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‎docs/linux/sql-server-linux-machine-learning-services-container.md‎

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---
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title: Run SQL Server Machine Learning Services in a Container | Microsoft Docs
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description: This tutorial show how to use SQL Server Machine Learning Services in a Linux container running on Docker.
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author: uc-msft
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ms.author: umajay
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manager: craigg
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ms.date: 06/26/2019
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ms.topic: conceptual
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ms.prod: sql
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ms.technology: linux
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ms.collection: linux-container
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moniker: ">= sql-server-linux-ver15 || =sqlallproducts-allversions"
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---
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# Run SQL Server Machine Learning Services in a Container
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[!INCLUDE[appliesto-ss-xxxx-xxxx-xxx-md-linuxonly](../includes/appliesto-ss-xxxx-xxxx-xxx-md-linuxonly.md)]
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This tutorial demonstrates how to build a Docker container with SQL Server Machine Learning Services & run Machine Learning Scripts from Transact-SQL.
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> [!div class="checklist"]
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> * Clone the mssql-docker repository.
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> * Build SQL Server Linux container image with Machine Learning Services.
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> * Run SQL Server Linux container image with Machine Learning Services.
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> * Run R or Python scripts using Transact-SQL statements.
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> * Stop and remove the SQL Server Linux container.
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## Pre-requisites
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* Git command-line interface.
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* Docker Engine 1.8+ on any supported Linux distribution or Docker for Mac/Windows. For more information, see [Install Docker](https://docs.docker.com/engine/installation/).
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* Minimum of 2 GB of disk space
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* Minimum of 2 GB of RAM
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* [System requirements for SQL Server on Linux](sql-server-linux-setup.md#system).
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## Clone the mssql-docker repository
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1. Open a bash terminal on Linux/Mac or WSL terminal on Windows.
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1. Create a local directory to hold a copy of the mssql-docker repository locally.
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1. Run the git clone command to clone the mssql-docker repository.
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```bash
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git clone https://github.com/microsoft/mssql-docker mssql-docker
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```
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## Build SQL Server Linux container image with Machine Learning Services
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1. Change directory to the mssql-mlservices directory.
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```bash
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cd mssql-docker/linux/preview/examples/mssql-mlservices
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```
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1. Execute build.sh script.
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```bash
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./build.sh
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```
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> [!NOTE]
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> Building the docker image requires installing packages that are several GBs in size. The script may take up to 20 minutes to complete depending on network bandwidth.
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## Run SQL Server Linux container image with Machine Learning Services
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1. Set environment variables before running the container. Set PATH_TO_MSSQL environment variable to a host directory.
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```bash
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export MSSQL_PID='Developer'
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export ACCEPT_EULA='Y'
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export ACCEPT_EULA_ML='Y'
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export PATH_TO_MSSQL='/home/mssql/'
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```
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1. Execute run.sh script.
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```bash
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./run.sh
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```
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This command creates a SQL Server container with Machine Learning Services with the Developer edition (default). SQL Server port **1433** is exposed on the host as port **1401**.
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> [!NOTE]
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> The process for running production SQL Server editions in containers is slightly different. For more information, see [Run production container images](sql-server-linux-configure-docker.md#production). If you use the same container names and ports, the rest of this walk-through still works with production containers.
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1. To view your Docker containers, use the `docker ps` command.
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```bash
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sudo docker ps -a
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```
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1. If the **STATUS** column shows a status of **Up**, then 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).
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```bash
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$ sudo docker ps -a
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```
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Output:
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```
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CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
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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
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```
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## Change the SA password
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[!INCLUDE [Change docker password](../includes/sql-server-linux-change-docker-password.md)]
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## Execute R / Python scripts from Transact-SQL
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1. Connect to SQL Server in the container and enable the external script configuration option by running the following T-SQL statement.
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```sql
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EXEC sp_configure 'external scripts enabled', 1
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RECONFIGURE WITH OVERRIDE
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go
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```
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1. Verify Machine Learning Services is working by running the following simple R/Python sp_execute_external_script.
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```sql
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execute sp_execute_external_script
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@language = N'R',
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@script = N'
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print("Hello World!")
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print(R.version)
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print(Revo.version)
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OutputDataSet <- InputDataSet',
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@input_data_1 = N'select 1'
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with result sets((i int));
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go
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```
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```sql
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execute sp_execute_external_script
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@language = N'Python',
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@script = N'
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import sys
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print(sys.version)
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print("Hello World!")
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OutputDataSet = InputDataSet',
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@input_data_1 = N'select 1'
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with result sets((i int));
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go
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```
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## Next steps
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In this tutorial, you learned to do the following:
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> [!div class="checklist"]
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> * Clone the mssql-docker repository.
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> * Build SQL Server Linux container image with Machine Learning Services.
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> * Run SQL Server Linux container image with Machine Learning Services.
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> * Run R or Python scripts using Transact-SQL statements.
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> * Stop and remove the SQL Server Linux container.
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Next, review other Docker configuration and troubleshooting scenarios:
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> [!div class="nextstepaction"]
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>[Configuration guide for SQL Server on Docker](sql-server-linux-configure-docker.md)

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