fix errors that cause build to fail under markdig

gewarren · gewarren · commit eb2acbd5c8b4 · 2018-10-18T15:45:55.000-07:00
diff --git a/docs/ai/about-ai-tools.md b/docs/ai/about-ai-tools.md
@@ -23,19 +23,19 @@ Get started with deep learning using [Microsoft Cognitive Toolkit (CNTK)](http:/
 ## 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)
+![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.
 
-![sample explorer](media\about\gallery.png)
+![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
 
-![submit job](media\about\submitjobs.png)
+![submit job](media/about/submitjobs.png)
 
 [Learn more about training models in the cloud](tensorflow-vm.md)
 
diff --git a/docs/ai/create-project-existing.md b/docs/ai/create-project-existing.md
@@ -13,11 +13,11 @@ Once you've [installed Visual Studio Tools for AI](installation.md), it's easy t
 
 1. In the **New Project** dialog, search for "**AI Tools**", select the "**From Existing Python code**" template, give the project a name and location, and select **OK**.
 
-    ![New Project from Existing Code, step 1](media\create-project-existing\new-ai-project.png)
+    ![New Project from Existing Code, step 1](media/create-project-existing/new-ai-project.png)
 
 1. In the wizard that appears, set the path to your existing code, set a filter for file types, and specify any search paths that your project requires, then select **OK**. If you don't know what search paths are, leave that field blank.
 
-![New Project from Existing Code, step 2](media\create-project-existing\azurebatch-newproject.png)
+![New Project from Existing Code, step 2](media/create-project-existing/azurebatch-newproject.png)
 
 > If your existing code is part of an Azure Machine Learning project, check the "**Is Azure Machine Learning folder**" to ensure successful conversion of important Azure Machine Learning configuration details like which Experimentation account, which Workspace, the compute contexts to use and more.
 
diff --git a/docs/ai/create-project-gallery.md b/docs/ai/create-project-gallery.md
@@ -25,15 +25,15 @@ Once you've [installed Visual Studio Tools for AI](installation.md), it's easy t
 
 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)
+    ![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)
+    ![login](media/create-project-gallery/azureml-login.png)
 
 2. Select **AI Tools > Azure Machine Learning Sample Gallery**.
 
-    ![Sample gallery](media\create-project-gallery\gallery.png)
+    ![Sample gallery](media/create-project-gallery/gallery.png)
 
 1. For this Quickstart, select the "**MNIST using TensorFlow**" sample and click **Install**. Provide the following:
 
@@ -49,12 +49,12 @@ Once you've [installed Visual Studio Tools for AI](installation.md), it's easy t
 
 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)
+    ![mnist](media/create-project-gallery/azml-mnist.png)
 
 1. Submit the job to Azure Machine Learning.
 
-    ![mnist](media\create-project-gallery\submit-azml.png)
+    ![mnist](media/create-project-gallery/submit-azml.png)
 
 1. Run in a Docker container or on your local machine
 
-    ![mnist](media\create-project-gallery\azml-local.png)
+    ![mnist](media/create-project-gallery/azml-local.png)
diff --git a/docs/ai/create-project-repo.md b/docs/ai/create-project-repo.md
@@ -7,13 +7,13 @@ Once you've [installed Visual Studio Tools for AI](installation.md), you can eas
 
 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)
+    ![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 Azure DevOps, or clone a repository.
 
-    ![Team explorer window showing Azure DevOps, GitHub, and cloning a repository](media\create-project-repo\team-explorer.png)
+    ![Team explorer window showing Azure DevOps, GitHub, and cloning a repository](media/create-project-repo/team-explorer.png)
 
 4. In the URL field under **Local Git Repositories**, enter `https://github.com/Microsoft/samples-for-ai`, enter a folder for the cloned files, and select **Clone**.
 
@@ -22,7 +22,7 @@ Once you've [installed Visual Studio Tools for AI](installation.md), you can eas
 
 5. When cloning is complete, double-click the repository folder at the bottom of Team Explorer to navigate to the repository dashboard. Under **Solutions**, select **New...**.
 
