--- title: "Setup and Configuration | Microsoft Docs" ms.custom: "" ms.date: "03/30/2017" ms.prod: "sql-server-2016" ms.reviewer: "" ms.suite: "" ms.technology: - "r-services" ms.tgt_pltfrm: "" ms.topic: "article" author: "jeannt" ms.author: "jeannt" manager: "jhubbard" --- # Set up Python Machine Learning Services (In-Database) You install the components required for using Python by running the [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] setup wizard and following the interactive prompts as described in this topic. **Overview of the setup process** + Be sure to install the database engine. An instance of SQL Server is required to run Python scripts in-database. + Choose the **Machine Learning Services** feature, and select **Python** as the language. + After installation is complete, reconfigure the instance to allow execution of scripts that use an external executable. + You might need to make some additional configuration of the worker account pool that is used to run Python. + Each configuration change requires a restart of the Trusted Launchpad service. To perform an unattended installation, use the command-line options for SQL Server setup and the arguments specific to Python, as described here: [Unattended Installs of SQL Server with Python Machine Learning Services](./unattended-installs-of-sql-server-python-services.md). ## Prerequisites + SQL Server 2017 is required. Python integration is not supported on previous versions of SQL Server. + Additional prerequisites, such as .NET Core, are installed as part of the Python component setup. + The **Shared Features** section contains a separate installation option, **Machine Learning Server (Standalone)**. We recommend that you **do not** install this on the same computer as a SQL Server instance that uses R Services or Python Services. Instead, install the Machine Learning Server (Standalone) on a separate computer that you will use for developing and testing your R solutions. + You cannot install Machine Learning with Python Services on a failover cluster. The reason is that the security mechanism used for isolating Python processes is not compatible with a Windows Server failover cluster environment. As a workaround, you can use replication to copy necessary tables to a standalone SQL Server instance that uses Python Services, or you can install Machine Learning with Python Services on a standalone computer that uses Always On and is part of an availability group. + Side-by-side installation with other versions of Python is possible, because the SQL Server instance uses its own copy of the Anaconda distribution. However, running code using Python on the SQL Server computer outside of SQL Server can lead to various problems: + You use a different library and different executable and get different results than when running in SQL Server. + Python scripts running in external libraries cannot be managed by SQL Server, leading to resource contention. > [!IMPORTANT] > After setup is complete, be sure to complete the additional post-configuration steps described in this topic. These include enabling SQL Server to use external scripts, and adding accounts required for SQL Server to run Python jobs on your behalf. ## Step 1: Install Machine Learning Services (In-Database) on SQL Server 1. Run the setup wizard for SQL Server 2017. 2. On the **Installation** tab, click **New SQL Server stand-alone installation or add features to an existing installation**. 3. On the **Feature Selection** page, select both of these options: - **Database Engine Services** To use Python with SQL Server, you must install an instance of the database engine. You can use either a default or named instance. - **Machine Services (In-Database)** This option installs the database services that support Python script execution. - **Python** Check this option to get the Python 3.5 executable and select libraries from the Anaconda distribution. We recommend you install only one language per instance. > [!NOTE] > Do not select the option in **Shared Features** for **Microsoft R Server (Standalone)**. Use this option in a separate installation if you need to add the Machine Learning components to a different computer that is used for R development, such as your data scientist's laptop. ![Setup options for Python](media/ml-svcs-features-python-highlight.png "Setup options for Python") 4. On the page, **Consent to Install Python**, click **Accept**. This license agreement is required to download the Python executable, Python packages from Anaconda. ![Agreement to Python license](media/ml-svcs-license-python.png "License agreement for Python") > [!NOTE] > If the computer you are using does not have Internet access, you can pause setup at this point to download the installers separately as described here: [Installing Components without Internet Access](../../advanced-analytics/r-services/installing-ml-components-without-internet-access.md) Click **Accept**, wait until the **Next** button becomes active, and then click **Next**. 5. On the **Ready to Install** page, verify that these selections are included, and click **Install**. + Database Engine Services + Machine Learning Services (In-Database) + Python These selections represent the minimum configuration required to use Python with SQL Server. ![Ready to install Python](media/ready-to-install-python.png "Required components for Python install") Optionally, make a note of the location of the folder under the path `..