Top / Microsoft Azure / Azure Machine Learning / Compute Instance

Azure Machine Learning Compute Instance

This page shows how to write Terraform and Azure Resource Manager for Machine Learning Compute Instance and write them securely.

terraform-logo

Review your .tf file for Azure best practices

Shisho Cloud, our free checker to make sure your Terraform configuration follows best practices, is available (beta).

azurerm_machine_learning_compute_instance (Terraform)

The Compute Instance in Machine Learning can be configured in Terraform with the resource name azurerm_machine_learning_compute_instance. The following sections describe 1 example of how to use the resource and its parameters.

Example Usage from GitHub

module.tf#L11
resource "azurerm_machine_learning_compute_instance" "mlci" {
  name = azurecaf_name.mlci.result

  location                      = var.location
  machine_learning_workspace_id = var.remote_objects.machine_learning_workspace_id
  virtual_machine_size          = var.settings.virtual_machine_size

Review your Terraform file for Azure best practices

Shisho Cloud, our free checker to make sure your Terraform configuration follows best practices, is available (beta).

Parameters

The following arguments are supported:

  • name - (Required) The name which should be used for this Machine Learning Compute Instance. Changing this forces a new Machine Learning Compute Instance to be created.

  • location - (Required) The Azure Region where the Machine Learning Compute Instance should exist. Changing this forces a new Machine Learning Compute Instance to be created.

  • machine_learning_workspace_id - (Required) The ID of the Machine Learning Workspace. Changing this forces a new Machine Learning Compute Instance to be created.

  • virtual_machine_size - (Required) The Virtual Machine Size. Changing this forces a new Machine Learning Compute Instance to be created.

  • authorization_type - (Optional) The Compute Instance Authorization type. Possible values include: personal. Changing this forces a new Machine Learning Compute Instance to be created.

  • assign_to_user - (Optional) A assign_to_user block as defined below. A user explicitly assigned to a personal compute instance. Changing this forces a new Machine Learning Compute Instance to be created.

  • description - (Optional) The description of the Machine Learning Compute Instance. Changing this forces a new Machine Learning Compute Instance to be created.

  • identity - (Optional) An identity block as defined below. Changing this forces a new Machine Learning Compute Instance to be created.

  • local_auth_enabled - (Optional) Whether local authentication methods is enabled. Defaults to true. Changing this forces a new Machine Learning Compute Instance to be created.

  • ssh - (Optional) A ssh block as defined below. Specifies policy and settings for SSH access. Changing this forces a new Machine Learning Compute Instance to be created.

  • subnet_resource_id - (Optional) Virtual network subnet resource ID the compute nodes belong to. Changing this forces a new Machine Learning Compute Instance to be created.

  • tags - (Optional) A mapping of tags which should be assigned to the Machine Learning Compute Instance. Changing this forces a new Machine Learning Compute Instance to be created.

A identity block supports the following:

  • type - (Required) The Type of Identity which should be used for this Machine Learning Compute Instance. Possible values are SystemAssigned, UserAssigned and SystemAssigned,UserAssigned. Changing this forces a new Machine Learning Compute Instance to be created.

  • identity_ids - (Optional) A list of User Managed Identity ID's which should be assigned to the Machine Learning Compute Instance. Changing this forces a new Machine Learning Compute Instance to be created.

A assign_to_user block supports the following:

  • object_id - (Optional) User’s AAD Object Id.

  • tenant_id - (Optional) User’s AAD Tenant Id.

A ssh block supports the following:

  • public_key - (Required) Specifies the SSH rsa public key file as a string. Use "ssh-keygen -t rsa -b 2048" to generate your SSH key pairs.

In addition to the Arguments listed above - the following Attributes are exported:

  • id - The ID of the Machine Learning Compute Instance.

  • identity - An identity block as defined below, which contains the Managed Service Identity information for this Machine Learning Compute Instance.

  • ssh - An ssh block as defined below, which specifies policy and settings for SSH access for this Machine Learning Compute Instance.

A identity block exports the following:

  • principal_id - The Principal ID for the Service Principal associated with the Managed Service Identity of this Machine Learning Compute Instance.

  • tenant_id - The Tenant ID for the Service Principal associated with the Managed Service Identity of this Machine Learning Compute Instance.

A ssh block exports the following:

  • username - The admin username of this Machine Learning Compute Instance.

  • port - Describes the port for connecting through SSH.

>> from Terraform Registry

Explanation in Terraform Registry

Manages a Machine Learning Compute Instance.

>> from Terraform Registry

Microsoft.MachineLearningServices/workspaces/computes (Azure Resource Manager)

The workspaces/computes in Microsoft.MachineLearningServices can be configured in Azure Resource Manager with the resource name Microsoft.MachineLearningServices/workspaces/computes. The following sections describe how to use the resource and its parameters.

Example Usage from GitHub

An example could not be found in GitHub.

Parameters

  • apiVersion required - string
  • identity optional
      • type optional - string

        The identity type.

      • userAssignedIdentities optional - undefined

        dictionary containing all the user assigned identities, with resourceId of the UAI as key.

  • location optional - string

    Specifies the location of the resource.

  • name required - string

    Name of the Azure Machine Learning compute.

  • properties required
      • computeLocation optional - string

        Location for the underlying compute

      • description optional - string

        The description of the Machine Learning compute.

      • disableLocalAuth optional - boolean

        Opt-out of local authentication and ensure customers can use only MSI and AAD exclusively for authentication.

      • resourceId optional - string

        ARM resource id of the underlying compute

  • sku optional
      • name optional - string

        Name of the sku

      • tier optional - string

        Tier of the sku like Basic or Enterprise

  • systemData optional
      • createdAt optional - string

        The timestamp of resource creation (UTC).

      • createdBy optional - string

        The identity that created the resource.

      • createdByType optional - string

        The type of identity that created the resource.

      • lastModifiedAt optional - string

        The timestamp of resource last modification (UTC)

      • lastModifiedBy optional - string

        The identity that last modified the resource.

      • lastModifiedByType optional - string

        The type of identity that last modified the resource.

  • tags optional - string

    Contains resource tags defined as key/value pairs.

  • type required - string

>> from Azure Resource Manager Documentation

The Other Related Azure Machine Learning Resources

Frequently asked questions

What is Azure Machine Learning Compute Instance?

Azure Machine Learning Compute Instance is a resource for Machine Learning of Microsoft Azure. Settings can be wrote in Terraform.

Where can I find the example code for the Azure Machine Learning Compute Instance?

For Terraform, the aztfmod/terraform-azurerm-caf source code example is useful. See the Terraform Example section for further details.

security-icon

Automate config file reviews on your commits

Fix issues in your infrastructure as code with auto-generated patches.

Table of Contents