-    ![Team explorer window, creating a new project from a clone](media\create-project-repo\team-explorer-new-project.png)
+    ![Team explorer window, creating a new project from a clone](media/create-project-repo/team-explorer-new-project.png)
 
 6. In the **New Project** dialog that appears, select "**From Existing Python Code**", specify a name for the project, set **Location** to the same folder as the repository, and select **OK**. In the wizard that appears, select **Finish**.
 
@@ -35,14 +35,14 @@ Once you've [installed Visual Studio Tools for AI](installation.md), you can eas
 
 11. When the program runs successfully, you'll see it start to download your training and test dataset, then train the model and output your error rate. You want error rate to decrease over time
 
-    ![First output from the Python MNIST program](media\create-project-repo\tensorflow-mnist-running.png)
+    ![First output from the Python MNIST program](media/create-project-repo/tensorflow-mnist-running.png)
 
 > If you are using Anaconda and get an error about missing numpy, you may need to [change your Python environment to use Anaconda](../python/selecting-a-python-environment-for-a-project.md).
 
 11. You can visualize the progress with TensorBoard. Right click your project and click **Run TensorBoard** then select the directory of your output TensorBoard logs.
 
-    ![run tensorboard](media\create-project-repo\run-tensorboard.png)
+    ![run tensorboard](media/create-project-repo/run-tensorboard.png)
 
 11. Notice the error decreasing overtime, which means the quality is improving
 
-    ![run tensorboard](media\create-project-repo\tensorboard.png)
+    ![run tensorboard](media/create-project-repo/tensorboard.png)
diff --git a/docs/ai/create-project.md b/docs/ai/create-project.md
@@ -9,22 +9,22 @@ Once you've [installed Visual Studio Tools for AI](installation.md), it's easy t
 
 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)
+    ![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. 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)
+    ![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. 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)
+    ![Properties](media/create-project/project-properties.png)
 
 1. Click the **Debug** tab to see the Script Arguments automatically added. you may change them as needed to where your input data is located and where you would like your output stored.
 
-	![Properties](media\create-project\/project-properties_1.png)
+	![Properties](media/create-project//project-properties_1.png)
 
 1. Run the program by pressing Ctrl+F5 or selecting **Debug > Start Without Debugging** on the menu. The results are displayed in a console window.
diff --git a/docs/ai/installation.md b/docs/ai/installation.md
@@ -25,7 +25,7 @@ You can download the tools from the [Visual Studio Marketplace](http://aka.ms/vs
 
 1. Select **Tools** > **Extensions and Updates**.
 
-   ![Extensions and Updates menu in Visual Studio](media\installation\extensions.png)
+   ![Extensions and Updates menu in Visual Studio](media/installation/extensions.png)
 
 2. In the **Extensions and Updates** dialog box, select **Online** on the left-hand side.
 3. In the search box in the upper right-hand corner, type or enter "tools for ai".
@@ -53,7 +53,7 @@ To install CUDA
 - Make sure to install the CUDA runtime libraries, and then add CUDA binary path to the %PATH% or $Path environment variable.
 - On Windows, this path is "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin" by default.
 
-![Install CUDA on Windows](media\installation\install_cuda_win.png)
+![Install CUDA on Windows](media/installation/install_cuda_win.png)
 
 ### cuDNN
 
@@ -79,7 +79,7 @@ Python has been the primary programming language for deep learning applications.
 
 Deep learning frameworks rely on pip for their own installation.
 
