Merge pull request MicrosoftDocs#5748 from MicrosoftDocs/master

5/2 AM Publish

Author: v-alje

docs/advanced-analytics/install/sql-machine-learning-standalone-windows-install.md

@@ -14,7 +14,7 @@ manager: cgronlun
 
 SQL Server setup includes the option to install a machine learning server that runs outside of SQL Server. This option might be useful if you need to develop high-performance machine learning solutions that can use remote compute contexts, switching interchangeably between the local server and a remote Machine Learning Server on a Spark cluster or on another SQL Server instance.
 
-This article describes how to use SQL Server setup to install the standalone version of **SQL Server 2017 Machine Learning Server**. If you have an Enterprise Edition or Software Assurance, installing the standalone machine learning server is free.
+This article describes how to use SQL Server setup to install the standalone version of **SQL Server 2017 Machine Learning Server**. 
 
 ## <a name="bkmk_prereqs"> </a> Pre-install checklist
 

docs/advanced-analytics/install/sql-ml-component-install-without-internet-access.md

@@ -3,7 +3,7 @@ title: Install SQL Server machine learning components without internet access |
 ms.prod: sql
 ms.technology: machine-learning
 
-ms.date: 04/15/2018
+ms.date: 05/02/2018
 ms.topic: conceptual
 author: HeidiSteen
 ms.author: heidist
@@ -57,6 +57,16 @@ Microsoft R Open     |no change; use previous|
 Microsoft R Server      |[SRS_9.2.0.400_1033.cab](https://go.microsoft.com/fwlink/?LinkId=866212&clcid=1033)|
 Microsoft Python Open     |no change; use previous|
 Microsoft Python Server    |[SPS_9.2.0.400_1033.cab](https://go.microsoft.com/fwlink/?LinkId=866213&clcid=1033)|
+**SQL Server 2017 CU5** |
+Microsoft R Open     |no change; use previous|
+Microsoft R Server      |[SRS_9.2.0.500_1033.cab](https://go.microsoft.com/fwlink/?LinkId=869052&clcid=1033)|
+Microsoft Python Open     |no change; use previous|
+Microsoft Python Server    |[SPS_9.2.0.500_1033.cab](https://go.microsoft.com/fwlink/?LinkId=869053&clcid=1033)|
+**SQL Server 2017 CU6** |
+Microsoft R Open     |no change; use previous|
+Microsoft R Server      |[SRS_9.2.0.600_1033.cab](https://go.microsoft.com/fwlink/?LinkId=871074&clcid=1033)|
+Microsoft Python Open     |no change; use previous|
+Microsoft Python Server    |[SPS_9.2.0.600_1033.cab](https://go.microsoft.com/fwlink/?LinkId=871073&clcid=1033)|
 
 ### <a name="bkmk_2016Installers"></a>Downloads for SQL Server 2016
 

docs/advanced-analytics/install/sql-r-standalone-windows-install.md

@@ -12,7 +12,7 @@ manager: cgronlun
 # Install SQL Server 2016 R Server (Standalone)
 [!INCLUDE[appliesto-ss-xxxx-xxxx-xxx-md-winonly](../../includes/appliesto-ss-xxxx-xxxx-xxx-md-winonly.md)]
 
-This article describes how to use SQL Server 2016 setup to install the standalone version of **SQL Server 2016 R Server**. If you have an Enterprise Edition or Software Assurance, installing the standalone R Server on a production server is free.
+This article describes how to use SQL Server 2016 setup to install the standalone version of **SQL Server 2016 R Server**.
 
