Merge branch 'master' into kraigb-pythonfeedback

Author: Lisaco88

.openpublishing.publish.config.json

@@ -38,6 +38,7 @@
       "build_source_folder": "docs",
       "build_output_subfolder": "vsdocsdocs",
       "locale": "en-us",
+      "xref_query_tags": ["/dotnet"],
       "monikers": [],
       "open_to_public_contributors": true,
       "type_mapping": {

.openpublishing.redirection.json

@@ -67,7 +67,7 @@
             "redirect_url": "https://vspartner.com/pages/vsshells",
             "redirect_document_id": false
         },
-        {   
+        {
             "source_path": "docs/extensibility/shell/distributing-isolated-shell-applications.md",
             "redirect_url": "https://vspartner.com/pages/vsshells",
             "redirect_document_id": false
@@ -1037,6 +1037,21 @@
             "redirect_url": "/visualstudio/ide/reference/call-hierarchy",
             "redirect_document_id": false
         },
+        {
+            "source_path": "docs/ide/reference/installvstemplates-devenv-exe.md",
+            "redirect_url": "/visualstudio/ide/reference/devenv-command-line-switches",
+            "redirect_document_id": false
+        },
+        {
+            "source_path": "docs/ide/reference/resetaddin-devenv-exe.md",
+            "redirect_url": "/visualstudio/ide/reference/devenv-command-line-switches",
+            "redirect_document_id": false
+        },
+        {
+            "source_path": "docs/ide/reference/updateconfiguration-devenv-exe.md",
+            "redirect_url": "/visualstudio/ide/reference/devenv-command-line-switches",
+            "redirect_document_id": false
+        },
         {
             "source_path": "docs/ide/reference/visual-studio-for-applications-reference.md",
             "redirect_url": "/visualstudio/ide/reference/visual-studio-reference",
@@ -1827,6 +1842,11 @@
             "redirect_url": "/visualstudio/test/unit-test-your-code",
             "redirect_document_id": false
         },
+        {
+            "source_path": "docs/test/lab-management/upgrade-scvmm-2008-r2-scvmm-2012.md",
+            "redirect_url": "/visualstudio/test/lab-management/using-a-lab-environment-for-your-application-lifecycle",
+            "redirect_document_id": false
+        },
         {
             "source_path": "docs/test/using-microsoft-visualstudio-testtools-cppunittestframework.md",
             "redirect_url": "/visualstudio/test/microsoft.visualstudio.testtools.cppunittestframework-api-reference",
@@ -1837,6 +1857,11 @@
             "redirect_url": "/visualstudio/test/writing-unit-tests-for-c-cpp",
             "redirect_document_id": false
         },
+        {
+            "source_path": "docs/test/writing-unit-tests-for-the-dotnet-framework-with-the-microsoft-unit-test-framework-for-managed-code.md",
+            "redirect_url": "/visualstudio/test/unit-test-your-code",
+            "redirect_document_id": false
+        },
         {
             "source_path": "docs/vb-ide/code-generation-vb.md",
             "redirect_url": "/visualstudio/ide/code-generation-in-visual-studio",

