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Licensed under the MIT License.
Setting up your Azure Machine Learning services workspace and configuring your notebook library
This notebook configures your library of notebooks to connect to an Azure Machine Learning (ML) workspace. In this case, a library contains all of the notebooks in the current folder and any nested folders. You can configure this notebook library to use an existing workspace or create a new workspace.
Typically you will need to run this notebook only once per notebook library as all other notebooks will use connection information that is written here. If you want to redirect your notebook library to work with a different workspace, then you should re-run this notebook.
In this notebook you will
An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows. In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inference, and the monitoring of deployed models.
This section describes activities required before you can access any Azure ML services functionality.
In order to create an Azure ML Workspace, first you need access to an Azure subscription. An Azure subscription allows you to manage storage, compute, and other assets in the Azure cloud. You can create a new subscription or access existing subscription information from the Azure portal. Later in this notebook you will need information such as your subscription ID in order to create and access AML workspaces.
If you are running in your own environment, follow SDK installation instructions. If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.
Also install following libraries to your environment. Many of the example notebooks depend on them
(myenv) $ conda install -y matplotlib tqdm scikit-learn
Once installation is complete, the following cell checks the Azure ML SDK version:
import azureml.core
print("This notebook was created using version 1.18.0 of the Azure ML SDK")
print("You are currently using version", azureml.core.VERSION, "of the Azure ML SDK")If you are using an older version of the SDK then this notebook was created using, you should upgrade your SDK.
Azure Machine Learning uses of Azure Container Instance (ACI) to deploy dev/test web services. An Azure subscription needs to be registered to use ACI. If you or the subscription owner have not yet registered ACI on your subscription, you will need to use the Azure CLI and execute the following commands. Note that if you ran through the AML quickstart you have already registered ACI.
# check to see if ACI is already registered
(myenv) $ az provider show -n Microsoft.ContainerInstance -o table
# if ACI is not registered, run this command.
# note you need to be the subscription owner in order to execute this command successfully.
(myenv) $ az provider register -n Microsoft.ContainerInstance
To use an AML Workspace, you will need to import the Azure ML SDK and supply the following information:
You can get your subscription ID from the Azure portal.
You will also need access to a resource group, which organizes Azure resources and provides a default region for the resources in a group. You can see what resource groups to which you have access, or create a new one in the Azure portal. If you don't have a resource group, the create workspace command will create one for you using the name you provide.
The region to host your workspace will be used if you are creating a new workspace. You do not need to specify this if you are using an existing workspace. You can find the list of supported regions here. You should pick a region that is close to your location or that contains your data.
The name for your workspace is unique within the subscription and should be descriptive enough to discern among other AML Workspaces. The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.
The following cell allows you to specify your workspace parameters. This cell uses the python method os.getenv to read values from environment variables which is useful for automation. If no environment variable exists, the parameters will be set to the specified default values.
If you ran the Azure Machine Learning quickstart in Azure Notebooks, you already have a configured workspace! You can go to your Azure Machine Learning Getting Started library, view config.json file, and copy-paste the values for subscription ID, resource group and workspace name below.
Replace the default values in the cell below with your workspace parameters
import os
subscription_id = os.getenv("SUBSCRIPTION_ID", default="<my-subscription-id>")
resource_group = os.getenv("RESOURCE_GROUP", default="<my-resource-group>")
workspace_name = os.getenv("WORKSPACE_NAME", default="<my-workspace-name>")
workspace_region = os.getenv("WORKSPACE_REGION", default="eastus2")The following cell uses the Azure ML SDK to attempt to load the workspace specified by your parameters. If this cell succeeds, your notebook library will be configured to access the workspace from all notebooks using the Workspace.from_config() method. The cell can fail if the specified workspace doesn't exist or you don't have permissions to access it.
from azureml.core import Workspace
try:
ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)
# write the details of the workspace to a configuration file to the notebook library
ws.write_config()
print("Workspace configuration succeeded. Skip the workspace creation steps below")
except:
print("Workspace not accessible. Change your parameters or create a new workspace below")If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace. If you don't have a resource group, the create workspace command will create one for you using the name you provide.
Note: As with other Azure services, there are limits on certain resources (for example AmlCompute quota) associated with the Azure ML service. Please read this article on the default limits and how to request more quota.
This cell will create an Azure ML workspace for you in a subscription provided you have the correct permissions.
This will fail if:
If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources.
Note: A Basic workspace is created by default. If you would like to create an Enterprise workspace, please specify sku = 'enterprise'. Please visit our pricing page for more details on our Enterprise edition.
from azureml.core import Workspace
# Create the workspace using the specified parameters
ws = Workspace.create(name = workspace_name,
subscription_id = subscription_id,
resource_group = resource_group,
location = workspace_region,
create_resource_group = True,
sku = 'basic',
exist_ok = True)
ws.get_details()
# write the details of the workspace to a configuration file to the notebook library
ws.write_config()Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.
To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors. Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.
The cluster parameters are:
az vm list-skus -o tsv
To create a CPU cluster now, run the cell below. The autoscale settings mean that the cluster will scale down to 0 nodes when inactive and up to 4 nodes when busy.
from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException
# Choose a name for your CPU cluster
cpu_cluster_name = "cpu-cluster"
# Verify that cluster does not exist already
try:
cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)
print("Found existing cpu-cluster")
except ComputeTargetException:
print("Creating new cpu-cluster")
# Specify the configuration for the new cluster
compute_config = AmlCompute.provisioning_configuration(vm_size="STANDARD_D2_V2",
min_nodes=0,
max_nodes=4)
# Create the cluster with the specified name and configuration
cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)
# Wait for the cluster to complete, show the output log
cpu_cluster.wait_for_completion(show_output=True)To create a GPU cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see these instructions.
from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException
# Choose a name for your GPU cluster
gpu_cluster_name = "gpu-cluster"
# Verify that cluster does not exist already
try:
gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)
print("Found existing gpu cluster")
except ComputeTargetException:
print("Creating new gpu-cluster")
# Specify the configuration for the new cluster
compute_config = AmlCompute.provisioning_configuration(vm_size="STANDARD_NC6",
min_nodes=0,
max_nodes=4)
# Create the cluster with the specified name and configuration
gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)
# Wait for the cluster to complete, show the output log
gpu_cluster.wait_for_completion(show_output=True)In this notebook you configured this notebook library to connect easily to an Azure ML workspace. You can copy this notebook to your own libraries to connect them to you workspace, or use it to bootstrap new workspaces completely.
If you came here from another notebook, you can return there and complete that exercise, or you can try out the Tutorials or jump into "how-to" notebooks and start creating and deploying models. A good place to start is the train within notebook example that walks through a simplified but complete end to end machine learning process.
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