 ## <a name="bkmk_prereqs"> </a> Pre-install checklist
 

docs/advanced-analytics/what-is-sql-server-machine-learning.md

@@ -19,7 +19,7 @@ The key value proposition of Machine Learning Services is the power of its propr
 There are two options for using machine learning capabilities in SQL Server: 
 
 + [**SQL Server Machine Learning Services (In-Database)**](r/sql-server-r-services.md) operates within the database engine instance, where the calculation engine is fully integrated with the database engine. Most installations are this option.
-+ [**SQL Server Machine Learning Server (Standalone)**](r/r-server-standalone.md) is a non-SQL installation. Although you use SQL Server Setup to install the server, it is completely decoupled from SQL Server. Functionally, it is equivalent to the non-SQL [Microsoft Machine Learning Server for Windows](https://docs.microsoft.com/machine-learning-server/install/machine-learning-server-windows-install).
++ [**SQL Server Machine Learning Server (Standalone)**](r/r-server-standalone.md) is Machine Learning Server for Windows that runs independently of the database engine. Although you use SQL Server Setup to install the server, the feature is not instance-aware. Functionally, it is equivalent to the non-SQL-Server [Microsoft Machine Learning Server for Windows](https://docs.microsoft.com/machine-learning-server/install/machine-learning-server-windows-install).
 
 ## R and Python packages
 

docs/advanced-analytics/what-s-new-in-sql-server-machine-learning-services.md

@@ -3,7 +3,7 @@ title: "What's new in SQL Server Machine Learning Services | Microsoft Docs"
 ms.prod: sql
 ms.technology: machine-learning
 
-ms.date: 04/15/2018  
+ms.date: 05/02/2018  
 ms.topic: conceptual
 author: HeidiSteen
 ms.author: heidist
@@ -21,7 +21,9 @@ This release added Python support and industry-leading machine learning algorith
 This release also introduced **SQL Server Machine Learning Server (Standalone)**, fully independent of SQL Server, for R and Python workloads that you want to run on a dedicated system. With the standalone server, you can distribute and scale R or Python solutions without using SQL Server.
 
 | Release | Feature update |
-|---------|---------------|
+|---------|----------------|
+| CU 6 | Bug fixes and package refresh, but no new feature announcements. |
+| CU 5 | Bug fixes and package refresh, but no new feature announcements. Fixes include improvements to transform functions and variables in revoscalepy, correcting the long path-related errors in rxInstallPackages, fixing connections in a loopback for RxExec and rx_exec functions, and revisions to warning messages. |
 | CU 4 | Bug fixes and package refresh, but no new feature announcements. |
 | CU 3 | Python model serialization in revoscalepy, using the [rx_serialize_model function](https://docs.microsoft.com/machine-learning-server/python-reference/revoscalepy/rx-serialize-model).<br/><br/>[Native scoring](sql-native-scoring.md) plus enhancements to [Realtime scoring](real-time-scoring.md). With in-database scoring, throughput is a million rows per second using R models. In this update, realtime scoring and native scoring offer better performance in single-row and batch scoring. Native scoring uses a T-SQL function for fast scoring that can be run on any edition of SQL Server, even on Linux. The function requires no installation of R or extra configuration. This means you can train a model elsewhere, save it in SQL Server, and then perform scoring without ever calling R. For more information on scoring methodologies, see [How to perform realtime scoring or native scoring](r/how-to-do-realtime-scoring.md). |
 | CU 2 | Bug fixes and package refresh, but no new feature announcements. |

docs/linux/quickstart-install-connect-docker.md

@@ -53,13 +53,13 @@ This image consists of SQL Server running on Linux based on Ubuntu 16.04. It can
 
    ```bash
    sudo docker run -e 'ACCEPT_EULA=Y' -e 'MSSQL_SA_PASSWORD=<YourStrong!Passw0rd>' \
-      -p 1401:1433 --name sql1 \
+      -p 1433:1433 --name sql1 \
       -d microsoft/mssql-server-linux:2017-latest
    ```
 
    ```PowerShell
    docker run -e "ACCEPT_EULA=Y" -e "MSSQL_SA_PASSWORD=<YourStrong!Passw0rd>" `
-      -p 1401:1433 --name sql1 `
+      -p 1433:1433 --name sql1 `
       -d microsoft/mssql-server-linux:2017-latest
    ```
 
