Create an Azure Machine Learning compute cluster

• If you're not running your code on a compute instance, install the Azure Machine Learning Python SDK. This SDK is already installed for you on a compute instance.

• Attach to the workspace in your Python script:

  Run this code to connect to your Azure Machine Learning workspace.

  Replace your Subscription ID, Resource Group name, and Workspace name in the following code. To find these values:

  1. Sign in to Azure Machine Learning studio.
  2. Open the workspace you wish to use.
  3. Select your workspace name in the upper right Azure Machine Learning studio toolbar.
  4. Copy the value for workspace, resource group, and subscription ID into the code.

  APPLIES TO: Python SDK azure-ai-ml v2 (current)

  # Enter details of your AML workspace
  subscription_id = "<SUBSCRIPTION_ID>"
  resource_group = "<RESOURCE_GROUP>"
  workspace = "<AML_WORKSPACE_NAME>"
  

  # get a handle to the workspace
  from azure.ai.ml import MLClient
  from azure.identity import DefaultAzureCredential
  
  ml_client = MLClient(
      DefaultAzureCredential(), subscription_id, resource_group, workspace
  )
  

  ml_client is a handler to the workspace that you use to manage other resources and jobs.

• If you're not running these commands on a compute instance, install the Azure CLI extension for Machine Learning service (v2). This extension is already installed for you on a compute instance.

• Authenticate and set the default workspace and resource group. Leave the terminal open to run the rest of the commands in this article.

  • If you're on a compute instance:

    az login --identity
    # next line needed only if you have multiple subscriptions:
    az account set --subscription "<SUBSCRIPTION-NAME>" # replace with your subscription name
    az configure --defaults group=$CI_RESOURCE_GROUP workspace=$CI_WORKSPACE
    

  • If you're running the commands locally, omit --identity and follow instructions for authentication. Also replace $CI_RESOURCE_GROUP and $CI_WORKSPACE with your values.

Start at Azure Machine Learning studio.

To create a persistent Azure Machine Learning Compute resource in Python, specify the size and max_instances properties. Azure Machine Learning then uses smart defaults for the other properties.

• size: The VM family of the nodes created by Azure Machine Learning Compute.
• max_instances: The maximum number of nodes to autoscale up to when you run a job on Azure Machine Learning Compute.

APPLIES TO: Python SDK azure-ai-ml v2 (current)

from azure.ai.ml.entities import AmlCompute

cluster_basic = AmlCompute(
    name="basic-example",
    type="amlcompute",
    size="STANDARD_DS3_v2",
    location="chinaeast",
    min_instances=0,
    max_instances=2,
    idle_time_before_scale_down=120,
)
ml_client.begin_create_or_update(cluster_basic)

You can also configure several advanced properties when you create Azure Machine Learning Compute. The properties allow you to create a persistent cluster of fixed size, or within an existing Azure Virtual Network in your subscription. See the AmlCompute class for details.

APPLIES TO: Azure CLI ml extension v2 (current)

az ml compute create -f create-cluster.yml

Where the file create-cluster.yml is:

$schema: https://azuremlschemas.azureedge.net/latest/amlCompute.schema.json 
name: location-example
type: amlcompute
size: STANDARD_DS3_v2
min_instances: 0
max_instances: 2
idle_time_before_scale_down: 120
location: chinaeast2

Create a single- or multi- node compute cluster for your training, batch inference or reinforcement learning workloads.

1. Navigate to Azure Machine Learning studio.

2. Under Manage, select Compute.

3. If you have no compute resources, select New in the middle of the page.

   

4. If you see a list of compute resources, select +New above the list.

   

5. In the tabs at the top, select Compute cluster.

6. Fill out the form as follows:

   Field	Description
   Location	The Azure region where the compute cluster is created. By default, this is the same location as the workspace. If you don't have sufficient quota in the default region, switch to a different region for more options. When using a different region than your workspace or datastores, you might see increased network latency and data transfer costs. The latency and costs can occur when creating the cluster, and when running jobs on it.
   Virtual machine type	Choose CPU or GPU. This type can't be changed after creation.
   Virtual machine priority	Choose Dedicated or Low priority. Low priority virtual machines are cheaper but don't guarantee the compute nodes. Your job might be preempted.
   Virtual machine size	Supported virtual machine sizes might be restricted in your region. Check the availability list

