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Test-AzTemplate: Skipping nested templates for prereqs (#690)
* Test-AzTemplate: Skipping nested templates for prereq,parameters, and CreateUIDefinition (because they cannot have them) (Fixes #686, makes #680 more quiet) * Adding Test Directory for JSONFiles-Should-Be-Valid (re #686) * Delete prereq.azuredeploy.parameters.json * Delete azuredeploy.parameters.json * Delete .settings.json Co-authored-by: James Brundage <@github.com> Co-authored-by: Brian Moore <bmoore@microsoft.com>
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‎arm-ttk/Test-AzTemplate.ps1‎

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@@ -424,11 +424,13 @@ Each test script has access to a set of well-known variables:
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}
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}
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if ($testOut.InnerTemplateLocation) {
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if ($testOut.InnerTemplateLocation -and $location) {
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$location.Line += $testOut.InnerTemplateLocation.Line - 1
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}
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$testOut | Add-Member NoteProperty Location $location -Force
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if ($location) {
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$testOut | Add-Member NoteProperty Location $location -Force
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}
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}
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}
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elseif ($testOut -is [Management.Automation.WarningRecord]) {
@@ -565,12 +567,20 @@ Each test script has access to a set of well-known variables:
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$templateFileName = $fileInfo.Name
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$TemplateObject = $fileInfo.Object
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$TemplateText = $fileInfo.Text
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# If the file had inner templates
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if ($fileInfo.InnerTemplates) {
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# use the inner templates from just this file
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$InnerTemplates = $fileInfo.InnerTemplates
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$InnerTemplatesText = $fileInfo.InnerTemplatesText
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$InnerTemplatesNames = $fileInfo.InnerTemplatesNames
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$innerTemplatesLocations = $fileInfo.InnerTemplatesLocations
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} else {
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}
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elseif ($fileInfo.Name -match '^(?>parameters|prereq|CreateUIDefinition)\.') {
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$InnerTemplates, $InnerTemplateText, $InnerTemplatesNames, $innerTemplatesLocations = $null
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}
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else
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{
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# Otherwise, use the inner templates from the main file
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$InnerTemplates = $mainInnerTemplates
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$InnerTemplatesText = $mainInnerTemplatesText
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$InnerTemplatesNames = $MainInnerTemplatesNames

‎unit-tests/JSONFiles-Should-Be-Valid/JSONFiles-Should-Be-Valid.tests.ps1‎

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#requires -module arm-ttk
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. $PSScriptRoot\..\arm-ttk.test.functions.ps1
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Test-TTK $psScriptRoot
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return
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‎unit-tests/JSONFiles-Should-Be-Valid/Pass/azuredeploy.json‎

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{
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"$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
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"contentVersion": "1.0.0.0",
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"metadata": {
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"_generator": {
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"name": "bicep",
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"version": "0.10.61.36676",
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"templateHash": "4722508883802150279"
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}
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},
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"parameters": {
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"location": {
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"type": "string",
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"defaultValue": "[resourceGroup().location]",
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"metadata": {
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"description": "Specifies the location for all resources."
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}
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},
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"workspaceName": {
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"type": "string",
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"metadata": {
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"description": "Specifies the name of the Azure Machine Learning workspace where sweep job will be deployed"
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}
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},
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"jobName": {
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"type": "string",
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"metadata": {
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"description": "Specifies the unique name for sweep job."
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}
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},
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"computeName": {
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"type": "string",
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"metadata": {
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"description": "Specifies the name of the Azure Machine Learning amlcompute cluster on which job will be run."
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}
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},
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"storageAccountName": {
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"type": "string",
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"metadata": {
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"description": "The name for the storage account to created and associated with the workspace."
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}
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},
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"experimentName": {
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"type": "string",
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"metadata": {
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"description": "Specifies the name of the Azure Machine Learning experiment under which job will be created."
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}
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},
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"_artifactsLocation": {
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"type": "string",
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"defaultValue": "[deployment().properties.templateLink.uri]",
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"metadata": {
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"description": "The base URI where artifacts required by this template are located including a trailing '/'."
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}
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},
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"_artifactsLocationSasToken": {
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"type": "secureString",
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"defaultValue": "",
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"metadata": {
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"description": "The sasToken required to access _artifactsLocation."
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}
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},
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"inputs": {
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"type": "object",
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"defaultValue": {
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"iris_csv": {
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"mode": "ReadOnlyMount",
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"uri": "[uri(parameters('_artifactsLocation'), format('data/iris.csv{0}', parameters('_artifactsLocationSasToken')))]",
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"jobInputType": "uri_file"
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}
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},
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"metadata": {
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"description": "Specifies dictionary of inputs search for sweep job."
