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articles/machine-learning/data-science-virtual-machine/dsvm-tutorial-bicep.md

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---
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title: 'Quickstart: Create an Azure Data Science VM - Bicep'
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titleSuffix: Azure Data Science Virtual Machine
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description: In this quickstart, you use Bicep to quickly deploy a Data Science Virtual Machine
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description: In this quickstart, you use Bicep to quickly deploy a Data Science Virtual Machine.
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services: machine-learning
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author: s-polly
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ms.author: scottpolly
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ms.date: 05/02/2022
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ms.author: scottpolly
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ms.reviewer: franksolomon
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ms.date: 04/22/2024
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ms.topic: quickstart
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ms.service: data-science-vm
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ms.custom: subject-armqs, mode-arm, devx-track-bicep
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---
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# Quickstart: Create an Ubuntu Data Science Virtual Machine using Bicep
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This quickstart will show you how to create an Ubuntu Data Science Virtual Machine using Bicep. Data Science Virtual Machines are cloud-based virtual machines preloaded with a suite of data science and machine learning frameworks and tools. When deployed on GPU-powered compute resources, all tools and libraries are configured to use the GPU.
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This quickstart shows how to create an Ubuntu Data Science Virtual Machine using Bicep. A Data Science Virtual Machine (DSVM) is a cloud-based virtual machine, preloaded with a suite of data science and machine learning frameworks and tools. When deployed on GPU-powered compute resources, all tools and libraries are configured to use the GPU.
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[!INCLUDE [About Bicep](../../../includes/resource-manager-quickstart-bicep-introduction.md)]
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## Review the Bicep file
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The Bicep file used in this quickstart is from [Azure Quickstart Templates](https://azure.microsoft.com/resources/templates/vm-ubuntu-DSVM-GPU-or-CPU/).
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This quickstart uses the Bicep file from the [Azure Quickstart Templates](https://azure.microsoft.com/resources/templates/vm-ubuntu-DSVM-GPU-or-CPU/).
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:::code language="bicep" source="~/quickstart-templates/application-workloads/datascience/vm-ubuntu-DSVM-GPU-or-CPU/main.bicep":::
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The following resources are defined in the Bicep file:
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The Bicep file defines these resources:
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* [Microsoft.Network/networkInterfaces](/azure/templates/microsoft.network/networkinterfaces)
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* [Microsoft.Network/networkSecurityGroups](/azure/templates/microsoft.network/networksecuritygroups)
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* [Microsoft.Network/virtualNetworks](/azure/templates/microsoft.network/virtualnetworks)
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* [Microsoft.Network/publicIPAddresses](/azure/templates/microsoft.network/publicipaddresses)
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* [Microsoft.Storage/storageAccounts](/azure/templates/microsoft.storage/storageaccounts)
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* [Microsoft.Compute/virtualMachines](/azure/templates/microsoft.compute/virtualmachines): Create a cloud-based virtual machine. In this template, the virtual machine is configured as a Data Science Virtual Machine running Ubuntu.
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* [Microsoft.Compute/virtualMachines](/azure/templates/microsoft.compute/virtualmachines): Create a cloud-based virtual machine. In this template, the virtual machine is configured as a Data Science Virtual Machine that runs Ubuntu.
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## Deploy the Bicep file
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1. Save the Bicep file as **main.bicep** to your local computer.
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1. Deploy the Bicep file using either Azure CLI or Azure PowerShell.
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1. Save the Bicep file as **main.bicep** to your local computer
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1. Deploy the Bicep file with either Azure CLI or Azure PowerShell
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# [Azure CLI](#tab/CLI)
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> [!NOTE]
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> Replace **\<admin-user\>** with the username for the administrator account. Replace **\<vm-name\>** with the name of your virtual machine.
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When the deployment finishes, you should see a message indicating the deployment succeeded.
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When the deployment finishes, you should see a message indicating that the deployment succeeded.
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## Review deployed resources
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## Clean up resources
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When no longer needed, use the Azure portal, Azure CLI, or Azure PowerShell to delete the resource group and its resources.
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When you no longer need your resources, use the Azure portal, Azure CLI, or Azure PowerShell to delete both the resource group and its resources.
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# [Azure CLI](#tab/CLI)
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