\Setup Bootstrap\Log` where the configuration files are stored. When setup is complete, you can review the installed components in the Summary file. 6. When installation is complete, restart the computer. ## Step 2: Enable Python script execution 1. Open [!INCLUDE[ssManStudioFull](../../includes/ssmanstudiofull-md.md)]. If it is not already installed, you can re-run the SQL Server setup wizard to open a download link and install it. 2. Connect to the instance where you installed Machine Learning Services, and run the following command: ``` sp_configure ``` The value for the property, `external scripts enabled`, should be **0** at this point. That is because the feature is turned off by default, to reduce the surface area. The feature must be explicitly enabled by an administrator before you can run R or Python scripts. 3. To enable the external scripting feature that supports Python, run the following statement. ``` EXEC sp_configure 'external scripts enabled', 1 RECONFIGURE WITH OVERRIDE ``` Note that this is exactly the same process that is used to enable R, because the underlying extensibility feature supports both languages. 10. Restart the SQL Server service for the [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] instance. Restarting the SQL Server service will also automatically restart the related [!INCLUDE[rsql_launchpad](../../includes/rsql-launchpad-md.md)] service. You can restart the service using the **Services** panel in Control Panel, or by using [SQL Server Configuration Manager](https://docs.microsoft.com/en-us/relational-databases/sql-server-configuration-manager. ## Step 3. Verify that the external script execution feature is running Take a moment to verify that all components used to launch Python script are running. 1. In SQL Server Management Studio, open a new Query window, and run the following command: ``` exec sp_configure 'external scripts enabled' ``` The **run_value** should now be set to 1. 2. Open the **Services** panel or [SQL Server Configuration Manager](https://docs.microsoft.com/en-us/relational-databases/sql-server-configuration-manager) and verify that the Launchpad service for your instance is running. If the Launchpad is not running, restart the service. If you have installed multiple instances of SQL Server, using either R or Python, each instance has its own Launchpad service. However, if you install R and Python on a single instance, only one Launchpad is installed, but a separate language-specific launcher DLL is added for each language. For more information, see [Components to Support Python Integration](new-components-in-sql-server-to-support-python-integration.md) 3. If Launchpad is running, you should be able to run simple Python scripts like the following in [!INCLUDE[ssManStudioFull](../../includes/ssmanstudiofull-md.md)]: ``` exec sp_execute_external_script @language =N'Python', @script=N'OutputDataSet=InputDataSet', @input_data_1 = N'SELECT 1 as col' ``` **Results** *col* *1* ## Troubleshooting and Additional Steps If this command was successful, you can run Python commands from SQL Server Management Studio, Visual Studio Code, or any other client that can send T-SQL statements to the server. If not, review the following list and see if changes to the configuration of the service or database might be needed. It is important to note that not all of the listed changes are required, and none might be required. It depends on your security schema, where you installed SQL Server, and how you expect users to connect to the database and run external scripts. ### Enable implied authentication for Launchpad account group During setup, a number of new Windows user accounts are created for the purpose of running tasks under the security token of the [!INCLUDE[rsql_launchpad_md](../../includes/rsql-launchpad-md.md)] service. When a user sends a Python or R script from an external client, [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] will activate an available worker account, map it to the identity of the calling user, and run the script on behalf of the user. This is called called *implied authentication*, and is a service of the database engine that supports secure execution of external scripts in SQL Server 2016 and SQL Server 2017. You can view these accounts in the Windows user group, **SQLRUserGroup**. By default, 20 worker accounts are created, which is typically more than enough for running external script jobs. > [!IMPORTANT] > The worker group is named SQLRUserGroup regardless of the type of script you are running. There is a single group for each instance. If you need to run R scripts from a remote data science client and are using Windows authentication, these worker accounts must be given permission to log into the [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] instance on your behalf. 1. In [!INCLUDE[ssManStudioFull](../../includes/ssmanstudiofull-md.md)], in Object Explorer, expand **Security**, right-click **Logins**, and select **New Login**. 2. In the **Login - New** dialog box, click **Search**. 3. Click **Object Types** and select **Groups**. Deselect everything else. 4. In Enter the object name to select, type *SQLRUserGroup* and click **Check Names**. 5. The name of the local group associated with the instance's Launchpad service should resolve to something like *instancename\SQLRUserGroup*. Click **OK**. 6. By default, the login is assigned to the **public** role and has permission to connect to the database engine. 