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}
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},
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"limits": {
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"type": "object",
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"defaultValue": {
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"jobLimitsType": "Sweep",
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"timeout": "PT20M",
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"trialTimeout": "PT50S",
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"maxConcurrentTrials": 3,
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"maxTotalTrials": 5
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},
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"metadata": {
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"description": "Specifies execution contraints for sweep job."
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}
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},
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"objective": {
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"type": "object",
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"defaultValue": {
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"goal": "maximize",
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"primaryMetric": "result"
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},
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"metadata": {
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"description": "Specifies objective for sweep job."
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}
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},
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"samplingAlgorithmType": {
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"type": "string",
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"defaultValue": "Random",
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"metadata": {
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"description": "Specifies sampling algorithm for sweep job."
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}
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},
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"searchSpace": {
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"type": "object",
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"defaultValue": {
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"learning_rate": [
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"uniform",
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[
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"[json('0.01')]",
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"[json('0.9')]"
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]
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],
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"boosting": [
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"choice",
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[
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[
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"gbdt",
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"dart"
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]
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]
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]
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},
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"metadata": {
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"description": "Specifies different search space for sweep job."
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}
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},
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"command": {
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"type": "string",
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"defaultValue": "python main.py --iris-csv ${{inputs.iris_csv}} --learning-rate ${{search_space.learning_rate}} --boosting ${{search_space.boosting}}",
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"metadata": {
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"description": "Specifies command to be executed by trials of sweep job."
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}
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},
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"environmentName": {
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"type": "string",
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"defaultValue": "AzureML-lightgbm-3.2-ubuntu18.04-py37-cpu",
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"metadata": {
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"description": "Specifies the curated environment to run sweep job."
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}
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}
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},
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"resources": [
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{
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"type": "Microsoft.MachineLearningServices/workspaces/jobs",
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"apiVersion": "2022-06-01-preview",
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"name": "[format('{0}/{1}', parameters('workspaceName'), parameters('jobName'))]",
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"properties": {
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"description": "Sweep Job Resource from ARM Template",
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"properties": {},
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"tags": {
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"referenceNotebook": "https://github.com/Azure/azureml-examples/blob/main/sdk/jobs/single-step/lightgbm/iris/lightgbm-iris-sweep.ipynb"
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},
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"computeId": "[resourceId('Microsoft.MachineLearningServices/workspaces/computes', parameters('workspaceName'), parameters('computeName'))]",
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"displayName": "Sweep Job Resource",
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"experimentName": "[parameters('experimentName')]",
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"isArchived": false,
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"jobType": "Sweep",
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"inputs": "[parameters('inputs')]",
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"limits": "[parameters('limits')]",
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"objective": "[parameters('objective')]",
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"samplingAlgorithm": {
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"samplingAlgorithmType": "[parameters('samplingAlgorithmType')]"
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},
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"searchSpace": "[parameters('searchSpace')]",
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"trial": {
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"codeId": "[reference(resourceId('Microsoft.Resources/deployments', 'blob')).outputs.codeId.value]",
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"command": "[parameters('command')]",
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"environmentId": "[resourceId('Microsoft.MachineLearningServices/workspaces/environments/versions', parameters('workspaceName'), parameters('environmentName'), reference(resourceId('Microsoft.MachineLearningServices/workspaces/environments', split(format('{0}/{1}', parameters('workspaceName'), parameters('environmentName')), '/')[0], split(format('{0}/{1}', parameters('workspaceName'), parameters('environmentName')), '/')[1]), '2022-05-01').latestVersion)]",
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"environmentVariables": {}
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}
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},
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"dependsOn": [
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"[resourceId('Microsoft.Resources/deployments', 'blob')]"
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]
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},
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{
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"type": "Microsoft.Resources/deployments",
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"apiVersion": "2020-10-01",
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"name": "blob",
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"properties": {
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"expressionEvaluationOptions": {
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"scope": "inner"
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},
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"mode": "Incremental",
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"parameters": {
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"location": {
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"value": "[parameters('location')]"
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},
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"workspaceName": {
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"value": "[parameters('workspaceName')]"
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},
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"storageAccountName": {
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"value": "[parameters('storageAccountName')]"
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}
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},
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"template": {
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"$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
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"contentVersion": "1.0.0.0",
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"metadata": {
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"_generator": {
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"name": "bicep",
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"version": "0.10.61.36676",
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"templateHash": "17993837818224864413"
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}
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},
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"parameters": {
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"workspaceName": {
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"type": "string",
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"metadata": {
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"description": "Specifies the name of the Azure Machine Learning workspace where sweep job will be deployed"
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}
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},
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"filename": {
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"type": "string",
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"defaultValue": "main.py",
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"metadata": {
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"description": "Name of the blob as it is stored in the blob container"
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}
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},
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"containerName": {
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"type": "string",
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"defaultValue": "hdscript",
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"metadata": {
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"description": "Name of the blob container"
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}
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},
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"location": {
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"type": "string",
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"defaultValue": "[resourceGroup().location]",
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"metadata": {
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"description": "Azure region where resources should be deployed"
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}
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},
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"storageAccountName": {
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"type": "string",
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"metadata": {
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"description": "Desired name of the storage account"
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}
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},
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"codeVersion": {
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"type": "string",
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"defaultValue": "1",
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"metadata": {
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"description": "Specifies the env version for sweep job."