@@ -75,7 +75,7 @@ This image consists of SQL Server running on Linux based on Ubuntu 16.04. It can
    |-----|-----|
    | **-e 'ACCEPT_EULA=Y'** |  Set the **ACCEPT_EULA** variable to any value to confirm your acceptance of the [End-User Licensing Agreement](http://go.microsoft.com/fwlink/?LinkId=746388). Required setting for the SQL Server image. |
    | **-e 'MSSQL_SA_PASSWORD=\<YourStrong!Passw0rd\>'** | Specify your own strong password that is at least 8 characters and meets the [SQL Server password requirements](../relational-databases/security/password-policy.md). Required setting for the SQL Server image. |
-   | **-p 1401:1433** | Map a TCP port on the host environment (first value) with a TCP port in the container (second value). In this example, SQL Server is listening on TCP 1433 in the container and this is exposed to the port, 1401, on the host. |
+   | **-p 1433:1433** | Map a TCP port on the host environment (first value) with a TCP port in the container (second value). In this example, SQL Server is listening on TCP 1433 in the container and this is exposed to the port, 1433, on the host. |
    | **--name sql1** | Specify a custom name for the container rather than a randomly generated one. If you run more than one container, you cannot reuse this same name. |
    | **microsoft/mssql-server-linux:2017-latest** | The SQL Server 2017 Linux container image. |
 
@@ -223,14 +223,14 @@ The following steps use **sqlcmd** outside of your container to connect to SQL S
 
 1. Find the IP address for the machine that hosts your container. On Linux, use **ifconfig** or **ip addr**. On Windows, use **ipconfig**.
 
-1. Run sqlcmd specifying the IP address and the port mapped to port 1433 in your container. In this example, that is port 1401 on the host machine.
+1. Run sqlcmd specifying the IP address and the port mapped to port 1433 in your container. In this example, that is the same port, 1433, on the host machine. If you specified a different mapped port on the host machine, you would use it here.
 
    ```bash
-   sqlcmd -S 10.3.2.4,1401 -U SA -P '<YourNewStrong!Passw0rd>'
+   sqlcmd -S 10.3.2.4,1433 -U SA -P '<YourNewStrong!Passw0rd>'
    ```
 
    ```PowerShell
-   sqlcmd -S 10.3.2.4,1401 -U SA -P "<YourNewStrong!Passw0rd>"
+   sqlcmd -S 10.3.2.4,1433 -U SA -P "<YourNewStrong!Passw0rd>"
    ```
 
 1. Run Transact-SQL commands. When finished, type `QUIT`.

docs/linux/sql-server-linux-availability-group-cluster-rhel.md

@@ -138,7 +138,7 @@ sudo pcs property set cluster-recheck-interval=2min
 To update the property value to `true` run:
 
 ```bash
-sudo pcs property set start-failure-is-fatal = true
+sudo pcs property set start-failure-is-fatal=true
 ```
 
 To update the `ag1` resource property `failure-timeout` to `60s` run:

