Set up Python Machine Learning Services (In-Database)

THIS TOPIC APPLIES TO: yesSQL Server (Windows only)noAzure SQL DatabasenoAzure SQL Data WarehousenoParallel Data Warehouse

This article describes how to install the components required for Python by running the SQL Server setup wizard, and following the interactive prompts.

Machine learning options in SQL Server setup

Choose the Machine Learning Services feature, and select Python as the language.

The Shared Features section contains a separate installation option, Machine Learning Server (Standalone). This option supports operationalization of Python code on a server that does not have SQL Server, or that does not require use of SQL Server compute contexts. Thus, we recommend that you do not install this on the same computer as a SQL Server instance. Instead, install Machine Learning Server (Standalone) on a separate computer.

After the installation is complete, reconfigure the instance to allow execution of scripts that use an external executable. You might need to make additional changes to the server to support machine learning workloads. Configuration changes generally require a restart of the instance, or a restart of the Launchpad service.


  • SQL Server 2017 is required. Python integration is not supported on previous versions of SQL Server.
  • Be sure to install the database engine. An instance of SQL Server is required to run Python scripts in-database.
  • Prerequisites are installed as part of the Python component setup.
  • You cannot install machine learning with Python services on a failover cluster. 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. Alternatively, you can install machine learning with Python services on a standalone computer that uses the AlwaysOn setting, 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 that uses Python on the SQL Server computer outside SQL Server can lead to various problems:

    • You use a different library and different executable, and get different results, than you do when you are running in SQL Server.
    • Python scripts running in external libraries cannot be managed by SQL Server, leading to resource contention.


After setup is complete, be sure to complete the additional post-configuration steps described in this article. These steps include enabling SQL Server to use external scripts, and adding accounts required for SQL Server to run Python jobs on your behalf.

Unattended installation

To perform an unattended installation, use the command-line options for SQL Server setup and the arguments specific to Python. For more information, see Unattended installs of SQL Server with Python Machine Learning Services.

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, select New SQL Server stand-alone installation or add features to an existing installation.

    Install Python in-database

  3. On the Feature Selection page, select 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 a named instance.

    • Machine Learning 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. Install only one language per instance.

      Feature options for Python


      Do not select the option for Machine Learning Server (Standalone). The option to install Machine Learning Server under Shared Features is intended for use on a separate computer. For example, you might want to install the same version of the machine learning components on a different computer that is used for project development, such as your data scientist's laptop.

  4. On the Consent to Install Python page, select Accept.

    This license agreement is required to download the Python executable, Python packages from Anaconda.

    Agreement to Python license


    If the computer you are using does not have internet access, you can pause setup at this point to download the installers separately. For more information, see Installing components without internet access.

    Select Accept, wait until the Next button becomes active, and then select Next.

  5. On the Ready to Install page, verify that these selections are included, and select 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

      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 SQL Server Management Studio.


    You can download and install the appropriate version from this page: Download SQL Server Management Studio (SSMS).

    You can also try out the preview release of SQL Operations Studio, which supports administrative tasks and queries against SQL Server.

  2. Connect to the instance where you installed Machine Learning Services, and run the following command:


    The value for the property, external scripts enabled, should be 0 at this point. That is because the feature is turned off by default. 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

    If you have already enabled the feature for the R language, don't run reconfigure a second time for Python. The underlying extensibility platform supports both languages.

  4. Restart the SQL Server service for the SQL Server instance. Restarting the SQL Server service also automatically restarts the related SQL Server Trusted Launchpad service.

    You can restart the service by using the Services panel in Control Panel, or by using 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 the 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, 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, any instance that has either R or Python enabled has its own Launchpad service.

    If you install both R and Python on a single instance, only one Launchpad is installed. A separate, language-specific launcher DLL is added for each language. For more information, see Components to support Python integration.

  3. If Launchpad is running, you should be able to run simple Python scripts like the following in SQL Server Management Studio:

    EXEC sp_execute_external_script  @language =N'Python',
    @script=N'OutputDataSet = InputDataSet',
    @input_data_1 = N'SELECT 1 AS col'




Columns or headings used in the Python script are not returned, by design. To add column names for your output, you must specify the schema for the return data set. Do this by using the WITH RESULTS parameter of the stored procedure, naming the columns and specifying the SQL data type.

For example, you can add the following line to generate an arbitrary column name: WITH RESULT SETS ((Col1 AS int))

Step 4: Additional configuration

If the previous 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 you got an error when running the command, review the following list. You might need to make additional appropriate configurations to the service or database.


