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Create Tabular Dataset from Relative Path in Datastore

Data ultimo aggiornamento: 11/05/2021

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

Questo modello di Azure Resource Manager è stato creato da un membro della community e non da Microsoft. Ogni modello di Azure Resource Manager viene concesso in licenza ai sensi di un contratto di licenza a cura del proprietario e non di Microsoft. Microsoft non è responsabile dei modelli di Azure Resource Manager forniti e concessi in licenza da membri della community e non ne verifica la sicurezza, la compatibilità o le prestazioni. I modelli di Azure Resource Manager della community non sono supportati nell'ambito di alcun programma o servizio di supporto tecnico Microsoft e vengono resi disponibili COSÌ COME SONO senza garanzie di alcun tipo.

Parametri

Nome parametro Descrizione
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.

Usa il modello

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/quickstarts/microsoft.machinelearningservices/machine-learning-dataset-create-tabular-from-relative-path/azuredeploy.json
Installare e configurare Azure PowerShell

Riga di comando

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/quickstarts/microsoft.machinelearningservices/machine-learning-dataset-create-tabular-from-relative-path/azuredeploy.json
Installare e configurare l'interfaccia della riga di comando multipiattaforma di Azure