-![Install Python on Windows](media\installation\install_python_win.png)
+![Install Python on Windows](media/installation/install_python_win.png)
 
 Then, we need to verify whether Python 3.5 is installed correctly, and upgrade pip to the latest version by executing the following commands in a terminal:
 
diff --git a/docs/ai/job-details.md b/docs/ai/job-details.md
@@ -9,6 +9,6 @@ Once the jobs are submitted, you can view the list of jobs to see their status,
 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)
+![monitor jobs](media/job-details/monitor-jobs.png)
 
 > Job history submitted to Linux VMs is stored on the VM in the /tmp directory. Therefore, whenever it is rebooted the job history is cleared. For a permanent record of your job history, please configure your VM as a compute context in Azure Machine learning, then Submit Job to Azure Machine Learning (selecting your VM as the compute context).
diff --git a/docs/ai/manage-storage.md b/docs/ai/manage-storage.md
@@ -10,11 +10,11 @@ You can browse all storage on the remote machine or Azure file share to enable u
 2. Expand the remote machine or Batch AI compute context.
 3. Right-click **Storage**; then, click **Browse**.
 
-	![storage](media\manage-storage\browse-storage.png)
+	![storage](media/manage-storage/browse-storage.png)
 
 ## 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.
 
-	![storage](media\manage-storage\job-workingfolder.png)
+	![storage](media/manage-storage/job-workingfolder.png)
diff --git a/docs/ai/monitor-gpu.md b/docs/ai/monitor-gpu.md
@@ -7,8 +7,8 @@ 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)
+    ![gpu heatmap](media/monitor-gpu/gpu-heatmap-0.png)
 
 2. Click **Show Heat Map**.
 
-    ![gpu heatmap](media\monitor-gpu\heatmap.png)
+    ![gpu heatmap](media/monitor-gpu/heatmap.png)
diff --git a/docs/ai/monitor-tensorboard.md b/docs/ai/monitor-tensorboard.md
@@ -7,8 +7,8 @@ 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.
 
-    ![run tensorboard](media\monitor-tensorboard\run-tensorboard.png)
+    ![run tensorboard](media/monitor-tensorboard/run-tensorboard.png)
 
 1. Notice the error decreasing over time, which means the quality is improving.
 
-    ![run tensorboard](media\monitor-tensorboard\tensorboard.png)
+    ![run tensorboard](media/monitor-tensorboard/tensorboard.png)
diff --git a/docs/ai/tensorflow-local.md b/docs/ai/tensorflow-local.md
@@ -42,17 +42,17 @@ Download this [GitHub repository](https://github.com/Microsoft/samples-for-ai) c
 
 - Select the **Tensorflow Examples** folder from the samples repository downloaded and open the **TensorflowExamples.sln** file.
 
-![Open project](media\tensorflow-local\open-project.png)
+![Open project](media/tensorflow-local/open-project.png)
 
-![Open solution](media\tensorflow-local\open-solution.png)
+![Open solution](media/tensorflow-local/open-solution.png)
 
 - Find the MNIST project in **Solution Explorer**, right-click and select **Set as StartUp Project**.
 
 - Click **Start**.
 
 - The output is printed in the console.
 
-![Sample output from console](media\tensorflow-local\console-output.png)
+![Sample output from console](media/tensorflow-local/console-output.png)
 
 > [!div class="nextstepaction"]
 > [Train a TensorFlow model in the cloud](tensorflow-vm.md)
diff --git a/docs/ai/tensorflow-vm.md b/docs/ai/tensorflow-vm.md
@@ -46,20 +46,20 @@ Download this [GitHub repository](https://github.com/Microsoft/samples-for-ai) c
 
 - Select the **Tensorflow Examples** folder from the samples repository downloaded and open the **TensorflowExamples.sln** file.
 
-![Open project](media\tensorflow-local\open-project.png)
+![Open project](media/tensorflow-local/open-project.png)
 
-![Open solution](media\tensorflow-local\open-solution.png)
+![Open solution](media/tensorflow-local/open-solution.png)
 
 ## Add Azure Remote VM
 
 In Server Explorer, right click the **Remote Machines** node under the AI Tools node and select "Add…". Enter the Remote Machine display name, IP host, SSH port, user name, and password/key file.
 