docs/TOC.md

@@ -34,7 +34,7 @@
 ### Manage installations
 #### [Tools for detecting and managing Visual Studio instances](install/tools-for-managing-visual-studio-instances.md)
 ### Manage subscriptions
-#### [Visual Studio subscriptions: Administrator overview](/visualstudio/subscriptions/admin-responsibilities) 
+#### [Visual Studio subscriptions: Administrator overview](/visualstudio/subscriptions/admin-responsibilities)
 ### [Help Viewer Administrator Guide](ide/help-viewer-administrator-guide.md)
 #### [Command-Line Arguments for the Help Content Manager](ide/command-line-arguments-for-the-help-content-manager.md)
 #### [Help Content Manager Overrides](ide/help-content-manager-overrides.md)
@@ -158,7 +158,7 @@
 ###### [Filtered Completion Lists in Visual Basic](ide/filtered-completion-lists-in-visual-basic.md)
 ##### [C# IntelliSense](ide/visual-csharp-intellisense.md)
 ##### [JavaScript IntelliSense](ide/javascript-intellisense.md)
-##### [Visual C++ Intellisense](ide/visual-cpp-intellisense.md)
+##### [Visual C++ IntelliSense](ide/visual-cpp-intellisense.md)
 #### [Navigate Your Code](ide/navigating-code.md)
 ##### [Find references in your code](ide/finding-references.md)
 ##### [View Type and Member Definitions](ide/go-to-and-peek-definition.md)
@@ -390,15 +390,12 @@
 ### [-Project (devenv.exe)](ide/reference/project-devenv-exe.md)
 ### [-ProjectConfig (devenv.exe)](ide/reference/projectconfig-devenv-exe.md)
 ### [-Rebuild (devenv.exe)](ide/reference/rebuild-devenv-exe.md)
-### [-ResetAddin (devenv.exe)](ide/reference/resetaddin-devenv-exe.md)
-### [-InstallVSTemplates (devenv.exe)](ide/reference/installvstemplates-devenv-exe.md)
 ### [-ResetSettings (devenv.exe)](ide/reference/resetsettings-devenv-exe.md)
 ### [-ResetSkipPkgs (devenv.exe)](ide/reference/resetskippkgs-devenv-exe.md)
 ### [-Run (devenv.exe)](ide/reference/run-devenv-exe.md)
 ### [-Runexit (devenv.exe)](ide/reference/runexit-devenv-exe.md)
 ### [-SafeMode (devenv.exe)](ide/reference/safemode-devenv-exe.md)
 ### [-Setup (devenv.exe)](ide/reference/setup-devenv-exe.md)
-### [-Updateconfiguration (devenv.exe)](ide/reference/updateconfiguration-devenv-exe.md)
 ### [-Upgrade (devenv.exe)](ide/reference/upgrade-devenv-exe.md)
 ### [-UseEnv (devenv.exe)](ide/reference/useenv-devenv-exe.md)
 ## [Visual Studio Commands](ide/reference/visual-studio-commands.md)

docs/ai/about-ai-tools.md

@@ -9,34 +9,35 @@ ms.date: 11/13/2017
 ms.topic: article
 ms.devlang: multiple
 ms.service: multiple
-ms.workload: 
+ms.technology: vs-ai-tools
+ms.workload:
   - "multiple"
 ---
 
 # Visual Studio Tools for AI
 
 Visual Studio Tools for AI is an extension to build, test, and deploy Deep Learning / AI solutions. It seamlessly integrates with Azure Machine Learning for robust experimentation capabilities, including but not limited to submitting data preparation and model training jobs transparently to different compute targets. Additionally, it provides support for custom metrics and run history tracking, enabling data science reproducibility and auditing. Enterprise ready collaboration, allow to securely work on project with other people.
 
-Get started with deep learning using [Microsoft Cognitive Toolkit (CNTK)](http://www.microsoft.com/en-us/cognitive-toolkit), [Google TensorFlow](https://www.tensorflow.org), or other deep-learning frameworks today.  
- 
-## Develop, debug and deploy deep learning models and AI solutions  
-Use the productivity features of Visual Studio to accelerate AI innovation today. Use built-in code editor features like syntax highlighting, IntelliSense and text auto formatting. You can interactively test your deep learning application in your local environment using step-through debugging on local variables and models. 
+Get started with deep learning using [Microsoft Cognitive Toolkit (CNTK)](http://www.microsoft.com/en-us/cognitive-toolkit), [Google TensorFlow](https://www.tensorflow.org), or other deep-learning frameworks today.
+
+## Develop, debug and deploy deep learning models and AI solutions
+Use the productivity features of Visual Studio to accelerate AI innovation today. Use built-in code editor features like syntax highlighting, IntelliSense and text auto formatting. You can interactively test your deep learning application in your local environment using step-through debugging on local variables and models.
 
