Tutorial: Get started with Azure Machine Learning in your development environment (part 1 of 4)

• 02/11/2021
• 5 minutes to read
• • a
  • s
  • T
  • s
  • x
  • +2

In this four-part tutorial series, you'll learn the fundamentals of Azure Machine Learning and complete jobs-based Python machine learning tasks on the Azure cloud platform.

In part 1 of this tutorial series, you will:

Prerequisites

• An Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try Azure Machine Learning.
• Anaconda or Miniconda to manage Python virtual environments and install packages.
• If you're not familiar with using conda, see Getting started with conda.

Install the Azure Machine Learning SDK

Throughout this tutorial, you will use the Azure Machine Learning SDK for Python. To avoid Python dependency issues, you'll create an isolated environment. This tutorial series uses conda to create that environment. If you prefer to use other solutions, such as venv, virtualenv, or docker, make sure you use a Python version >=3.5 and < 3.9.

Check if you have conda installed on your system:

conda --version

If this command returns a conda not found error, download and install Miniconda.

Once you have installed Conda, use a terminal or Anaconda Prompt window to create a new environment:

conda create -n tutorial python=3.8

Next, install the Azure Machine Learning SDK into the conda environment you created:

conda activate tutorial
pip install azureml-core

Create a directory structure for code

We recommend that you set up the following simple directory structure for this tutorial:

• tutorial: Top-level directory of the project.
• .azureml: Hidden subdirectory for storing Azure Machine Learning configuration files.

For example, to create this in a bash window:

mkdir tutorial
cd tutorial
mkdir .azureml

Create an Azure Machine Learning workspace

A workspace is a top-level resource for Azure Machine Learning and is a centralized place to:

• Manage resources such as compute.
• Store assets like notebooks, environments, datasets, pipelines, models, and endpoints.
• Collaborate with other team members.

In the top-level directory, tutorial, add a new Python file called 01-create-workspace.py by using the following code. Adapt the parameters (name, subscription ID, resource group, and location) with your preferences.

You can run the code in an interactive session or as a Python file.

# tutorial/01-create-workspace.py
from azureml.core import Workspace

ws = Workspace.create(name='<my_workspace_name>', # provide a name for your workspace
                      subscription_id='<azure-subscription-id>', # provide your subscription ID
                      resource_group='<myresourcegroup>', # provide a resource group name
                      create_resource_group=True,
                      location='<NAME_OF_REGION>') # For example: 'westeurope' or 'eastus2' or 'westus2' or 'southeastasia'.

# write out the workspace details to a configuration file: .azureml/config.json
ws.write_config(path='.azureml')

In the window that has the activated tutorial1 conda environment, run this code from the tutorial directory.

cd <path/to/tutorial>
python ./01-create-workspace.py

After you've successfully run 01-create-workspace.py, your folder structure will look like:

The file .azureml/config.json contains the metadata necessary to connect to your Azure Machine Learning workspace. Namely, it contains your subscription ID, resource group, and workspace name.

Create an Azure Machine Learning compute cluster

Create a Python script in the tutorial top-level directory called 02-create-compute.py. Populate it with the following code to create an Azure Machine Learning compute cluster that will autoscale between zero and four nodes:

# tutorial/02-create-compute.py
from azureml.core import Workspace
from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException

ws = Workspace.from_config() # This automatically looks for a directory .azureml

# Choose a name for your CPU cluster
cpu_cluster_name = "cpu-cluster"

# Verify that the cluster does not exist already
try:
    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)
    print('Found existing cluster, use it.')
except ComputeTargetException:
    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',
                                                           idle_seconds_before_scaledown=2400,
                                                           min_nodes=0,
                                                           max_nodes=4)
    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)

cpu_cluster.wait_for_completion(show_output=True)

In the window that has the activated tutorial1 conda environment, run the Python file:

python ./02-create-compute.py

Your folder structure will now look as follows:

View in the studio

Sign in to Azure Machine Learning studio to view the workspace and compute instance you created.

1. Select the Subscription you used to create the workspace.
2. Select the Machine Learning workspace you created, tutorial-ws.
3. Once the workspace loads, on the left side, select Compute.
4. At the top, select the Compute clusters tab.

This view shows the provisioned compute cluster, along with the number of idle nodes, busy nodes, and unprovisioned nodes. Since you haven't used the cluster yet, all the nodes are currently unprovisioned.

Next steps

In this setup tutorial, you have:

• Created an Azure Machine Learning workspace.
• Set up your local development environment.
• Created an Azure Machine Learning compute cluster.

In the other parts of this tutorial you will learn:

• Part 2. Run code in the cloud by using the Azure Machine Learning SDK for Python.
• Part 3. Manage the Python environment that you use for model training.
• Part 4. Upload data to Azure and consume that data in training.

Continue to the next tutorial, to walk through submitting a script to the Azure Machine Learning compute cluster.