

Open the Labs/08/src/train-model-parameters.py script and explore its contents. Note that there is a main function which includes four other functions:
- Read data
- Split data
- Train model
- Evaluate model
After the main function, each function is defined. Note how each function defines the expected input and output.
Select the ▷▷ icon at the top of the notebook to save and run the script in the terminal. You should get an error after Reading data… telling you that it couldn’t get the data because the file path was invalid.

Scripts allow you to parameterize your code to easily change the input data or parameters. In this case, the script expects an input parameter for the data path which we didn’t provide. You can find the defined and expected parameters at the end of the script in the parse_args() function.

There are two input parameters defined:
- –training_data which expects a string.
- –reg_rate which expects a number, but has a default value of 0.01.
To run the script successfully, you’ll need to specify the value for the training data parameters. Let’s do that by referring to the diabetes.csv file which is stored in the same folder as the training script.

In the terminal, run the following commands:

 cd azure-ml-labs/Labs/08/src/
 python train-model-parameters.py --training_data diabetes.csv

The script should successfully run and as a result, the output should show the accuracy and AUC of the trained model.

Testing the script in the terminal is ideal for verifying whether the script works as expected. If there is any issue with the code, you’ll receive an error in the terminal.

Optionally, edit the code to force an error and run the command again in the terminal to run the script. For example, remove the line import pandas as pd, save and run the script with the input parameter to review the error message.

Run a script as a command job

If you know your script works, you can run it as a command job. By running your script as a command job, you’ll be able to track all the inputs and outputs of the script.

Open the Labs/08/Run script as command job.ipynb notebook.
Run all cells in the notebook.
In the Azure Machine Learning studio, navigate to the Jobs page.
Navigate to the diabetes-train-script job to explore the overview of the command job you ran.
Navigate to the Code tab. All contents of the src folder, which was the value of the code parameter of the command job, are copied here. You can review the training script which was executed by the command job.
Navigate to the Outputs + logs tab.
Open the std_log.txt file and explore its contents. The content of this file is the output of the command. Remember the same output was shown in the terminal when you tested the script there. If the job is unsuccessful because of an issue with the script, the error message will be shown here.

Optionally, edit the code to force an error and use the notebook to initiate the command job again. For example, remove the line import pandas as pd from the script and save the script. Or, edit the command job configuration to explore the error messages when something is wrong with the job configuration itself instead of the script.

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.