docs/linux/sql-server-linux-availability-group-cluster-sles.md

@@ -194,21 +194,21 @@ crm configure property cluster-recheck-interval=2min
 ```
 
 > [!IMPORTANT] 
-> All distributions that use the latest available Pacemaker package 1.1.18-11.el7 introduce a behavior change for the start-failure-is-fatal cluster setting when its value is false. This change affects the failover workflow. If a primary replica experiences an outage, the cluster is expected to failover to one of the available secondary replicas. Instead, users will notice that the cluster keeps trying to start the failed primary replica. If that primary never comes online (because of a permanent outage), the cluster never fails over to another available secondary replica. Because of this change, a previously recommended configuration to set start-failure-is-fatal is no longer valid and the setting needs to be reverted back to its default value of `true`. 
+> If you already have an availability group resource managed by a Pacemaker cluster, note that all distributions that use the latest available Pacemaker package 1.1.18-11.el7 introduce a behavior change for the start-failure-is-fatal cluster setting when its value is false. This change affects the failover workflow. If a primary replica experiences an outage, the cluster is expected to failover to one of the available secondary replicas. Instead, users will notice that the cluster keeps trying to start the failed primary replica. If that primary never comes online (because of a permanent outage), the cluster never fails over to another available secondary replica. Because of this change, a previously recommended configuration to set start-failure-is-fatal is no longer valid and the setting needs to be reverted back to its default value of `true`. 
 > Additionally, the AG resource needs to be updated to include the `failover-timeout` property. 
-
-To update the property value to `true` run:
-
-```bash
-crm configure property start-failure-is-fatal=true
-```
-
-To update the `ag1` resource property `failure-timeout` to `60s` run:
-
-```bash
-crm configure edit ag1
-# In the text editor, add `meta failure-timeout=60s` after any `param`s and before any `op`s
-```
+>
+>To update the property value to `true` run:
+>
+>```bash
+>crm configure property start-failure-is-fatal=true
+>```
+>
+>Update your existing AG resource property `failure-timeout` to `60s` run (replace `ag1` with the name of your availability group resource): 
+>
+>```bash
+>crm configure edit ag1
+># In the text editor, add `meta failure-timeout=60s` after any `param`s and before any `op`s
+>```
 
 For more information on Pacemaker cluster properties, see [Configuring Cluster Resources](https://www.suse.com/documentation/sle_ha/book_sleha/data/sec_ha_config_crm_resources.html).
 

docs/linux/sql-server-linux-availability-group-cluster-ubuntu.md

@@ -157,20 +157,20 @@ sudo pcs property set cluster-recheck-interval=2min
 ```
 
 > [!IMPORTANT] 
-> All distributions that use the latest available Pacemaker package 1.1.18-11.el7 introduce a behavior change for the start-failure-is-fatal cluster setting when its value is false. This change affects the failover workflow. If a primary replica experiences an outage, the cluster is expected to failover to one of the available secondary replicas. Instead, users will notice that the cluster keeps trying to start the failed primary replica. If that primary never comes online (because of a permanent outage), the cluster never fails over to another available secondary replica. Because of this change, a previously recommended configuration to set start-failure-is-fatal is no longer valid and the setting needs to be reverted back to its default value of `true`. 
+> If you already have an availability group resource managed by a Pacemaker cluster, note that all distributions that use the latest available Pacemaker package 1.1.18-11.el7 introduce a behavior change for the start-failure-is-fatal cluster setting when its value is false. This change affects the failover workflow. If a primary replica experiences an outage, the cluster is expected to failover to one of the available secondary replicas. Instead, users will notice that the cluster keeps trying to start the failed primary replica. If that primary never comes online (because of a permanent outage), the cluster never fails over to another available secondary replica. Because of this change, a previously recommended configuration to set start-failure-is-fatal is no longer valid and the setting needs to be reverted back to its default value of `true`. 
 > Additionally, the AG resource needs to be updated to include the `failover-timeout` property. 
-
-To update the property value to `true` run:
-
-```bash
-sudo pcs property set start-failure-is-fatal = true
-```
-
-To update the `ag1` resource property `failure-timeout` to `60s` run:
-
-```bash
-pcs resource update ag1 meta failure-timeout=60s
-```
+>
+>To update the property value to `true` run:
+>
+>```bash
+>sudo pcs property set start-failure-is-fatal=true
+>```
+>
+>Update your existing AG resource property `failure-timeout` to `60s` run (replace `ag1` with the name of your availability group resource):
+>
+>```bash
+>pcs resource update ag1 meta failure-timeout=60s
+>```
 
 ## Install SQL Server resource agent for integration with Pacemaker