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‎articles/sql-database/sql-database-quickstart-r-create-script.md

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ms.date: 04/11/2019
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---
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# Create and run simple R scripts in Azure SQL Database Machine Learning Services (preview)
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# Quickstart: Create and run simple R scripts in Azure SQL Database Machine Learning Services (preview)
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In this quickstart, you'll create and run a set of simple R scripts using the public preview of [Machine Learning Services (with R) in Azure SQL Database](sql-database-machine-learning-services-overview.md). You'll learn how to wrap a well-formed R script in the stored procedure [sp_execute_external_script](https://docs.microsoft.com/sql/relational-databases/system-stored-procedures/sp-execute-external-script-transact-sql) and execute the script in a SQL database.
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In this quickstart, you create and run a set of R scripts using Machine Learning Services (with R) in Azure SQL Database.
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[!INCLUDE[ml-preview-note](../../includes/sql-database-ml-preview-note.md)]
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## Prerequisites
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- If you don't have an Azure subscription, [create an account](https://azure.microsoft.com/free/) before you begin.
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- An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/?ref=microsoft.com&utm_source=microsoft.com&utm_medium=docs&utm_campaign=visualstudio).
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- To run the example code in these exercises, you must first have an Azure SQL database with Machine Learning Services (with R) enabled. During the public preview, Microsoft will onboard you and enable machine learning for your existing or new database. Follow the steps in [Sign up for the preview](sql-database-machine-learning-services-overview.md#signup).
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- An [Azure SQL database](sql-database-single-database-get-started.md) with a [server-level firewall rule](sql-database-server-level-firewall-rule.md)
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- Make sure you've installed the latest [SQL Server Management Studio](https://docs.microsoft.com/sql/ssms/sql-server-management-studio-ssms) (SSMS). You can run R scripts using other database management or query tools, but in this quickstart you'll use SSMS.
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- [Machine Learning Services](sql-database-machine-learning-services-overview.md) with R enabled. [Sign up for the preview](sql-database-machine-learning-services-overview.md#signup).
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- This quickstart requires that you configure a server-level firewall rule. For information on how to do this, see [Create server-level firewall rule](sql-database-server-level-firewall-rule.md).
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- [SQL Server Management Studio](/sql/ssms/sql-server-management-studio-ssms) (SSMS)
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> [!NOTE]
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> During the public preview, Microsoft will onboard you and enable machine learning for your existing or new database.
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This example uses the stored procedure [sp_execute_external_script](/sql/relational-databases/system-stored-procedures/sp-execute-external-script-transact-sql) to wrap a well-formed R script.
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## Run a simple script
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‎articles/sql-database/sql-database-quickstart-r-train-score-model.md

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ms.date: 04/11/2019
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---
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# Create and train a predictive model in R with Azure SQL Database Machine Learning Services (preview)
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# Quickstart: Create and train a predictive model in R with Azure SQL Database Machine Learning Services (preview)
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In this quickstart, you'll create and train a predictive model using R, save the model to a table in your SQL database, then use the model to predict values from new data using the public preview of [Machine Learning Services (with R) in Azure SQL Database](sql-database-machine-learning-services-overview.md).
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The model you'll use in this quickstart is a simple regression model that predicts the stopping distance of a car based on speed. You'll use the **cars** dataset included with R, because it's small and easy to understand.
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> [!TIP]
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> Many datasets, small and large, are included with the R runtime. To get a list of datasets installed with R, type `library(help="datasets")` from an R command prompt.
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In this quickstart, you create and train a predictive model using R, save the model to a table in your database, then use the model to predict values from new data using Machine Learning Services (with R) in Azure SQL Database.
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[!INCLUDE[ml-preview-note](../../includes/sql-database-ml-preview-note.md)]
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## Prerequisites
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- If you don't have an Azure subscription, [create an account](https://azure.microsoft.com/free/) before you begin.
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- An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/?ref=microsoft.com&utm_source=microsoft.com&utm_medium=docs&utm_campaign=visualstudio).
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- An [Azure SQL database](sql-database-single-database-get-started.md) with a [server-level firewall rule](sql-database-server-level-firewall-rule.md)
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- To run the example code in these exercises, you must first have an Azure SQL database with Machine Learning Services (with R) enabled. During the public preview, Microsoft will onboard you and enable machine learning for your existing or new database. Follow the steps in [Sign up for the preview](sql-database-machine-learning-services-overview.md#signup).
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- [Machine Learning Services](sql-database-machine-learning-services-overview.md) with R enabled. [Sign up for the preview](sql-database-machine-learning-services-overview.md#signup).
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- Make sure you've installed the latest [SQL Server Management Studio](https://docs.microsoft.com/sql/ssms/sql-server-management-studio-ssms) (SSMS). You can run the R scripts using other database management or query tools, but in this quickstart you'll use SSMS.
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- [SQL Server Management Studio](/sql/ssms/sql-server-management-studio-ssms) (SSMS)
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- This quickstart requires that you configure a server-level firewall rule. For information on how to do this, see [Create server-level firewall rule](sql-database-server-level-firewall-rule.md).
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> [!NOTE]
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> During the public preview, Microsoft will onboard you and enable machine learning for your existing or new database.
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This example uses a simple regression model to predict the stopping distance of a car based on speed using the **cars** dataset included with R.
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> [!TIP]
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> Many datasets are included with the R runtime, to get a list of installed datasets, type `library(help="datasets")` from the R command prompt.
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## Create and train a predictive model
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‎articles/virtual-machines/linux/image-builder-json.md