-![Add a new remote machine](media\tensorflow-vm\add-remote-vm.png)
+![Add a new remote machine](media/tensorflow-vm/add-remote-vm.png)
 
 ## Submit job to Azure VM
 Right-click on MNIST project in **Solution Explorer** and select **Submit Job**.
 
-![Job submission to a remote machine](media\tensorflow-vm\job-submission.png)
+![Job submission to a remote machine](media/tensorflow-vm/job-submission.png)
 
 In the submission window:
 
@@ -72,7 +72,7 @@ In the submission window:
 ## Check status of job
 To see status and details of jobs: expand the virtual machine you submitted the job to in the **Server Explorer**. Double-click on **Jobs**.
 
-![Job browser](media\tensorflow-vm\job-browser.png)
+![Job browser](media/tensorflow-vm/job-browser.png)
 
 ## Clean up resources
 
diff --git a/docs/ai/train-model.md b/docs/ai/train-model.md
@@ -22,16 +22,16 @@ It's integrated with Visual Studio Tools for AI so you can dynamically scale out
 
 1. Launch Visual Studio. Open the **Server Explorer** by opening the **AI Tools** menu and choosing **Select Cluster**
 
-    ![Cluster chooser](media\train-model\select-cluster.png)
+    ![Cluster chooser](media/train-model/select-cluster.png)
 
 
 2. Expand **AI Tools**. Any Batch AI resources you have will be auto-detected and appear in the Server Explorer.
 
-    ![Sample gallery](media\train-model\batchai.png)
+    ![Sample gallery](media/train-model/batchai.png)
 
 3. Select **View > Team Explorer...** to open the **Team Explorer** window in which you can connect to GitHub or Azure DevOps, or clone a repository.
 
-    ![Team explorer window showing Azure DevOps, GitHub, and cloning a repository](media\train-model\team-explorer.png)
+    ![Team explorer window showing Azure DevOps, GitHub, and cloning a repository](media/train-model/team-explorer.png)
 
 4. In the URL field under **Local Git Repositories**, enter `https://github.com/Microsoft/samples-for-ai`, enter a folder for the cloned files, and select **Clone**.
 
@@ -40,20 +40,20 @@ It's integrated with Visual Studio Tools for AI so you can dynamically scale out
 
 5. When cloning is complete, click **File > Open Solution > Project / Solution**
 
-	![Sample gallery](media\train-model\open-solution.png)
+	![Sample gallery](media/train-model/open-solution.png)
 
 5. Open **samples-for-ai\TensorFlowExamples\TensorFlowExamples.sln** in the directory you cloned the repository
 
-	![Sample gallery](media\train-model\tensorflowexamples.png)
+	![Sample gallery](media/train-model/tensorflowexamples.png)
 
 5. Set MNIST project as the **Startup Project **
 
-	![Sample gallery](media\train-model\mnist-startup.png)
+	![Sample gallery](media/train-model/mnist-startup.png)
 
 1. **Right-click **MNIST project, **Submit Job**
 
-	![Sample gallery](media\train-model\submit-job.png)
+	![Sample gallery](media/train-model/submit-job.png)
 
 1. Select your **Azure Batch AI** cluster, then click **Import**. Select the `AzureBatchAI_TF_MNIST.json` file to quickly populate some default values like which Docker Image to use. Then click **Submit**
 
-	![Sample gallery](media\train-model\submit-batch.png)
+	![Sample gallery](media/train-model/submit-batch.png)
diff --git a/docs/deployment/how-to-disable-url-activation-of-clickonce-applications.md b/docs/deployment/how-to-disable-url-activation-of-clickonce-applications.md
diff --git a/docs/ide/reference/signing-page-project-designer.md b/docs/ide/reference/signing-page-project-designer.md
diff --git a/docs/ide/visual-studio-2017-for-dotnet-developers.md b/docs/ide/visual-studio-2017-for-dotnet-developers.md
diff --git a/docs/install/using-visual-studio-vm.md b/docs/install/using-visual-studio-vm.md
diff --git a/docs/vs-2015/extensibility/visual-studio-sdk-reference.md b/docs/vs-2015/extensibility/visual-studio-sdk-reference.md