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}
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},
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"codeId": {
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"type": "string",
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"defaultValue": "code",
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"metadata": {
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"description": "Specifies the env for sweep job."
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}
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}
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},
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"variables": {
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"$fxv#0": "# imports\r\nimport os\r\nimport mlflow\r\nimport argparse\r\n\r\nimport pandas as pd\r\nimport lightgbm as lgbm\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom sklearn.metrics import log_loss, accuracy_score\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.model_selection import train_test_split\r\n\r\n# define functions\r\ndef main(args):\r\n # enable auto logging\r\n mlflow.autolog()\r\n\r\n # setup parameters\r\n num_boost_round = args.num_boost_round\r\n params = {\r\n \"objective\": \"multiclass\",\r\n \"num_class\": 3,\r\n \"boosting\": args.boosting,\r\n \"num_iterations\": args.num_iterations,\r\n \"num_leaves\": args.num_leaves,\r\n \"num_threads\": args.num_threads,\r\n \"learning_rate\": args.learning_rate,\r\n \"metric\": args.metric,\r\n \"seed\": args.seed,\r\n \"verbose\": args.verbose,\r\n }\r\n\r\n # read in data\r\n df = pd.read_csv(args.iris_csv)\r\n\r\n # process data\r\n X_train, X_test, y_train, y_test, enc = process_data(df)\r\n\r\n # train model\r\n model = train_model(params, num_boost_round, X_train, X_test, y_train, y_test)\r\n\r\n\r\ndef process_data(df):\r\n # split dataframe into X and y\r\n X = df.drop([\"species\"], axis=1)\r\n y = df[\"species\"]\r\n\r\n # encode label\r\n enc = LabelEncoder()\r\n y = enc.fit_transform(y)\r\n\r\n # train/test split\r\n X_train, X_test, y_train, y_test = train_test_split(\r\n X, y, test_size=0.2, random_state=42\r\n )\r\n\r\n # return splits and encoder\r\n return X_train, X_test, y_train, y_test, enc\r\n\r\n\r\ndef train_model(params, num_boost_round, X_train, X_test, y_train, y_test):\r\n # create lightgbm datasets\r\n train_data = lgbm.Dataset(X_train, label=y_train)\r\n test_data = lgbm.Dataset(X_test, label=y_test)\r\n\r\n # train model\r\n model = lgbm.train(\r\n params,\r\n train_data,\r\n num_boost_round=num_boost_round,\r\n valid_sets=[test_data],\r\n valid_names=[\"test\"],\r\n )\r\n\r\n # return model\r\n return model\r\n\r\n\r\ndef parse_args():\r\n # setup arg parser\r\n parser = argparse.ArgumentParser()\r\n\r\n # add arguments\r\n parser.add_argument(\"--iris-csv\", type=str)\r\n parser.add_argument(\"--num-boost-round\", type=int, default=10)\r\n parser.add_argument(\"--boosting\", type=str, default=\"gbdt\")\r\n parser.add_argument(\"--num-iterations\", type=int, default=16)\r\n parser.add_argument(\"--num-leaves\", type=int, default=31)\r\n parser.add_argument(\"--num-threads\", type=int, default=0)\r\n parser.add_argument(\"--learning-rate\", type=float, default=0.1)\r\n parser.add_argument(\"--metric\", type=str, default=\"multi_logloss\")\r\n parser.add_argument(\"--seed\", type=int, default=42)\r\n parser.add_argument(\"--verbose\", type=int, default=0)\r\n\r\n # parse args\r\n args = parser.parse_args()\r\n\r\n # return args\r\n return args\r\n\r\n\r\n# run script\r\nif __name__ == \"__main__\":\r\n # parse args\r\n args = parse_args()\r\n\r\n # run main function\r\n main(args)"
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},
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"resources": [
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{
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"type": "Microsoft.Storage/storageAccounts/blobServices/containers",
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"apiVersion": "2021-04-01",
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"name": "[format('{0}/{1}/{2}', parameters('storageAccountName'), 'default', parameters('containerName'))]",
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"properties": {
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"publicAccess": "Container"
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},
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"dependsOn": [
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"[resourceId('Microsoft.Storage/storageAccounts/blobServices', parameters('storageAccountName'), 'default')]"
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]
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},
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{
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"type": "Microsoft.Storage/storageAccounts/blobServices",
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"apiVersion": "2021-04-01",
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"name": "[format('{0}/{1}', parameters('storageAccountName'), 'default')]"
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},
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{
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"type": "Microsoft.Resources/deploymentScripts",
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"apiVersion": "2020-10-01",
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"name": "[format('deployscript-upload-blob-{0}', uniqueString(resourceId('Microsoft.Storage/storageAccounts/blobServices/containers', parameters('storageAccountName'), 'default', parameters('containerName'))))]",
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"location": "[parameters('location')]",
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"kind": "AzureCLI",
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"properties": {
