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Create Tabular Dataset from Relative Path in Datastore

Achal Jain 提供
上次更新时间: 2020/7/12

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

此 Azure Resource Manager (ARM) 模板由社区的某个成员(而不是由 Microsoft)创建。每个 ARM 模板都根据其所有者(不是 Microsoft)的许可协议向你授予许可。Microsoft 不对由社区成员提供并授予许可的 ARM 模板负责,并且不针对安全性、兼容性和性能进行筛选。社区 ARM 模板不由任何 Microsoft 支持计划或服务提供支持,按“原样”提供,没有任何种类的担保。

参数

参数名 说明
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.

使用模板

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
安装和配置 Azure PowerShell

命令行

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
安装和配置 Azure 跨平台命令行界面