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title Install on Docker
titleSuffix SQL Server Machine Learning Services
description Learn how to install SQL Server Machine Learning Services (Python and R) on Docker.
author cawrites
ms.author chadam
ms.reviewer davidph
manager cgronlun
ms.date 05/11/2020
ms.topic conceptual
ms.prod sql
ms.technology machine-learning
monikerRange >=sql-server-ver15||>=sql-server-linux-ver15||=sqlallproducts-allversions

Install SQL Server Machine Learning Services (Python and R) on Docker

[!INCLUDEappliesto-ss-xxxx-xxxx-xxx-md-linuxonly]

This article explains how to install SQL Server Machine Learning Services on Docker. You can use Machine Learning Services to execute Python and R scripts in-database. We do not provide pre-built containers with Machine Learning Services. You can create one from the SQL Server containers using an example template available on GitHub.

Prerequisites

Clone the mssql-docker repository

The following command clones the mssql-docker git repository to a local directory.

  1. Open a Bash terminal on Linux or Mac.

  2. Create a directory to hold a local copy of the mssql-docker repository.

  3. Run the git clone command to clone the mssql-docker repository:

    git clone https://github.com/microsoft/mssql-docker mssql-docker

Build a SQL Server Linux container image

Complete the following steps to build the docker image:

  1. Change the directory to the mssql-mlservices directory:

    /mssql-docker/linux/preview/examples/mssql-mlservices

  2. In the same directory, run the following command:

    docker build -t mssql-server-mlservices .

  3. Run the command:

    docker run -d -e MSSQL_PID=Developer -e ACCEPT_EULA=Y -e ACCEPT_EULA_ML=Y -e SA_PASSWORD=<password> -v <directory on the host OS>:/var/opt/mssql -p 1433:1433 mssql-server-mlservices

    [!NOTE] Any of the following values can be used for MSSQL_PID: Developer (free), Express (free), Enteprise (paid), Standard (paid). If you are using a paid edition, please ensure that you have purchased a license. Replace with your actual password. Volume mounting using -v is optional. Replace with an actual directory where you want to mount the database data and log files.

  4. Confirm by running the following command:

    docker ps -a

    [!NOTE] To build the Docker image, you must install packages that are several GBs in size. The script may take some time to finish running, depending on network bandwidth.

Run the SQL Server Linux container image

  1. Set your environment variables before running the container. Set the PATH_TO_MSSQL environment variable to a host directory:

    export MSSQL_PID='Developer'
export ACCEPT_EULA='Y'
export ACCEPT_EULA_ML='Y'
export PATH_TO_MSSQL='/home/mssql/'

    [!NOTE] The process for running production SQL Server editions in containers is slightly different. For more information, see Configure SQL Server container images on Docker. If you use the same container names and ports, the rest of this walkthrough still works with production containers.

  2. To view your Docker containers, run the docker ps command:

    sudo docker ps -a

  3. If the STATUS column shows a status of Up, SQL Server is running in the container and listening on the port specified in the PORTS column. If the STATUS column for your SQL Server container shows Exited, see the Troubleshooting section of the configuration guide.

    Output:

    CONTAINER ID        IMAGE                          COMMAND                  CREATED             STATUS              PORTS                    NAMES
941e1bdf8e1d        mcr.microsoft.com/mssql/server/mssql-server-linux   "/bin/sh -c /opt/m..."   About an hour ago   Up About an hour     0.0.0.0:1401->1433/tcp   sql1

Enable Machine Learning Services

To enable Machine Learning Services, connect to your SQL Server instance and run the following T-SQL statement:

EXEC sp_configure  'external scripts enabled', 1;
RECONFIGURE WITH OVERRIDE

Next steps

Python developers can learn how to use Python with SQL Server by following these tutorials:

R developers can get started with some simple examples, and learn the basics of how R works with SQL Server. For your next step, see the following links: