Add Terraform option to environment_setup (#268)

* setup basic folder and file structure

* add tf backend file and bash script to create state storage

* basic pipeline for infrastructure with tf - yaml, tf, bash

* naming and deleting unnecessary bash script

* updated documentation

* added to the get_started.md guide

* added terraform plan step
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Stephanie Visser 2020-05-05 20:16:56 +02:00 коммит произвёл GitHub
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6 изменённых файлов: 158 добавлений и 4 удалений

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@ -18,7 +18,8 @@ High level directory structure for this repository:
│ ├── util <- Python script for various utility operations specific to this ML project.
├── docs <- Extensive markdown documentation for entire project.
├── environment_setup <- The top-level folder for everything related to infrastructure.
│ ├── arm-templates <- Azure Resource Manager(ARM) templates to build infrastructure needed for this project.
│ ├── arm-templates <- Azure Resource Manager(ARM) templates to build infrastructure needed for this project.
│ ├── tf-templates <- Terraform templates to build infrastructure needed for this project.
├── experimentation <- Jupyter notebooks with ML experimentation code.
├── ml_service <- The top-level folder for all Azure Machine Learning resources.
│ ├── pipelines <- Python script that builds Azure Machine Learning pipelines.
@ -35,7 +36,11 @@ The repository provides a template with folders structure suitable for maintaini
- `environment_setup/install_requirements.sh` : This script prepares a local conda environment i.e. install the Azure ML SDK and the packages specified in environment definitions.
- `environment_setup/iac-*.yml, arm-templates` : Infrastructure as Code piplines to create and delete required resources along with corresponding arm-templates.
- `environment_setup/iac-*-arm.yml, arm-templates` : Infrastructure as Code piplines to create required resources using ARM, along with corresponding arm-templates. Infrastructure as Code can be deployed with this template or with the Terraform template.
- `environment_setup/iac-*-tf.yml, tf-templates` : Infrastructure as Code piplines to create required resources using Terraform, along with corresponding tf-templates. Infrastructure as Code can be deployed with this template or with the ARM template.
- `environment_setup/iac-remove-environment.yml` : Infrastructure as Code piplines to delete the created required resources.
- `environment_setup/Dockerfile` : Dockerfile of a build agent containing Python 3.6 and all required packages.

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@ -81,7 +81,7 @@ More variables are available for further tweaking, but the above variables are a
## Provisioning resources using Azure Pipelines
The easiest way to create all required Azure resources (Resource Group, Azure ML Workspace, Container Registry, and others) is to use the **Infrastructure as Code (IaC)** [pipeline in this repository](../environment_setup/iac-create-environment-pipeline.yml). The pipeline takes care of setting up all required resources based on these [Azure Resource Manager templates](../environment_setup/arm-templates/cloud-environment.json).
The easiest way to create all required Azure resources (Resource Group, Azure ML Workspace, Container Registry, and others) is to use the **Infrastructure as Code (IaC)** [pipeline with ARM templates](../environment_setup/iac-create-environment-pipeline-arm.yml) or the [pipeline with Terraform templates](../environment_setup/iac-create-environment-pipeline-tf.yml). The pipeline takes care of setting up all required resources based on these [Azure Resource Manager templates](../environment_setup/arm-templates/cloud-environment.json), or based on these [Terraform templates](../environment_setup/tf-templates).
### Create an Azure DevOps Service Connection for the Azure Resource Manager
@ -100,10 +100,12 @@ In your Azure DevOps project, create a build pipeline from your forked repositor
![Build connect step](./images/build-connect.png)
Select the **Existing Azure Pipelines YAML file** option and set the path to [/environment_setup/iac-create-environment-pipeline.yml](../environment_setup/iac-create-environment-pipeline.yml):
Select the **Existing Azure Pipelines YAML file** option and set the path to [/environment_setup/iac-create-environment-pipeline-arm.yml](../environment_setup/iac-create-environment-pipeline-arm.yml) or to [/environment_setup/iac-create-environment-pipeline-tf.yml](../environment_setup/iac-create-environment-pipeline-tf.yml), depending on if you want to deploy your infrastructure using ARM templates or Terraform:
![Configure step](./images/select-iac-pipeline.png)
If you decide to use Terraform, make sure the ['Terraform Build & Release Tasks' from Charles Zipp](https://marketplace.visualstudio.com/items?itemName=charleszipp.azure-pipelines-tasks-terraform) is installed.
Having done that, run the pipeline:
![IaC run](./images/run-iac-pipeline.png)

