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Merge pull request microsoft#73 from denniseik/master
Including Azure ML compute with existing examples. Adding network-isolated examples.
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‎.gitignore‎

Whitespace-only changes.

‎quickstart/101-machine-learning/compute.tf‎

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# Generate random string for unique compute instance name
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resource "random_string" "ci_prefix" {
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length = 8
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upper = false
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special = false
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number = false
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}
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# Compute instance
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resource "azurerm_machine_learning_compute_instance" "compute_instance" {
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name = "${random_string.ci_prefix.result}instance"
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location = azurerm_resource_group.default.location
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machine_learning_workspace_id = azurerm_machine_learning_workspace.default.id
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virtual_machine_size = "STANDARD_DS2_V2"
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}
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# Compute Cluster
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resource "azurerm_machine_learning_compute_cluster" "compute" {
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name = "cpu-cluster"
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location = azurerm_resource_group.default.location
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machine_learning_workspace_id = azurerm_machine_learning_workspace.default.id
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vm_priority = "Dedicated"
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vm_size = "STANDARD_DS2_V2"
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identity {
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type = "SystemAssigned"
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}
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scale_settings {
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min_node_count = 0
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max_node_count = 3
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scale_down_nodes_after_idle_duration = "PT15M" # 15 minutes
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}
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}

‎quickstart/101-machine-learning/main.tf‎

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@@ -4,7 +4,7 @@ terraform {
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required_providers {
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azurerm = {
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source = "hashicorp/azurerm"
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version = "=2.56.0"
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version = "=2.76.0"
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}
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}
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}

‎quickstart/101-machine-learning/readme.md‎

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# Azure Machine Learning workspace
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# Azure Machine Learning workspace (public network connectivity)
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This deployment configuration specifies an [Azure Machine Learning workspace](https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace),
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and its associated resources including Azure Key Vault, Azure Storage, Azure Application Insights and Azure Container Registry.
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This configuration describes the minimal set of resources you require to get started with Azure Machine Learning.
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Network connectivity to the workspace is allowed over public endpoints, making this configuration suitable for open source projects or pilot environments.
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## Resources
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| Terraform Resource Type | Description |
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| - | - |
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| `azurerm_resource_group` | The resource group all resources get deployed into |
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| `azurerm_application_insights` | An Azure Application Insights instance associated to the Azure Machine Learning workspace |
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| `azurerm_key_vault` | An Azure Key Vault instance associated to the Azure Machine Learning workspace |
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| `azurerm_storage_account` | An Azure Storage instance associated to the Azure Machine Learning workspace |
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| `azurerm_container_registry` | An Azure Container Registry instance associated to the Azure Machine Learning workspace |
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| `azurerm_machine_learning_workspace` | An Azure Machine Learning workspace instance |
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| `azurerm_resource_group` | The resource group all resources get deployed into. |
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| `azurerm_application_insights` | An Azure Application Insights instance associated to the Azure Machine Learning workspace. |
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| `azurerm_key_vault` | An Azure Key Vault instance associated to the Azure Machine Learning workspace. |
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| `azurerm_storage_account` | An Azure Storage instance associated to the Azure Machine Learning workspace. |
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| `azurerm_container_registry` | An Azure Container Registry instance associated to the Azure Machine Learning workspace. |
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| `azurerm_machine_learning_workspace` | An Azure Machine Learning workspace instance. |
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| `azurerm_machine_learning_compute_instance` | An Azure Machine Learning compute instance a single-node managed compute. |
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| `azurerm_machine_learning_compute_cluster` | An Azure Machine Learning compute cluster as multi-node shared and managed compute. |
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## Variables
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| Name | Description |
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|-|-|
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| name | Name of the deployment |
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| environment | The deployment environment name (used for pre- and postfixing resource names) |
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| location | The Azure region used for deployments |
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| Name | Description | Default |
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|-|-|-|
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| name | Name of the deployment | - |
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| environment | The deployment environment name (used for pre- and postfixing resource names) | dev |
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| location | The Azure region used for deployments | East US |
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## Usage
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```bash
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terraform init
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terraform plan -var name=azureml567 -out demo.tfplan
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terraform apply "demo.tfplan"
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```
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## Learn more
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- If you are new to Azure Machine Learning, see [Azure Machine Learning service](https://azure.microsoft.com/services/machine-learning-service/) and [Azure Machine Learning documentation](https://docs.microsoft.com/azure/machine-learning/).
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- To learn more about security configurations in Azure Machine Learning, see [Enterprise security and governance for Azure Machine Learning](https://docs.microsoft.com/en-us/azure/machine-learning/concept-enterprise-security).
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- For all configurations of Azure Machine Learning in Terraform, see [Terraform Hashicorp AzureRM provider documentation](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/machine_learning_workspace).

‎quickstart/101-machine-learning/workspace.tf‎

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tenant_id = data.azurerm_client_config.current.tenant_id
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sku_name = "premium"
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purge_protection_enabled = false
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network_acls {
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default_action = "Deny"
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bypass = "AzureServices"
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}
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}
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resource "azurerm_storage_account" "default" {
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resource_group_name = azurerm_resource_group.default.name
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account_tier = "Standard"
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account_replication_type = "GRS"
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network_rules {
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default_action = "Deny"
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bypass = ["AzureServices"]
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}
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}
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resource "azurerm_container_registry" "default" {
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name = "cr${var.name}${var.environment}"
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location = azurerm_resource_group.default.location
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resource_group_name = azurerm_resource_group.default.name
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sku = "Premium"
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admin_enabled = true
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name = "cr${var.name}${var.environment}"
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location = azurerm_resource_group.default.location
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resource_group_name = azurerm_resource_group.default.name
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sku = "Premium"
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admin_enabled = true
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}
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# Machine Learning workspace
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identity {
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type = "SystemAssigned"
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}
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}
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}
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‎quickstart/201-machine-learning-moderately-secure/compute.tf‎

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# Generate random string for unique compute instance name
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resource "random_string" "ci_prefix" {
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length = 8
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upper = false
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special = false
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number = false
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}
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# Compute instance
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resource "azurerm_machine_learning_compute_instance" "compute_instance" {
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name = "${random_string.ci_prefix.result}instance"
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location = azurerm_resource_group.default.location
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machine_learning_workspace_id = azurerm_machine_learning_workspace.default.id
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virtual_machine_size = "STANDARD_DS2_V2"
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subnet_resource_id = azurerm_subnet.snet-training.id
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depends_on = [
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azurerm_private_endpoint.mlw_ple
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]
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}
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# Compute cluster
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resource "azurerm_machine_learning_compute_cluster" "compute" {
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name = "cpu-cluster"
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location = azurerm_resource_group.default.location
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machine_learning_workspace_id = azurerm_machine_learning_workspace.default.id
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vm_priority = "Dedicated"
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vm_size = "STANDARD_DS2_V2"
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subnet_resource_id = azurerm_subnet.snet-training.id
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identity {
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type = "SystemAssigned"
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}
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scale_settings {
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min_node_count = 0
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max_node_count = 3
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scale_down_nodes_after_idle_duration = "PT15M" # 15 minutes
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}
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}

‎quickstart/201-machine-learning-private/main.tf‎ renamed to ‎quickstart/201-machine-learning-moderately-secure/main.tf‎

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required_providers {
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azurerm = {
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source = "hashicorp/azurerm"
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version = "=2.72.0"
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version = "=2.78.0"
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}
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}
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}

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