Not all the listed changes are required, and none might be required. Requirements depend 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 SQL Server Trusted Launchpad service. When a user sends a Python or R script from an external client, SQL Server activates an available worker account. Then it maps it to the identity of the calling user, and runs the script on behalf of the user.

This is called implied authentication, and is a service of the database engine. This service 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 usually more than enough for running external script jobs.


The worker group is named SQLRUserGroup regardless of whether you installed R or Python. There is a single group for each instance.

If you need to run scripts from a remote data science client, and you are using Windows authentication, there are additional considerations. These worker accounts must be given permission to sign in to the SQL Server instance on your behalf.

  1. In SQL Server Management Studio, in Object Explorer, expand Security. Then right-click Logins, and select New Login.
  2. In the Login - New dialog box, select Search.
  3. Select Object Types, and select Groups. Clear everything else.
  4. In Enter the object name to select, type SQLRUserGroup, and select Check Names.
  5. The name of the local group associated with the instance's Launchpad service should resolve to something like instancename\SQLRUserGroup. Select OK.
  6. By default, the group is assigned to the public role, and has permission to connect to the database engine.
  7. Select OK.


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 SQL Server yourself, and you are running Python scripts in your own instance, you typically execute scripts as an administrator. Thus, you have implicit permission over various operations and all data in the database.

Most users, however, do not have such elevated permissions. For example, users in an organization who use SQL logins to access the database generally do not have elevated permissions. Therefore, for each user who is using Python, you must grant users of Machine Learning Services the permission to run external scripts in each database where Python is used. Here's how:

USE <database_name>


Permissions are not specific to the supported script language. In other words, there are not separate permission levels for R script versus Python script. If you need to maintain separate permissions for these languages, install R and Python on separate instances.

Give your users read, write, or data definition language (DDL) permissions to databases

While a user is running scripts, the user might need to read data from other databases. The user might also need to create new tables to store results, and write data into tables.

For each Windows user account or SQL login that is running R or Python scripts, ensure that it has the appropriate permissions on the specific database: db_datareader, db_datawriter, or db_ddladmin.

For example, the following Transact-SQL 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
EXEC sp_addrolemember 'db_datareader', 'MySQLLogin'

For more information about the permissions included in each role, see Database-level roles.

Ensure that the SQL Server installation 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.

Create an ODBC data source for the instance on your data science client

You might create a machine learning solution on a data science client computer. If you need to run code by using the SQL Server computer as the compute context, you have two options: access the instance by using a SQL login, or by using a Windows account.

  • For SQL logins: Ensure that the login has appropriate permissions on the database where you are reading data. You can do this by adding Connect to and SELECT permissions, or by adding the login to the db_datareader role. To create objects, assign DDL_admin rights. If you must save data to tables, add to the db_datawriter role.

  • For Windows authentication: You might need to create an ODBC data source on the data science client that specifies the instance name and other connection information. For more information, see ODBC data source administrator.

Additional optimizations

Now that you have everything working, you might also want to optimize the server to support machine learning, or install pretrained models.

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 Machine Learning Services.

Optimize the server for script execution

The default settings for SQL Server setup optimize the balance of the server for a variety of services. These services include ETL processes, reporting, auditing, and applications that use SQL Server data.

If you use the default settings, you might find that resources for running external scripts are restricted or throttled, particularly in memory-intensive operations. If machine learning is a priority, change the default database settings to ensure that external script jobs are prioritized and resourced appropriately. These changes can include:

  • Reducing the amount of memory allocated to the SQL Server database engine.
  • Increasing the number of accounts running under the SQL Server Trusted Launchpad service. This does not increase the number of resources, but does increase the number of scripts that can run concurrently.

If you have SQL Server Enterprise Edition, use resource governor to configure an external resource pool for Python. For more information, see the following articles:

If you are using SQL Server Standard Edition and do not have resource governor, you can use dynamic management views and extended events to help you manage server resources. You can also use Windows event monitoring for this purpose. For more information, see Monitoring and managing R Services.

Upgrade the machine learning components

When you install Machine Learning Services by using SQL Server 2017, you get the version of the components at the time the release was published. Each time you patch or upgrade the SQL Server instance, the machine learning components are upgraded as well.

You can upgrade the machine learning components on a faster schedule than is supported by SQL Server releases, by installing Microsoft Machine Learning Server. When you do so, you also get any new features supported in the latest release of Machine Learning Server, such as:

For information about how to upgrade an instance, see Upgrade R components through binding.


See the following tutorials for some examples of how you can use Python with SQL Server to build and deploy machine learning solutions:

Using Python in T-SQL

Create a Python model using revoscalepy