7. Click **OK**. > [!NOTE] > If you use a SQL login for running scripts in a SQL Server compute context, this extra step is not required. ### Give users permission to run external scripts If you installed [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] yourself and are running R scripts in your own instance, you are typically executing scripts as an administrator and thus have implicit permission over various operations and all data in the database, as well as the ability to install new R packages as needed. However, in an enterprise scenario, most users, including those accessing the database using SQL logins, do not have such elevated permissions. Therefore, for each user that will be running external scripts, you must grant users of machine learning services the permission to run scripts in each database where R or Python will be used. ``` USE GO GRANT EXECUTE ANY EXTERNAL SCRIPT TO [UserName] ``` Note that permissions are not specific to the supported script language. In other words, there are not separate permission levels for R script vs. Python script. If you need to maintain separate permissions for these languages, you can install R and Python on separate instances. ### Add more worker accounts If you expect many users to be running scripts concurrently, you can increase the number of worker accounts that are assigned to the Launchpad service. For more information, see [Modify the User Account Pool for SQL Server R Services](../../advanced-analytics/r-services/modify-the-user-account-pool-for-sql-server-r-services.md). ### Give your users read, write, or DDL permissions to databases While running scripts, the user account or SQL login might need to read data from other databases, create new tables to store results, and write data into tables. For each user account or SQL login that will be executing R or Python scripts, be sure that the account or login has **db_datareader**, **db_datawriter**, or **db_ddladmin** permissions on the specific database. For example, the following [!INCLUDE[tsql](../../includes/tsql-md.md)] statement gives the SQL login *MySQLLogin* the rights to run T-SQL queries in the *ML_Samples* database. To run this statement, the SQL login must already exist in the security context of the server. ``` USE ML_Samples GO EXEC sp_addrolemember 'db_datareader', 'MySQLLogin' ``` For more information about the permissions included in each role, see [Database-Level Roles](../../relational-databases/security/authentication-access/database-level-roles.md). ### Ensure that the SQL Server supports remote connections If you cannot connect from a remote computer, check whether the firewall allows access to SQL Server. In a default installation, remote connections might be disabled, or the specific port used by SQL Server might be blocked by the firewall. For more information, see [Configure Windows Firewall for Database Engine Access](https://docs.microsoft.com/sql/database-engine/configure-windows/configure-a-windows-firewall-for-database-engine-access) ### Create an ODBC data source for the instance on your data science client If you create a machine learning solution on a data science client computer and need to run code using the SQL Server computer as the compute context, you can use either a SQL login, or integrated Windows authentication. + For SQL logins: Ensure that the login has appropriate permissions on the database where you will be reading data. You can do this by adding *Connect to* and *SELECT* permissions, or by adding the login to the **db_datareader** role. If you need to create objects, you will need **DDL_admin** rights. To save data to tables, add the login to the **db_datawriter** role. + For Windows authentication: You must configure an ODBC data source on the data science client that specifies the instance name and other connection information. For more information, see [Using the ODBC Data Source Administrator](http://windows.microsoft.com/windows/using-odbc-data-source-administrator). ### Optimize the server for script execution The default settings for [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] setup are intended to optimize the balance of the server for a variety of services supported by the database engine, which might include ETL processes, reporting, auditing, and applications that use [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] data. As a result, if you use the default settings created by SQ Server setup, you might find that resources for running external scripts are restricted or throttled, particularly in memory-intensive operations. If machine learning is a priority, you need to change default database settings to ensure that external script jobs are prioritized and resourced appropriately. These changes might include: + Reduce the amount of memory allocated to the [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] database engine + Increase the number of accounts running under the [!INCLUDE[rsql_launchpad](../../includes/rsql-launchpad-md.md)] service. This does not increase the number of resources, but does increase the number of scripts that can run concurrently. If you have Enterprise Edition, we recommend that you use Resource Governor to configure an external resource pool specific for Python. See these articles for more information: - Configure a resource pool for managing external resources [CREATE EXTERNAL RESOURCE POOL (Transact-SQL)](../../t-sql/statements/create-external-resource-pool-transact-sql.md) - Change the amount of memory reserved for the database engine [Server Memory Server Configuration Options](../../database-engine/configure-windows/server-memory-server-configuration-options.md) - Change the number of worker accounts that can be started by [!INCLUDE[rsql_launchpad](../../includes/rsql-launchpad-md.md)] [Modify the User Account Pool for SQL Server R Services](../../advanced-analytics/r-services/modify-the-user-account-pool-for-sql-server-r-services.md) If you are using Standard Edition and do not have Resource Governor, you can use DMVs and extended events, as well as Windows event monitoring, to help you manage server resources. For more information, see [Monitoring and Managing R Services](../../advanced-analytics/r-services/managing-and-monitoring-r-solutions.md). ## See Also [Using Python in T-SQL](../tutorials/run-python-using-t-sql.md) [Create a Python Model using revoscapepy](../tutorials/use-python-revoscalepy-to-create-model.md)