 ![deep learning ide](media\about\ide.png)
 
-## Get started quickly with the Azure Machine Learning Sample Gallery  
-Visual Studio Tools for AI is integrated with Azure Machine Learning to make it easy to browse through a gallery of sample experiments using CNTK, TensorFlow, MMLSpark and more. 
+## Get started quickly with the Azure Machine Learning Sample Gallery
+Visual Studio Tools for AI is integrated with Azure Machine Learning to make it easy to browse through a gallery of sample experiments using CNTK, TensorFlow, MMLSpark and more.
 
 ![sample explorer](media\about\gallery.png)
 
 [Learn more about creating projects from the sample gallery](create-project-gallery.md)
 
 ## Scale out deep learning model training and/or inferencing to the cloud
-This extension makes it easy to train models on your local computer or you can submit jobs to the cloud by using our integration with Azure Machine Learning. You can submit jobs to different compute targets like Spark clusters, Azure GPU virtual machines and more  
- 
+This extension makes it easy to train models on your local computer or you can submit jobs to the cloud by using our integration with Azure Machine Learning. You can submit jobs to different compute targets like Spark clusters, Azure GPU virtual machines and more
+
 ![submit job](media\about\submitjobs.png)
 
-[Learn more about training models in the cloud](tensorflow-vm.md) 
+[Learn more about training models in the cloud](tensorflow-vm.md)
 
 ## Supported Operating Systems
 Currently this extension supports Windows 64-bit operating systems

docs/ai/create-project-existing.md

@@ -1,4 +1,5 @@
 ---
+ms.technology: vs-ai-tools
 ---
 # Create an AI project from existing code
 

docs/ai/create-project-gallery.md

@@ -9,29 +9,30 @@ ms.date: 11/13/2017
 ms.topic: how to article
 ms.devlang: multiple
 ms.service: multiple
-ms.workload: 
+ms.technology: vs-ai-tools
+ms.workload:
   - "multiple"
 ---
 
 ## Create an AI project from the Azure Machine Learning Gallery in Visual Studio
 
-Azure Machine Learning is integrated with Visual Studio Tools for AI. You can use it to submit machine learning jobs to remote compute targets like Azure virtual machines, Spark clusters, and more. Learn more about [Azure Machine Learning Experimentation](https://docs.microsoft.com/azure/machine-learning/preview/experimentation-service-configuration) 
+Azure Machine Learning is integrated with Visual Studio Tools for AI. You can use it to submit machine learning jobs to remote compute targets like Azure virtual machines, Spark clusters, and more. Learn more about [Azure Machine Learning Experimentation](https://docs.microsoft.com/azure/machine-learning/preview/experimentation-service-configuration)
 
 Once you've [installed Visual Studio Tools for AI](installation.md), it's easy to create a new Python project using pre-made recipes in the Azure Machine Learning Sample Gallery.
 
-> [!NOTE] 
-> Azure Machine Learning Workbench must be installed. To install it please see the [Azure Machine Learning installation quickstart](https://docs.microsoft.com/azure/machine-learning/preview/quickstart-installation) 
+> [!NOTE]
+> Azure Machine Learning Workbench must be installed. To install it please see the [Azure Machine Learning installation quickstart](https://docs.microsoft.com/azure/machine-learning/preview/quickstart-installation)
 
-1. Launch Visual Studio. Open the **Server Explorer** by opening the **AI Tools** menu and choosing **Select Cluster**	
+1. Launch Visual Studio. Open the **Server Explorer** by opening the **AI Tools** menu and choosing **Select Cluster**
 
     ![Cluster chooser](media\create-project-gallery\select-cluster.png)
 
 1. Sign in to your Azure Machine Learning subscription by right-clicking the **Azure Machine Learning** node in the Server Explorer then select **Login** and follow the directions.
 
