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Create Tabular Dataset from Relative Path in Datastore

Dernière mise à jour : 11/05/2021

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

Ce modèle ARM (Azure Resource Manager) a été créé par un membre de la communauté et non par Microsoft. Chaque modèle ARM vous est concédé sous licence sous un contrat de licence par son propriétaire, et non par Microsoft. Microsoft ne peut pas être tenu responsable des modèles ARM fournis et concédés sous licence par les membres de la communauté, ni ne vérifie leur sécurité, leur compatibilité ou leurs performances. Les modèles ARM de la communauté ne sont pris en charge par aucun programme ou service de support Microsoft. Ils sont rendus disponibles EN L'ÉTAT sans garantie d'aucune sorte.

Paramètres

Nom du paramètre Description
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.

Utiliser le modèle

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
Installer et configurer Azure PowerShell

Ligne de commande

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
Installer et configurer l'interface de ligne de commande multiplateforme Azure