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---
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title: Create an Azure Image Builder template (preview)
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description: Learn how to create a template to use with Azure Image Builder.
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author: cynthn
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ms.author: cynthn
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ms.date: 07/31/2019
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author: danis
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ms.author: danis
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ms.date: 01/23/2020
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ms.topic: article
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ms.service: virtual-machines-linux
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"tags": {
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"<name": "<value>",
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"<name>": "<value>"
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},
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}
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"identity":{},
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"dependsOn": [],
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"properties": {
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"buildTimeoutInMinutes": <minutes>,
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"buildTimeoutInMinutes": <minutes>,
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"vmProfile":
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{
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"vmSize": "<vmSize>"
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},
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"build": {},
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"customize": {},
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"distribute": {}
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```json
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```
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## vmProfile
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By default Image Builder will use a "Standard_D1_v2" build VM, you can override this, for example, if you want to customize an Image for a GPU VM, you need a GPU VM size. This is optional.
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```json
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{
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"vmSize": "Standard_D1_v2"
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},
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```
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## osDiskSizeGB
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By default, Image Builder will not change the size of the image, it will use the size from the source image. You can adjust the size of the OS Disk (Win and Linux), note, do not go too small than the minimum required space required for the OS. This is optional, and a value of 0 means leave the same size as the source image. This is optional.
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```json
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{
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"osDiskSizeGB": 100
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},
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```
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## Tags
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> The access tokens of the links are refreshed at frequent intervals, so every time you want to submit a template, you must check if the RH link address has changed.
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### PlatformImage source
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Azure Image Builder supports the following Azure Marketplace images:
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* Ubuntu 18.04
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* Ubuntu 16.04
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* RHEL 7.6
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* CentOS 7.6
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* Windows 2016
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* Windows 2019
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Azure Image Builder supports Windows Server and client, and Linux Azure Marketplace images, see [here](https://docs.microsoft.com/azure/virtual-machines/windows/image-builder-overview#os-support) for the full list.
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```json
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"source": {
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{
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"type": "Shell",
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"scriptUri": "<path to script>"
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"scriptUri": "<path to script>",
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"sha256Checksum": "<sha256 checksum>"
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"scriptUri": "<link to script>",
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"sha256Checksum": "<sha256 checksum>"
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- **sha256Checksum** - Value of sha256 checksum of the file, you generate this locally, and then Image Builder will checksum and validate.
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* To generate the sha256Checksum, using a terminal on Mac/Linux run: `sha256sum <fileName>`
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For commands to run with super user privileges, they must be prefixed with `sudo`.
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> When running the shell customizer with RHEL ISO source, you need to ensure your first customization shell handles registering with a Red Hat entitlement server before any customization occurs. Once customization is complete, the script should unregister with the entitlement server.
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"restartTimeout": "5m"
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}],
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{
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"type": "WindowsRestart",
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"restartCommand": "shutdown /r /f /t 0 /c",
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"restartCheckCommand": "echo Azure-Image-Builder-Restarted-the-VM > c:\\buildArtifacts\\azureImageBuilderRestart.txt",
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"restartTimeout": "5m"
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}
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"scriptUri": "<path to script>",
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"runElevated": "<true false>",
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"runElevated": "<true or false>"
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- **runElevated** – Optional, boolean, support for running commands and scripts with elevated permissions.
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- **sha256Checksum** - Value of sha256 checksum of the file, you generate this locally, and then Image Builder will checksum and validate.
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You can distribute an image to both of the target types in the same configuration, please see [examples](https://github.com/danielsollondon/azvmimagebuilder/blob/7f3d8c01eb3bf960d8b6df20ecd5c244988d13b6/armTemplates/azplatform_image_deploy_sigmdi.json#L80).
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Because you can have more than one target to distribute to, Image Builder maintains a state for every distribution target that can be accessed by querying the `runOutputName`. The `runOutputName` is an object you can query post distribution for information about that distribution. For example, you can query the location of the VHD, or regions where the image version was replicated to. This is a property of every distribution target. The `runOutputName` must be unique to each distribution target.
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Because you can have more than one target to distribute to, Image Builder maintains a state for every distribution target that can be accessed by querying the `runOutputName`. The `runOutputName` is an object you can query post distribution for information about that distribution. For example, you can query the location of the VHD, or regions where the image version was replicated to, or SIG Image version created. This is a property of every distribution target. The `runOutputName` must be unique to each distribution target. Here is an example, this is querying a Shared Image Gallery distribution:
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```bash
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subscriptionID=<subcriptionID>
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imageResourceGroup=<resourceGroup of image template>
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runOutputName=<runOutputName>
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az resource show \
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--ids "/subscriptions/$subscriptionID/resourcegroups/$imageResourceGroup/providers/Microsoft.VirtualMachineImages/imageTemplates/ImageTemplateLinuxRHEL77/runOutputs/$runOutputName" \
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--api-version=2019-05-01-preview
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```
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Output:
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```json
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{
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"id": "/subscriptions/xxxxxx/resourcegroups/rheltest/providers/Microsoft.VirtualMachineImages/imageTemplates/ImageTemplateLinuxRHEL77/runOutputs/rhel77",
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"identity": null,
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"kind": null,
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"location": null,
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"managedBy": null,
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"name": "rhel77",
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"plan": null,
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"properties": {
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"artifactId": "/subscriptions/xxxxxx/resourceGroups/aibDevOpsImg/providers/Microsoft.Compute/galleries/devOpsSIG/images/rhel/versions/0.24105.52755",
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"provisioningState": "Succeeded"
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},
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"resourceGroup": "rheltest",
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"sku": null,
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"tags": null,
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"type": "Microsoft.VirtualMachineImages/imageTemplates/runOutputs"
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}
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```
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There are sample .json files for different scenarios in the [Azure Image Builder GitHub](https://github.com/danielsollondon/azvmimagebuilder).
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