     ![login](media\create-project-gallery\azureml-login.png)
- 
-2. Select **AI Tools > Azure Machine Learning Sample Gallery**. 
-	
+
+2. Select **AI Tools > Azure Machine Learning Sample Gallery**.
+
     ![Sample gallery](media\create-project-gallery\gallery.png)
 
 1. For this Quickstart, select the "**MNIST using TensorFlow**" sample and click **Install**. Provide the following:
@@ -43,14 +44,14 @@ Once you've [installed Visual Studio Tools for AI](installation.md), it's easy t
  - **Add to Solution**: determines whether to add to your current Visual Studio Solution or a create and open a new solution
  - **Project Path**: Location to save the code
  - **Project Name**: Type **TensorFlowMNIST**
-   
+
 ![Resulting project when using the Python Application template](media/create-project-gallery/new-AzureSampleProject.png)
 
 1. Visual Studio creates the project file (a `.pyproj` file on disk) along with other files defined in the sample. With the "MNIST" template, the project contains several files.
 
     ![mnist](media\create-project-gallery\azml-mnist.png)
 
-1. Submit the job to Azure Machine Learning. 
+1. Submit the job to Azure Machine Learning.
 
     ![mnist](media\create-project-gallery\submit-azml.png)
 

docs/ai/create-project-repo.md

@@ -1,13 +1,14 @@
 ---
+ms.technology: vs-ai-tools
 ---
 # Clone a repository of Python code in Visual Studio
 
 Once you've [installed Visual Studio Tools for AI](installation.md), you can easily clone a repository of Python code and create a project from it.
 
 1. To connect to GitHub repositories, run the Visual Studio installer, select **Modify**, and select the **Individual components** tab. Scroll down to the **Code tools** section, select **GitHub extension for Visual Studio**, and select **Modify**.
-    
+
     ![Selecting the GitHub extension in the Visual Studio installer](media\create-project-repo\installation-github-extension.png)
-    
+
 2. Launch Visual Studio.
 
 3. Select **View > Team Explorer...** to open the **Team Explorer** window in which you can connect to GitHub or Visual Studio Team Services, or clone a repository.

docs/ai/create-project.md

@@ -1,24 +1,25 @@
 ---
+ms.technology: vs-ai-tools
 ---
 # Create an AI project from a template in Visual Studio
 
 Once you've [installed Visual Studio Tools for AI](installation.md), it's easy to create a new AI project using a variety of templates.
 
 1. Launch Visual Studio.
 
-1. Select **File > New > Project** (Ctrl+Shift+N). In the **New Project** dialog, search for "**AI Tools**", and select the template you want. Note that selecting a template displays a short description of what the template provides. 
+1. Select **File > New > Project** (Ctrl+Shift+N). In the **New Project** dialog, search for "**AI Tools**", and select the template you want. Note that selecting a template displays a short description of what the template provides.
 
     ![VS2017 New Project dialog with Python template](media\create-project\new-ai-project.png)
 
-1. For this Quickstart, select the "**TensorFlow Application**" template, give the project a name (such as "MNIST") and location, and select **OK**. 
+1. For this Quickstart, select the "**TensorFlow Application**" template, give the project a name (such as "MNIST") and location, and select **OK**.
 
 1. Visual Studio creates the project file (a `.pyproj` file on disk) along with any other files as described by the template. With the "TensorFlow Application" template, the project contains one file named the same as your project. The file is open in the Visual Studio editor by default.
 
     ![Resulting project when using the Python Application template](media\create-project\new-tensorflowapp.png)
 
-1. Notice the code already imports several libraries including TensorFlow, numpy, sys and os. Additionally it starts your application ready with some input arguments to easily enable switching the location of input training data, output models and log files. These params are useful when you submit your jobs to multiple compute contexts (ie different directory on your local dev box than on an Azure File Share). 
+1. Notice the code already imports several libraries including TensorFlow, numpy, sys and os. Additionally it starts your application ready with some input arguments to easily enable switching the location of input training data, output models and log files. These params are useful when you submit your jobs to multiple compute contexts (ie different directory on your local dev box than on an Azure File Share).
 