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@ -0,0 +1,72 @@
# CI/PR Pipeline that deploys an TF template to create or update the resources needed by the other pipelines.
trigger:
branches:
include:
- master
paths:
include:
- environment_setup/tf-templates/*
pr:
branches:
include:
- master
paths:
include:
- environment_setup/tf-templates/*
pool:
vmImage: 'ubuntu-latest'
variables:
- group: devopsforai-aml-vg
steps:
- task: charleszipp.azure-pipelines-tasks-terraform.azure-pipelines-tasks-terraform-installer.TerraformInstaller@0
displayName: 'Use Terraform 0.12.24'
inputs:
terraformVersion: 0.12.24
- task: charleszipp.azure-pipelines-tasks-terraform.azure-pipelines-tasks-terraform-cli.TerraformCLI@0
displayName: 'TF init - Deploy MLOps resources to Azure'
inputs:
command: init
commandOptions: '-backend=true -backend-config=$(Build.SourcesDirectory)/environment_setup/tf-templates/backend.tf'
workingDirectory: '$(Build.SourcesDirectory)/environment_setup/tf-templates'
backendType: azurerm
backendServiceArm: $(AZURE_RM_SVC_CONNECTION)
ensureBackend: true
backendAzureRmResourceGroupLocation: $(LOCATION)
backendAzureRmResourceGroupName: $(RESOURCE_GROUP)
backendAzureRmStorageAccountName: 'statestor'
backendAzureRmStorageAccountSku: 'Standard_LRS'
backendAzureRmContainerName: 'tfstate-cont'
backendAzureRmKey: 'mlopsinfra.tfstate'
- task: charleszipp.azure-pipelines-tasks-terraform.azure-pipelines-tasks-terraform-cli.TerraformCLI@0
displayName: 'TF validate - Deploy MLOps resources to Azure'
inputs:
command: validate
workingDirectory: '$(Build.SourcesDirectory)/environment_setup/tf-templates'
- task: charleszipp.azure-pipelines-tasks-terraform.azure-pipelines-tasks-terraform-cli.TerraformCLI@0
displayName: 'TF plan - Deploy MLOps resources to Azure'
inputs:
command: plan
workingDirectory: '$(Build.SourcesDirectory)/environment_setup/tf-templates'
environmentServiceName: $(AZURE_RM_SVC_CONNECTION)
env:
TF_VAR_BASE_NAME: $(BASE_NAME)
TF_VAR_RESOURCE_GROUP: $(RESOURCE_GROUP)
TF_VAR_WORKSPACE_NAME: $(WORKSPACE_NAME)
- task: charleszipp.azure-pipelines-tasks-terraform.azure-pipelines-tasks-terraform-cli.TerraformCLI@0
displayName: 'TF apply - Deploy MLOps resources to Azure'
inputs:
command: apply
workingDirectory: '$(Build.SourcesDirectory)/environment_setup/tf-templates'
environmentServiceName: $(AZURE_RM_SVC_CONNECTION)
env:
TF_VAR_BASE_NAME: $(BASE_NAME)
TF_VAR_RESOURCE_GROUP: $(RESOURCE_GROUP)
TF_VAR_WORKSPACE_NAME: $(WORKSPACE_NAME)

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@ -0,0 +1,4 @@
terraform {
backend "azurerm" {
}
}

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@ -0,0 +1,71 @@
provider "azurerm" {
version = "=2.3.0"
features {}
}
variable BASE_NAME {}
variable RESOURCE_GROUP {}
variable WORKSPACE_NAME {}
#--------------------------------------------------------------------------------
#Set the already-existing resource group
data "azurerm_resource_group" "amlrg" {
name = var.RESOURCE_GROUP
}
#Set client config for a.o. tenant id
data "azurerm_client_config" "currentconfig" {
}
#--------------------------------------------------------------------------------
# Storage account for AML Service
resource "azurerm_storage_account" "amlstor" {
name = "${var.BASE_NAME}amlsa"
location = data.azurerm_resource_group.amlrg.location
resource_group_name = data.azurerm_resource_group.amlrg.name
account_tier = "Standard"
account_replication_type = "LRS"
}
# Keyvault for AML Service
resource "azurerm_key_vault" "amlkv" {
name = "${var.BASE_NAME}-AML-KV"
location = data.azurerm_resource_group.amlrg.location
resource_group_name = data.azurerm_resource_group.amlrg.name
tenant_id = data.azurerm_client_config.currentconfig.tenant_id
sku_name = "standard"
}
# App Insights for AML Service
resource "azurerm_application_insights" "amlai" {
name = "${var.BASE_NAME}-AML-AI"
location = data.azurerm_resource_group.amlrg.location
resource_group_name = data.azurerm_resource_group.amlrg.name
application_type = "web"
}
# Container registry for AML Service
resource "azurerm_container_registry" "amlacr" {
name = "${var.BASE_NAME}amlcr"
resource_group_name = data.azurerm_resource_group.amlrg.name
location = data.azurerm_resource_group.amlrg.location
sku = "Standard"
admin_enabled = true
}
# ML Workspace for AML Service, depending on the storage account, Keyvault, App Insights and ACR.
resource "azurerm_machine_learning_workspace" "amlws" {
name = var.WORKSPACE_NAME
location = data.azurerm_resource_group.amlrg.location
resource_group_name = data.azurerm_resource_group.amlrg.name
application_insights_id = azurerm_application_insights.amlai.id
key_vault_id = azurerm_key_vault.amlkv.id
storage_account_id = azurerm_storage_account.amlstor.id
container_registry_id = azurerm_container_registry.amlacr.id
identity {
type = "SystemAssigned"
}
}