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Copy file name to clipboardExpand all lines: docs/advanced-analytics/administration/resource-governor.md
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@@ -3,7 +3,7 @@ title: Manage Python and R workloads with Resource Governor
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description: Learn how to use Resource Governor to manage CPU, physical IO, and memory resources allocation for Python and R workloads in SQL Server Machine Learning Services.
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ms.prod: sql
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ms.technology: machine-learning
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ms.date: 10/01/2019
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ms.date: 10/02/2019
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ms.topic: conceptual
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author: dphansen
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ms.author: davidph
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By default, the external script runtimes for machine learning are limited to no more than 20% of total machine memory. It depends on your system, but in general, you might find this limit inadequate for serious machine learning tasks such as training a model or predicting on many rows of data.
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## Use Resource Governor to control resourcing
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## Manage resources with Resource Governor
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By default, external processes use up to 20% of total host memory on the local server. You can modify the default resource pool to make server-wide changes, with R and Python processes utilizing whatever capacity you make available to external processes.
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Alternatively, you can construct custom *external resource pools*, with associated workload groups and classifiers, to determine resource allocation for requests originating from specific programs, hosts, or other criteria that you provide. An external resource pool is a type of resource pool introduced in [!INCLUDE[sssql15-md](../../includes/sssql15-md.md)] to help manage the R and Python processes external to the database engine.
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Alternatively, you can create custom **external resource pools**, with associated workload groups and classifiers, to determine resource allocation for requests originating from specific programs, hosts, or other criteria that you provide. An external resource pool is a type of resource pool introduced in [!INCLUDE[sssql15-md](../../includes/sssql15-md.md)] to help manage the R and Python processes external to the database engine.
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1.[Enable resource governance](https://docs.microsoft.com/sql/relational-databases/resource-governor/enable-resource-governor) (it is off by default).
Copy file name to clipboardExpand all lines: docs/database-engine/configure-windows/hybrid-buffer-pool.md
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@@ -89,6 +89,8 @@ SELECT name, is_memory_optimized_enabled FROM sys.databases;
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When formatting your PMEM device on Windows, use the largest allocation unit size available for NTFS (2 MB in Windows Server 2019) and ensure the device has been formatted for DAX (Direct Access).
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For optimal performance, enable [Locked Pages in Memory](./enable-the-lock-pages-in-memory-option-windows.md) on Windows.
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Files sizes should be a multiple of 2 MB (modulo 2 MB should equal zero).
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If the server scoped setting for Hybrid buffer pool is set to disabled, Hybrid buffer pool will not be used by any user database.
Copy file name to clipboardExpand all lines: docs/relational-databases/polybase/polybase-troubleshoot-connectivity.md
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@@ -19,7 +19,7 @@ You can use interactive diagnostics that have been built into PolyBase to help t
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This article serves as a guide to walk through the debugging process of such issues by leveraging these built-in diagnostics.
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> [!TIP}
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> [!TIP]
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> Instead of following the steps in this guide, you can choose to run the [HDFS Kerberos Tester](https://github.com/microsoft/sql-server-samples/tree/master/samples/manage/hdfs-kerberos-tester) to troubleshoot HDFS Kerberos connections for PolyBase, when you experience HDFS Kerberos failure while creating an external table in a Kerberos secured HDFS cluster.
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> This tool will assist in ruling out non-SQL Server issues, to help you concentrate on resolving HDFS Kerberos setup issues, namely identifying issues with username/password misconfigurations, and cluster Kerberos setup misconfigurations.
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> This tool is completely independent from [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)]. It is available as a Jupyter Notebook and requires Azure Data Studio.
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