-1. Your project also has some properties created to make it easy to debug your app by automatically passing commandline arguments to these input parameters. **Right click** your project then select **Properties** 
+1. Your project also has some properties created to make it easy to debug your app by automatically passing commandline arguments to these input parameters. **Right click** your project then select **Properties**
 
     ![Properties](media\create-project\project-properties.png)
 

docs/ai/installation.md

@@ -9,7 +9,8 @@ ms.date: 11/13/2017
 ms.topic: article
 ms.devlang: multiple
 ms.service: multiple
-ms.workload: 
+ms.technology: vs-ai-tools
+ms.workload:
   - "multiple"
 ---
 # Installation
@@ -18,11 +19,11 @@ Visual Studio Tools for AI can be installed on Windows 64-bit operating systems.
 
 ## Installing Visual Studio Tools for AI
 
-This extension works with [Visual Studio](https://docs.microsoft.com/visualstudio/) 2015, 2017, Community edition or higher. 
+This extension works with [Visual Studio](https://docs.microsoft.com/visualstudio/) 2015, 2017, Community edition or higher.
 
-To install, download from the [Visual Studio MarketPlace](http://aka.ms/vstoolsforai) or from within Visual Studio 
+To install, download from the [Visual Studio MarketPlace](http://aka.ms/vstoolsforai) or from within Visual Studio
 
-1. **Tools**> **Extensions and Updates** 
+1. **Tools**> **Extensions and Updates**
 
 ![install CUDA on Windows](media\installation\extensions.png)
 

docs/ai/job-details.md

@@ -1,11 +1,12 @@
 ---
+ms.technology: vs-ai-tools
 ---
 # View recent job performance and details
 Once the jobs are submitted, you can view the list of jobs to see their status, duration and more.
 
-1. In the **Server Explorer** expand the specific compute context 
+1. In the **Server Explorer** expand the specific compute context
 1. Double-click **Jobs**
-1. You will see the list of jobs submitted to that compute context. 
+1. You will see the list of jobs submitted to that compute context.
 1. Select a specific **Job** in the list to view details
 
 ![monitor jobs](media\job-details\monitor-jobs.png)

docs/ai/manage-storage.md

@@ -1,4 +1,5 @@
 ---
+ms.technology: vs-ai-tools
 ---
 # Browse storage to upload data or download models and logs
 
@@ -14,6 +15,6 @@ You can browse all storage on the remote machine or Azure file share to enable u
 ## To access job specific data on the remote machine or file share
 1. Open the [Job History](job-details.md)
 2. Select the job
-3. Click **Working Folder** or click StdOut / Stderr for quick access to these important log files 
+3. Click **Working Folder** or click StdOut / Stderr for quick access to these important log files
 
 	![storage](media\manage-storage\job-workingfolder.png)

docs/ai/monitor-gpu.md

@@ -1,14 +1,14 @@
 ---
+ms.technology: vs-ai-tools
 ---
 # Monitoring GPU Utilization
 To monitor GPU utilization of remote Linux machines:
 
 1. In **Server Explorer**, expand **Remote Machines**
 2. **Right click** the remote machine you want to monitor
-    
+
     ![gpu heatmap](media\monitor-gpu\gpu-heatmap-0.png)
 
 2. Click **Show Heat Map**
-    
+
     ![gpu heatmap](media\monitor-gpu\heatmap.png)
-    

docs/ai/monitor-tensorboard.md

@@ -1,8 +1,9 @@
 ---
+ms.technology: vs-ai-tools
 ---
 # Monitor with TensorBoard
 
-You can visualize your model training progress with TensorBoard. 
+You can visualize your model training progress with TensorBoard.
 
 1. Right click your project and click **Run TensorBoard** then select the directory of your output TensorBoard logs.