Merge pull request #3128 from MicrosoftDocs/master

10/23 AM Publish

Author: Taojunshen

connected-environment/docfx.json

@@ -40,11 +40,12 @@
       "feedback_product_url": "https://aka.ms/vsce-product-survey",
       "breadcrumb_path": "/visualstudio/connected-environment/breadcrumb/toc.json",
       "extendBreadcrumb": true,
-      "ROBOTS":  "NOINDEX,NOFOLLOW",
+      "ROBOTS": "NOINDEX,NOFOLLOW",
       "titleSuffix": "Visual Studio Connected Environment"
     },
     "fileMetadata": {},
     "template": [],
-    "dest": "connected-environment"
+    "dest": "connected-environment",
+    "markdownEngineName": "markdig"
   }
 }

docs/ai/create-project-existing.md

@@ -6,28 +6,27 @@ ms.technology: vs-ai-tools
 Once you've [installed Visual Studio Tools for AI](installation.md), it's easy to bring existing Python code into a Visual Studio project.
 
 > [!Important]
->
 > The process described here does not move or copy the original source files. If you want to work with a copy, duplicate the folder first.
 
 1. Launch Visual Studio and select **File > New > Project**.
 
-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**.
+2. 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.
+3. 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.
+   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 the Experimentation account, Workspace, which compute contexts to use, and more.
 
-1. To set a startup file, locate the file in Solution Explorer, right-click, and select **Set as Startup File**.
+4. To set a startup file, locate the file in **Solution Explorer**, right-click, and select **Set as Startup File**.
 
-1. If desired, run the program by pressing Ctrl+F5 or selecting **Debug > Start Without Debugging**.
+5. Run the program by pressing **Ctrl**+**F5** or selecting **Debug > Start Without Debugging**.
 
 > [!div class="nextstepaction"]
 > [Tutorial: Working with Python in Visual Studio](../python/tutorial-working-with-python-in-visual-studio-step-00-installation.md)
 
-## See Also
+## See also
 
 - [Manually identify an existing Python environment](../python/managing-python-environments-in-visual-studio.md#manually-identify-an-existing-environment)

docs/ai/create-project-gallery.md

@@ -27,34 +27,34 @@ Once you've [installed Visual Studio Tools for AI](installation.md), it's easy t
 
     ![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.
+2. 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**.
+3. 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:
+4. For this Quickstart, select the "**MNIST using TensorFlow**" sample and click **Install**. Provide the following:
 
- - **Resource Group**: Azure resource group where your metadata will be stored
- - **Account**: Azure Machine Learning experimentation Account
- - **Workspace**: Azure Machine Learning workspace
- - **Project Type**: The machine learning framework. In this case choose **TensorFlow**
- - **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**
+   - **Resource Group**: Azure resource group where your metadata will be stored
+   - **Account**: Azure Machine Learning experimentation Account
+   - **Workspace**: Azure Machine Learning workspace
+   - **Project Type**: The machine learning framework. In this case choose **TensorFlow**
+   - **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)
+   ![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.
+5. 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.
+6. Submit the job to Azure Machine Learning.
 
     ![mnist](media/create-project-gallery/submit-azml.png)
 
-1. Run in a Docker container or on your local machine
+7. Run in a Docker container or on your local machine
 
     ![mnist](media/create-project-gallery/azml-local.png)

docs/ai/create-project-repo.md

@@ -30,19 +30,19 @@ Once you've [installed Visual Studio Tools for AI](installation.md), you can eas
 
 8. In Solution Explorer, expand the `TensorFlow Examples> MNIST` node, right-click `convolutional.py`, and select **Set as Startup File**. This step tells Visual Studio which file it should use when running the project.
 
-10. Press Ctrl+F5 or select **Debug > Start Without Debugging** to run the program. If you see an `, re-check the working directory setting in the previous step.
+9. Press **Ctrl**+**F5** or select **Debug > Start Without Debugging** to run the program. If you see an `, re-check the working directory setting in the previous step.
 
-
-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
+10. 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)
 
-> 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).
+   > [!NOTE]
+   > If you're 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
+12. 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)

docs/ai/create-project.md

@@ -7,24 +7,24 @@ Once you've [installed Visual Studio Tools for AI](installation.md), it's easy t
 
 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.
+2. 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**.
+3. 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.
+4. 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).
+5. 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**
+6. 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)
 
-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.
+7. 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.
+8. Run the program by pressing Ctrl+F5 or selecting **Debug > Start Without Debugging** on the menu. The results are displayed in a console window.

docs/ai/job-details.md

@@ -2,12 +2,13 @@
 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. Double-click **Jobs**.
-1. You will see the list of jobs submitted to that compute context.
-1. Select a specific **Job** in the list to view details.
+2. Double-click **Jobs**.
+3. You will see the list of jobs submitted to that compute context.
+4. Select a specific **Job** in the list to view details.
 
