---
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

[!INCLUDE[appliesto-ss-xxxx-xxxx-xxx-md-linuxonly](../includes/appliesto-ss-xxxx-xxxx-xxx-md-linuxonly.md)]

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](https://github.com/Microsoft/mssql-docker/tree/master/linux/preview/examples/mssql-mlservices).

## Prerequisites

- Git command-line interface.

- Docker Engine 1.8+ on any supported Linux distribution, or Docker for Mac/Windows. For more information, see [Get Docker](https://docs.docker.com/get-docker/).

- See also the [system requirements for SQL Server on Linux](sql-server-linux-setup.md#system).

## 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:

    ```bash
    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:
    
    ```bash
    /mssql-docker/linux/preview/examples/mssql-mlservices
    ```

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

    ```bash
    docker build -t mssql-server-mlservices .
    ```

3. Run the command:

    ```bash
    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 <password> with your actual password. Volume mounting using -v is optional. Replace <directory on the host OS> with an actual directory where you want to mount the database data and log files.
    

4. Confirm by running the following command:

    ```bash
    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:

   ```bash
   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](sql-server-linux-configure-docker.md). 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:

   ```bash
   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](sql-server-linux-configure-docker.md#troubleshooting).

 
    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:

```sql
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:

+ [Python tutorial: Predict ski rental with linear regression in SQL Server Machine Learning Services](../machine-learning/tutorials/python-ski-rental-linear-regression-deploy-model.md)
+ [Python tutorial: Categorizing customers using k-means clustering with SQL Server Machine Learning Services](../machine-learning/tutorials/python-clustering-model.md)

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:

+ [Quickstart: Run R in T-SQL](../machine-learning/tutorials/quickstart-r-create-script.md)
+ [Tutorial: In-database analytics for R developers](../machine-learning/tutorials/sqldev-in-database-r-for-sql-developers.md)