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SQLML: Merge release-mlserver-endofsupport to master (#19677)
* SQLML: Removing ML/R Server references (1) * Fixing link * Remaining files * Fixed link * fix spelling * MLServer: Fixed support end date in include file Co-authored-by: Beth Harvey <v-bharve@microsoft.com> Co-authored-by: Alma Jenks (BEYONDSOFT CONSULTING INC) <v-alje@microsoft.com> Co-authored-by: PRMerger8 <prmrgr8@microsoft.com>
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‎docs/includes/machine-learning-server-retirement.md‎

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---
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author: garyericson
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ms.author: garye
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ms.prod: mlserver
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ms.topic: include
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ms.date: 06/30/2021
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---
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> [!IMPORTANT]
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> The support for Machine Learning Server (previously known as R Server) will end on July 1, 2022. For more information, see [What's happening to Machine Learning Server?](/machine-learning-server/what-is-happening-to-machine-learning-server.md)

‎docs/linux/sql-server-linux-setup-machine-learning.md‎

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sudo su
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# Set the location of the package repo at the "prod" directory containing the distribution
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# This example is for SLES12, the only supported version of SLES in Machine Learning Server
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# This example is for SLES12
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zypper ar -f https://packages.microsoft.com/sles/12/prod packages-microsoft-com
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# Update packages on your system (optional)

‎docs/machine-learning/concepts/extensibility-framework.md‎

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**BxlServer** is an executable provided by Microsoft that manages communication between SQL Server and the language runtime. It creates the Windows job objects for Windows, or the namespaces for Linux, that are used to contain external script sessions. It also provisions secure working folders for each external script job and uses SQL Satellite to manage data transfer between the external runtime and SQL Server. If you run [Process Explorer](/sysinternals/downloads/process-explorer) while a job is running, you might see one or multiple instances of BxlServer.
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In effect, BxlServer is a companion to a language runtime environment that works with SQL Server to transfer data and manage tasks. BXL stands for Binary Exchange language and refers to the data format used to move data efficiently between SQL Server and external processes. BxlServer is also an important part of related products such as Microsoft R Client and Microsoft R Server.
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In effect, BxlServer is a companion to a language runtime environment that works with SQL Server to transfer data and manage tasks. BXL stands for Binary Exchange language and refers to the data format used to move data efficiently between SQL Server and external processes.
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**SQL Satellite** is an extensibility API, included in the database engine, that supports external code or external runtimes implemented using C or C++.
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‎docs/machine-learning/install/sql-machine-learning-standalone-windows-install.md‎

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# Install Machine Learning Server (Standalone) or R Server (Standalone) using SQL Server Setup
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[!INCLUDE [SQL Server 2016 and later](../../includes/applies-to-version/sqlserver2016.md)]
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[!INCLUDE [ML Server retirement banner](~/includes/machine-learning-server-retirement.md)]
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::: moniker range=">=sql-server-2017"
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SQL Server Setup includes a **shared feature** option for installing a standalone machine learning server that runs outside of SQL Server. It's called **Machine Learning Server (Standalone)** and includes Python and R.
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::: moniker-end
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::: moniker range="=sql-server-2016"
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SQL Server Setup includes a **shared feature** option for installing a standalone machine learning server that runs outside of SQL Server. In SQL Server 2016, this feature is called **R Server (Standalone)**.
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::: moniker-end
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A standalone server as installed by SQL Server Setup is functionally equivalent to the non-SQL-branded versions of [Microsoft Machine Learning Server](/machine-learning-server/what-is-machine-learning-server), supporting the same use cases and scenarios, including:
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A standalone server as installed by SQL Server Setup supports use cases and scenarios such as the following:
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+ Remote execution, switching between local and remote sessions in the same console
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+ Operationalization with web nodes and compute nodes

‎docs/machine-learning/install/sql-ml-cab-downloads.md‎

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>
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> When installing SQL Server 2016 SP1 CU4 or SP1 CU5 offline, download SRO_3.2.2.16000_1033.cab. If you downloaded SRO_3.2.2.13000_1033.cab from FWLINK 831785 as indicated in the setup dialog box, rename the file as SRO_3.2.2.16000_1033.cab before installing the Cumulative Update.
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If you would like to view the source code for Microsoft R, it is available for download as an archive in .tar format: [Download R Server installers](/machine-learning-server/install/r-server-install-windows#download)
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::: moniker-end
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## Next steps

‎docs/machine-learning/install/upgrade-r-and-python.md‎

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# Upgrade Python and R runtime with binding in SQL Server Machine Learning Services
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[!INCLUDE [SQL Server 2016 and 2017](../../includes/applies-to-version/sqlserver2016-2017-only.md)]
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[!INCLUDE [retirement banner](~/includes/machine-learning-server-retirement.md)]
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This article describes how to use am installation process called **binding** to upgrade the R or Python runtimes in [SQL Server 2016 R Services](../r/sql-server-r-services.md) or [SQL Server 2017 Machine Learning Services](../sql-server-machine-learning-services.md). You can get [newer versions of Python and R](#version-map) by *binding* to [Microsoft Machine Learning Server](/machine-learning-server).
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> [!IMPORTANT]

‎docs/machine-learning/package-management/install-r-packages-with-revoscaler.md‎

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## Client connections
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[!INCLUDE [ML Server retirement banner](~/includes/machine-learning-server-retirement.md)]
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A client workstation can be [Microsoft R Client](/machine-learning-server/r-client/install-on-windows) or a [Microsoft Machine Learning Server](/machine-learning-server/install/machine-learning-server-windows-install) (data scientists often use the free developer edition) on the same network.
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When calling package management functions from a remote R client, you must create a compute context object first, using the [RxInSqlServer](/machine-learning-server/r-reference/revoscaler/rxinsqlserver) function. Thereafter, for each package management function that you use, pass the compute context as an argument.

‎docs/machine-learning/package-management/package-install-uninstall-and-sync.md‎

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## Requirements
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Before you can use package synchronization, you must have the appropriate version of Microsoft R or Machine Learning Server. This feature is provided in Microsoft R version 9.1.0 or later.
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Before you can use package synchronization, you must have the appropriate version of Microsoft R. This feature is provided in Microsoft R version 9.1.0 or later.
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You must also enable the [package management feature](r-package-how-to-enable-or-disable.md) on the server.
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‎docs/machine-learning/package-management/python-package-information.md‎

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### Component upgrades
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By default, Python packages are refreshed through service packs and cumulative updates. Additional packages and full version upgrades of core Python components are possible only through product upgrades or by binding Python support to Microsoft Machine Learning Server.
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By default, Python packages are refreshed through service packs and cumulative updates. Additional packages and full version upgrades of core Python components are possible only through product upgrades.
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For more information, see [Upgrade R and Python components in SQL Server](../install/upgrade-r-and-python.md).
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‎docs/machine-learning/package-management/r-package-how-to-enable-or-disable.md‎

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# Enable or disable remote package management for SQL Server
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[!INCLUDE [SQL Server 2016 and 2017 only](../../includes/applies-to-version/sqlserver2016-2017-only.md)]
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[!INCLUDE [ML Server retirement banner](~/includes/machine-learning-server-retirement.md)]
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This article describes how to enable remote management of R packages from a client workstation or a different Machine Learning Server. After the package management feature has been enabled on SQL Server, you can use RevoScaleR commands on a client to install packages on SQL Server.
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By default, the external package management feature for SQL Server is disabled. You must run a separate script to enable the feature as described in the next section.

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