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Create Tabular Dataset from Relative Path in Datastore

게시자: Achal Jain
마지막 업데이트: 2020-07-12

This template creates a tabular dataset from relative path in datastore in Azure Machine Learning workspace.

이 Azure Resource Manager 템플릿은 Microsoft가 아니라 커뮤니티 구성원에 의해 만들어졌습니다. 각 Resource Manager 템플릿은 Microsoft가 아닌 해당 소유자의 사용권 계약에 의거하여 사용이 허가됩니다. Microsoft는 커뮤니티 구성원에 의해 제공 및 라이선스가 부여된 Resource Manager 템플릿에 대해 책임이 없으며, 보안, 호환성 또는 성능을 검사하지 않습니다. 커뮤니티 Resource Manager 템플릿은 Microsoft 지원 프로그램 또는 서비스에서 지원되지 않고, 어떠한 보증도 없이 있는 그대로 제공됩니다.

매개 변수

매개 변수 이름 설명
workspaceName Specifies the name of the Azure Machine Learning workspace which will hold this datastore target.
datasetName The name of the dataset.
datasetDescription Optional : The description for the dataset.
datastoreName The datastore name.
relativePath Path within the datastore
sourceType Data source type
separator Optional: The separator used to split columns for 'delimited_files' sourceType, default to ',' for 'delimited_files'
header Optional : Header type. Defaults to 'all_files_have_same_headers'
partitionFormat Optional : The partition information of each path will be extracted into columns based on the specified format. Format part '{column_name}' creates string column, and '{column_name:yyyy/MM/dd/HH/mm/ss}' creates datetime column, where 'yyyy', 'MM', 'dd', 'HH', 'mm' and 'ss' are used to extract year, month, day, hour, minute and second for the datetime type. The format should start from the position of first partition key until the end of file path. For example, given the path '../USA/2019/01/01/data.parquet' where the partition is by country/region and time, partition_format='/{CountryOrRegion}/{PartitionDate:yyyy/MM/dd}/data.csv' creates a string column'CountryOrRegion' with the value 'USA' and a datetime column 'PartitionDate' with the value '2019-01-01
fineGrainTimestamp Optional : Column name to be used as FineGrainTimestamp
coarseGrainTimestamp Optional : Column name to be used as CoarseGrainTimestamp. Can only be used if 'fineGrainTimestamp' is specified and cannot be same as 'fineGrainTimestamp'.
tags Optional : Provide JSON object with 'key,value' pairs to add as tags on dataset. Example- {"sampleTag1": "tagValue1", "sampleTag2": "tagValue2"}
skipValidation Optional : Skip validation that ensures data can be loaded from the dataset before registration.
includePath Optional : Boolean to keep path information as column in the dataset. Defaults to False. This is useful when reading multiple files, and want to know which file a particular record originated from, or to keep useful information in file path.
location The location of the Azure Machine Learning Workspace.

템플릿 사용

PowerShell

New-AzResourceGroup -Name <resource-group-name> -Location <resource-group-location> #use this command when you need to create a new resource group for your deployment
New-AzResourceGroupDeployment -ResourceGroupName <resource-group-name> -TemplateUri https://raw.githubusercontent.com/Azure/azure-quickstart-templates/master/101-machine-learning-dataset-create-tabular-from-relative-path/azuredeploy.json
Azure PowerShell 설치 및 구성

명령줄

az group create --name <resource-group-name> --location <resource-group-location> #use this command when you need to create a new resource group for your deployment
az group deployment create --resource-group <my-resource-group> --template-uri https://raw.githubusercontent.com/Azure/azure-quickstart-templates/master/101-machine-learning-dataset-create-tabular-from-relative-path/azuredeploy.json
Azure 크로스 플랫폼 명령줄 인터페이스 설치 및 구성