Manage an Azure Machine Learning compute instance

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

Learn how to manage a compute instance in your Azure Machine Learning workspace.

Use a compute instance as your fully configured and managed development environment in the cloud. For development and testing, you can also use the instance as a training compute target. A compute instance can run multiple jobs in parallel and has a job queue. As a development environment, a compute instance can't be shared with other users in your workspace.

In this article, you learn how to start, stop, restart, delete a compute instance. To learn how to create a compute instance, see Create an Azure Machine Learning compute instance.

Note

This article shows CLI v2 in the sections below. If you are still using CLI v1, see Create an Azure Machine Learning compute cluster CLI v1.

Prerequisites

• An Azure Machine Learning workspace. For more information, see Manage Azure Machine Learning workspaces.

Select the appropriate tab for the rest of the prerequisites based on your preferred method of managing your compute instance.

Python SDK

• 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.

Azure CLI

• 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.

Studio

Start at Azure Machine Learning studio.

Manage

Start, stop, restart, and delete a compute instance. A compute instance doesn't always automatically scale down, so make sure to stop the resource to prevent ongoing charges. Stopping a compute instance deallocates it. Then start it again when you need it. While stopping the compute instance stops the billing for compute hours, you'll still be billed for disk, public IP, and standard load balancer.

You can enable automatic shutdown to automatically stop the compute instance after a specified time.

You can also create a schedule for the compute instance to automatically start and stop based on a time and day of week.

Tip

The compute instance has 120GB OS disk. If you run out of disk space, use the terminal to clear at least 5 GB before you stop or restart the compute instance. Please do not stop the compute instance by issuing sudo shutdown from the terminal. The temp disk size on compute instance depends on the VM size chosen and is mounted on /mnt.

Python SDK

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

In these examples, the name of the compute instance is stored in the variable ci_basic_name.

• Get status

  from azure.ai.ml.entities import ComputeInstance, AmlCompute
  
  # Get compute
  ci_basic_state = ml_client.compute.get(ci_basic_name)

• Stop

  from azure.ai.ml.entities import ComputeInstance, AmlCompute
  
  # Stop compute
  ml_client.compute.begin_stop(ci_basic_name).wait()

• Start

  from azure.ai.ml.entities import ComputeInstance, AmlCompute
  
  # Start compute
  ml_client.compute.begin_start(ci_basic_name).wait()

• Restart

  from azure.ai.ml.entities import ComputeInstance, AmlCompute
  
  # Restart compute
  ml_client.compute.begin_restart(ci_basic_name).wait()

• Delete

  from azure.ai.ml.entities import ComputeInstance, AmlCompute
  
  ml_client.compute.begin_delete(ci_basic_name).wait()

Azure CLI

APPLIES TO: Azure CLI ml extension v2 (current)

In these examples, the name of the compute instance is instance.

• Stop

  az ml compute stop --name instance 
  

• Start

  az ml compute start --name instance 
  

• Restart

  az ml compute restart --name instance 
  

• Delete

  az ml compute delete --name instance 
  

Studio

In your workspace in Azure Machine Learning studio, select Compute, then select compute instance on the top.

You can perform the following actions:

• Create a new compute instance
• Refresh the compute instances tab.
• Start, stop, and restart a compute instance. You do pay for the instance whenever it's running. Stop the compute instance when you aren't using it to reduce cost. Stopping a compute instance deallocates it. Then start it again when you need it. You can also schedule a time for the compute instance to start and stop.
• Delete a compute instance.
• Filter the list of compute instances to show only ones you created.

For each compute instance in a workspace that you created (or that was created for you), you can:

• Access Jupyter, JupyterLab, RStudio on the compute instance.

• SSH into compute instance. SSH access is disabled by default but can be enabled at compute instance creation time. SSH access is through public/private key mechanism. The tab gives you details for SSH connection such as IP address, username, and port number. In a virtual network deployment, disabling SSH prevents SSH access from public internet. You can still SSH from within virtual network using private IP address of compute instance node and port 22.

  Tip

  If the compute instances is in a managed virtual network and the public IP address is disabled, use the az ml compute connect-ssh command to connect to the compute instance.

• Select the compute name to:

  • View details about a specific compute instance such as IP address, and region.
  • Create or modify the schedule for starting and stopping the compute instance. Scroll down to the bottom of the page to edit the schedule.

Azure RBAC allows you to control which users in the workspace can create, delete, start, stop, restart a compute instance. All users in the workspace contributor and owner role can create, delete, start, stop, and restart compute instances across the workspace. However, only the creator of a specific compute instance, or the user assigned if it was created on their behalf, is allowed to access Jupyter, JupyterLab, and RStudio on that compute instance. A compute instance is dedicated to a single user who has root access. That user has access to Jupyter/JupyterLab/RStudio running on the instance. Compute instance has single-user sign-in and all actions use that user's identity for Azure RBAC and attribution of experiment jobs. SSH access is controlled through public/private key mechanism.

These actions can be controlled by Azure RBAC:

• Microsoft.MachineLearningServices/workspaces/computes/read
• Microsoft.MachineLearningServices/workspaces/computes/write
• Microsoft.MachineLearningServices/workspaces/computes/delete
• Microsoft.MachineLearningServices/workspaces/computes/start/action
• Microsoft.MachineLearningServices/workspaces/computes/stop/action
• Microsoft.MachineLearningServices/workspaces/computes/restart/action
• Microsoft.MachineLearningServices/workspaces/computes/updateSchedules/action

To create a compute instance, you need permissions for the following actions:

• Microsoft.MachineLearningServices/workspaces/computes/write
• Microsoft.MachineLearningServices/workspaces/checkComputeNameAvailability/action

Audit and observe compute instance version

Once a compute instance is deployed, it doesn't get automatically updated. Microsoft releases new VM images on a monthly basis. To understand options for keeping recent with the latest version, see vulnerability management.

To keep track of whether an instance's operating system version is current, you could query its version using the CLI, SDK, or Studio UI.

Python SDK

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

from azure.ai.ml.entities import ComputeInstance, AmlCompute

# Display operating system version
instance = ml_client.compute.get("myci")
print instance.os_image_metadata

For more information on the classes, methods, and parameters used in this example, see the following reference documents:

• AmlCompute class
• ComputeInstance class

Azure CLI

APPLIES TO: Azure CLI ml extension v2 (current)

az ml compute show --name "myci"

# query outdated compute instances:
az ml compute list --query "[?os_image_metadata.is_latest_os_image_version == ``false``].name"

Studio

In your workspace in Azure Machine Learning studio, select Compute, then select compute instance on the top. To see its properties including the current operating system, select a compute instance's compute name.

IT administrators can use Azure Policy to monitor the inventory of instances across workspaces in Azure Policy compliance portal. Assign the built-in policy Audit Azure Machine Learning Compute Instances with an outdated operating system on an Azure subscription or Azure management group scope.

Next steps

• Access the compute instance terminal
• Create and manage files
• Update the compute instance to the latest VM image