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"azCliVersion": "2.26.1",
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"timeout": "PT5M",
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"retentionInterval": "PT1H",
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"environmentVariables": [
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{
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"name": "AZURE_STORAGE_ACCOUNT",
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"value": "[parameters('storageAccountName')]"
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},
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{
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"name": "AZURE_STORAGE_KEY",
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"secureValue": "[listKeys(resourceId('Microsoft.Storage/storageAccounts', parameters('storageAccountName')), '2021-04-01').keys[0].value]"
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},
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{
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"name": "CONTENT",
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"value": "[variables('$fxv#0')]"
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}
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],
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"scriptContent": "[format('echo \"$CONTENT\" > {0} && az storage blob upload -f {1} -c {2} -n {3}', parameters('filename'), parameters('filename'), parameters('containerName'), parameters('filename'))]"
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},
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"dependsOn": [
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"[resourceId('Microsoft.Storage/storageAccounts/blobServices/containers', parameters('storageAccountName'), 'default', parameters('containerName'))]"
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]
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},
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{
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"type": "Microsoft.MachineLearningServices/workspaces/codes/versions",
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"apiVersion": "2022-05-01",
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"name": "[format('{0}/{1}-{2}/{3}', parameters('workspaceName'), parameters('codeId'), uniqueString(resourceId('Microsoft.Storage/storageAccounts/blobServices/containers', parameters('storageAccountName'), 'default', parameters('containerName'))), parameters('codeVersion'))]",
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"properties": {
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"codeUri": "[uri(format('https://{0}.blob.{1}/', parameters('storageAccountName'), environment().suffixes.storage), format('{0}/', parameters('containerName')))]",
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"isAnonymous": false
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},
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"dependsOn": [
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"[resourceId('Microsoft.Storage/storageAccounts/blobServices/containers', parameters('storageAccountName'), 'default', parameters('containerName'))]",
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"[resourceId('Microsoft.Resources/deploymentScripts', format('deployscript-upload-blob-{0}', uniqueString(resourceId('Microsoft.Storage/storageAccounts/blobServices/containers', parameters('storageAccountName'), 'default', parameters('containerName')))))]"
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]
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}
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],
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"outputs": {
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"codeId": {
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"type": "string",
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"value": "[resourceId('Microsoft.MachineLearningServices/workspaces/codes/versions', split(format('{0}/{1}-{2}/{3}', parameters('workspaceName'), parameters('codeId'), uniqueString(resourceId('Microsoft.Storage/storageAccounts/blobServices/containers', parameters('storageAccountName'), 'default', parameters('containerName'))), parameters('codeVersion')), '/')[0], split(format('{0}/{1}-{2}/{3}', parameters('workspaceName'), parameters('codeId'), uniqueString(resourceId('Microsoft.Storage/storageAccounts/blobServices/containers', parameters('storageAccountName'), 'default', parameters('containerName'))), parameters('codeVersion')), '/')[1], split(format('{0}/{1}-{2}/{3}', parameters('workspaceName'), parameters('codeId'), uniqueString(resourceId('Microsoft.Storage/storageAccounts/blobServices/containers', parameters('storageAccountName'), 'default', parameters('containerName'))), parameters('codeVersion')), '/')[2])]"
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}
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}
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}
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}
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}
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],
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"outputs": {
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"Job_Studio_Endpoint": {
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"type": "string",
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"value": "[reference(resourceId('Microsoft.MachineLearningServices/workspaces/jobs', split(format('{0}/{1}', parameters('workspaceName'), parameters('jobName')), '/')[0], split(format('{0}/{1}', parameters('workspaceName'), parameters('jobName')), '/')[1])).services.Studio.endpoint]"
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}
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}
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}

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