7. Select Next to proceed to Advanced Settings and fill out the form as follows:

   Field	Description
   Compute name	* Name is required and must be between 3 to 24 characters long. * Valid characters are upper and lower case letters, digits, and the - character. * Name must start with a letter. * Name needs to be unique across all existing computes within an Azure region. You see an alert if the name you choose isn't unique. * If - character is used, then it needs to be followed by at least one letter later in the name.
   Minimum number of nodes	Minimum number of nodes that you want to provision. If you want a dedicated number of nodes, set that count here. Save money by setting the minimum to 0, so you don't pay for any nodes when the cluster is idle.
   Maximum number of nodes	Maximum number of nodes that you want to provision. The compute automatically scales to a maximum of this node count when a job is submitted.
   Idle seconds before scale down	Idle time before scaling the cluster down to the minimum node count.
   Enable SSH access	Use the same instructions as Enable SSH access for a compute instance.
   Advanced settings	Optional. Configure network settings. * If an Azure Virtual Network, Specify the Resource group, Virtual network, and Subnet to create the compute instance inside the network. For more information, see network requirements. * If an Azure Machine Learning managed network, the compute cluster is automatically in the managed network. For more information, see managed computes with a managed network. * No public IP configures whether the compute cluster has a public IP address when in a network. * Assign a managed identity to grant access to resources.

8. Select Create.

Enable SSH access

SSH access is disabled by default. SSH access can't be changed after creation. Make sure to enable access if you plan to debug interactively with VS Code Remote.

After you have selected Next: Advanced Settings:

1. Turn on Enable SSH access.
2. In the SSH public key source, select one of the options from the dropdown:
   • If you Generate new key pair:
     1. Enter a name for the key in Key pair name.
     2. Select Create.
     3. Select Download private key and create compute. The key is usually downloaded into the Downloads folder.
   • If you select Use existing public key stored in Azure, search for and select the key in Stored key.
   • If you select Use existing public key, provide an RSA public key in the single-line format (starting with "ssh-rsa") or the multi-line PEM format. You can generate SSH keys using ssh-keygen on Linux and OS X, or PuTTYGen on Windows.

Connect with SSH access

After you create a compute with SSH access enabled, use these steps for access.

1. Find the compute in your workspace resources:

   1. On the left, select Compute.
   2. Use the tabs at the top to select Compute instance or Compute cluster to find your machine.

2. Select the compute name in the list of resources.

3. Find the connection string:

   • For a compute instance, select Connect at the top of the Details section.

     

   • For a compute cluster, select Nodes at the top, then select the Connection string in the table for your node.

4. Copy the connection string.

5. For Windows, open PowerShell or a command prompt:

   1. Go into the directory or folder where your key is stored

   2. Add the -i flag to the connection string to locate the private key and point to where it is stored:

      ssh -i <keyname.pem> azureuser@... (rest of connection string)

6. For Linux users, follow the steps from Create and use an SSH key pair for Linux VMs in Azure

7. For SCP use:

   scp -i key.pem -P {port} {fileToCopyFromLocal } azureuser@yourComputeInstancePublicIP:~/{destination}

APPLIES TO: Python SDK azure-ai-ml v2 (current)

from azure.ai.ml.entities import AmlCompute

cluster_low_pri = AmlCompute(
    name="low-pri-example",
    size="STANDARD_DS3_v2",
    min_instances=0,
    max_instances=2,
    idle_time_before_scale_down=120,
    tier="low_priority",
)
ml_client.begin_create_or_update(cluster_low_pri)

APPLIES TO: Azure CLI ml extension v2 (current)

Set the vm-priority:

az ml compute create -f create-cluster.yml

Where the file create-cluster.yml is:

$schema: https://azuremlschemas.azureedge.net/latest/amlCompute.schema.json 
name: low-pri-example
type: amlcompute
size: STANDARD_DS3_v2
min_instances: 0
max_instances: 2
idle_time_before_scale_down: 120
tier: low_priority

In the studio, choose Low Priority when you create a VM.

APPLIES TO: Python SDK azure-ai-ml v2 (current)

This deletes the basic compute cluster, created from the create_basic object earlier in this article.

from azure.ai.ml.entities import AmlCompute

ml_client.compute.begin_delete(cluster_basic.name).wait()

APPLIES TO: Azure CLI ml extension v2 (current)

This deletes a compute cluster named basic-example.

az ml compute delete --name basic-example 

1. Navigate to Azure Machine Learning studio.
2. In the left menu, under Manage, select Compute.
3. At the top of the Compute page, select Compute cluster.
4. Select the cluster you want to delete.
5. At the top of the page, select Delete.