 ![monitor jobs](media/job-details/monitor-jobs.png)
 

docs/ai/manage-storage.md

@@ -6,15 +6,17 @@ ms.technology: vs-ai-tools
 You can browse all storage on the remote machine or Azure file share to enable uploading data or downloading models and logs. Or, if you want to access logs and job outputs for a specific job, you can do that as well in the job browser.
 
 ## To access all data on the remote machine or file share
+
 1. Open the **Server Explorer**.
 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)

docs/ai/monitor-gpu.md

@@ -2,13 +2,14 @@
 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**.
+3. Click **Show Heat Map**.
 
     ![gpu heatmap](media/monitor-gpu/heatmap.png)

docs/ai/monitor-tensorboard.md

@@ -9,6 +9,6 @@ You can visualize your model training progress with TensorBoard.
 
     ![run tensorboard](media/monitor-tensorboard/run-tensorboard.png)
 
-1. Notice the error decreasing over time, which means the quality is improving.
+2. Notice the error decreasing over time, which means the quality is improving.
 
     ![run tensorboard](media/monitor-tensorboard/tensorboard.png)

docs/ai/tensorflow-local.md

@@ -13,10 +13,10 @@ ms.technology: vs-ai-tools
 ms.workload:
   - "multiple"
 ---
-
 # Train a TensorFlow model locally
 
 In this quickstart, we will train a TensorFlow model with the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset locally in Visual Studio Tools for AI.
+
 The MNIST database has a training set of 60,000 examples, and a test set of 10,000 examples of handwritten digits.
 
 ## Prerequisites
@@ -25,7 +25,8 @@ Before you begin, ensure you have the following installed:
 
 ### Google TensorFlow
 
-Run the following command in a terminal.
+Run the following command in a terminal:
+
 ```cmd
 C:\>pip.exe install tensorflow
 ```
@@ -42,17 +43,17 @@ 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)

docs/ai/tensorflow-vm.md

@@ -13,7 +13,6 @@ ms.technology: vs-ai-tools
 ms.workload:
   - "multiple"
 ---
-
 # Train a TensorFlow model in the cloud
 
 In this tutorial, we will train a TensorFlow model using the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) on an Azure [Deep Learning](https://docs.microsoft.com/azure/machine-learning/data-science-virtual-machine/deep-learning-dsvm-overview) virtual machine.
@@ -46,9 +45,9 @@ 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
 

docs/ai/train-model.md

@@ -13,7 +13,6 @@ ms.technology: vs-ai-tools
 ms.workload:
   - "azure"
 ---
-
 # Train AI models in Azure Batch AI
 
 Batch AI is a managed service that enables data scientists and AI researchers to train AI and other machine learning models on clusters of Azure virtual machines, including VMs with GPU support. You describe the requirements of your job, where to find the inputs and store the outputs, and Batch AI handles the rest. [Learn more about Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview)
@@ -24,7 +23,6 @@ It's integrated with Visual Studio Tools for AI so you can dynamically scale out
 
     ![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)
@@ -40,20 +38,19 @@ 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)
-
-5. Open **samples-for-ai\TensorFlowExamples\TensorFlowExamples.sln** in the directory you cloned the repository
+    ![Sample gallery](media/train-model/open-solution.png)
 
-	![Sample gallery](media/train-model/tensorflowexamples.png)
+6. Open **samples-for-ai\TensorFlowExamples\TensorFlowExamples.sln** in the directory you cloned the repository
 
-5. Set MNIST project as the **Startup Project **
+    ![Sample gallery](media/train-model/tensorflowexamples.png)
 
-	![Sample gallery](media/train-model/mnist-startup.png)
+7. Set MNIST project as the **Startup Project **
 
-1. **Right-click **MNIST project, **Submit Job**
+    ![Sample gallery](media/train-model/mnist-startup.png)
 
-	![Sample gallery](media/train-model/submit-job.png)
+8. <strong>Right-click **MNIST project, **Submit Job</strong>
 
-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-job.png)
+9. 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)