Edits to DSVM articles and TOC entry

kraigb · kraigb · commit 4b5d3b3b638c · 2019-05-13T12:03:27.000-07:00
diff --git a/articles/notebooks/configure-manage-azure-notebooks-projects.md b/articles/notebooks/configure-manage-azure-notebooks-projects.md
@@ -12,7 +12,7 @@ ms.workload: na
 ms.tgt_pltfrm: na
 ms.devlang: na
 ms.topic: article
-ms.date: 02/25/2019
+ms.date: 05/13/2019
 ms.author: kraigb
 ---
 
@@ -33,8 +33,7 @@ Azure Notebooks starts the underlying virtual machine whenever you run a noteboo
 
 ## Compute tier
 
-By default, projects run on the Free Compute tier, which is limited to 4GB of memory and 1GB of data to prevent abuse. You can bypass these limitations by using a different virtual machine that you've provisioned in an Azure subscription. Connecting to a DSVM can increase your compute power.
-See [how to use Data Science Virtual Machines](use-data-science-virtual-machine.md).
+By default, projects run on the **Free Compute** tier, which is limited to 4GB of memory and 1GB of data to prevent abuse. You can bypass these limitations and increase compute power by using a different virtual machine that you've provisioned in an Azure subscription. For more information, see [How to use Data Science Virtual Machines](use-data-science-virtual-machine.md).
 
 ## Edit project metadata
 
diff --git a/articles/notebooks/toc.yml b/articles/notebooks/toc.yml
@@ -44,5 +44,7 @@
     href: work-with-project-data-files.md
   - name: Access data resources
     href: access-data-resources-jupyter-notebooks.md
+  - name: Use Data Science Virtual Machines
+    href: use-data-science-virtual-machine.md
   - name: Use Azure Machine Learning Services
     href: use-machine-learning-services-jupyter-notebooks.md
diff --git a/articles/notebooks/use-data-science-virtual-machine.md b/articles/notebooks/use-data-science-virtual-machine.md
@@ -1,6 +1,6 @@
 ---
-title: Use Data Science Virtual Machines
-description: Why use a Data Science Virutal Machine (DSVM). How to connect to a DSVM, and how to access a DSVM from Azure Notebooks.
+title: Use Azure Data Science Virtual Machines
+description: Connect to a Azure Data Science Virtual Machine (DSVM) to extend the compute power available to Azure Notebooks.
 services: app-service
 documentationcenter: ''
 author: getroyer
@@ -16,31 +16,20 @@ ms.date: 05/08/2019
 ms.author: getroyer
 ---
 
-# Using Data Science Virtual Machines
+# Use Azure Data Science Virtual Machines
 
-## Why connect to a Data Science Virtual Machine (DSVM)
+By default, projects run on the **Free Compute** tier, which is limited to 4GB of memory and 1GB of data to prevent abuse. You can bypass these limitations by using a different virtual machine that you've provisioned in an Azure subscription. For this purpose, the best choice is an Azure Data Science Virtual Machine (DSVM) using the **Data Science Virtual Machine for Linux (Ubuntu)** image. Such a DSVM comes pre-configured with everything you need for Azure Notebooks and appears automatically on the **Run** drop-down list in Azure Notebooks.
 
-By default, projects run on the **Free Compute** tier, which is limited to 4GB of memory and 1GB of data to prevent abuse. You can bypass these limitations by using a different virtual machine that you've provisioned in an Azure subscription. Connecting to a [DSVM](https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/) can increase your compute power.
+> [!Note]
+> Azure Notebooks is supported only on DSVMs created with the on Linux Ubuntu image. Notebooks are not supported on Windows 2012, Windows 2016, or Linux CentOS images.
 
-## How to create a DSVM
+## Create a DSVM instance
 
-|Operating System|Azure Notebooks|
-|:---:|:---:|
-|Linux Ubuntu| Supported|
-|Windows 2016| Not Supported|
-|Windows 2012| Not Supported|
-|Linux CentOS| Not Supported|
+To create a new DSVM instance, follow the instructions on [Create an Ubuntu Data Science VM](/azure/machine-learning/data-science-virtual-machine/dsvm-ubuntu-intro). For additional information including pricing details, see [Data Science Virtual Machines](https://azure.microsoft.com/services/virtual-machines/data-science-virtual-machines/).
 
-To create a new DSVM instance, follow the instructions on [Create an Ubuntu Data Science VM](/azure/machine-learning/data-science-virtual-machine/dsvm-ubuntu-intro). Use the **Data Science Virtual Machine for Linux (Ubuntu)** image if you want the DSVM to appear in the drop-down list in Azure Notebooks.
+## Connect to the DSVM
 
-## Ways to connect to a DSVM
-
-The **Run** drop-down list on the project dashboard is where you select the compute tier on which the project runs. 
-
-![Compute tier drop-down list on the project dashboard](media/project-compute-tier-list.png)
-
-### Discovery
-If the following conditions are true, the drop-down list also shows DSVM instances. (If any of these conditions aren't met, you can still connect to the DSVM using the Direct Compute option and enter the values obtained from the Azure portal.)
+Once you're created the DSVM, select the **Run** drop-down list on the Azure Notebooks project dashboard, and select the appropriate DSVM instance. The drop-down list shows DSVM instances if the following conditions are true:
 
 - You're signed into Azure Notebooks with an account that uses Azure Active Directory (AAD), such as a company account.
 - Your account is connected to an Azure subscription.
@@ -50,16 +39,17 @@ If the following conditions are true, the drop-down list also shows DSVM instanc
 
 When you select a DSVM instance, Azure Notebooks may prompt you for the specific machine credentials used when you created the VM.
 
-### Direct Connect
-Once you have a suitably configured Azure virtual machine, select the **Direct Compute** option in the drop-down list, which prompts you for a name (to show in the list), the VM's IP address and port (typically 8000, the default port to which JupyterHub listens), and the VM credentials:
+If any of the conditions aren't met, you can still connect to the DSVM. On the drop-down list, select the **Direct Compute** option,
+which prompts you for a name (to show in the list), the VM's IP address and port (typically 8000, the default port to which JupyterHub listens), and the VM credentials:
 
 ![Prompt to collect server information for the Direct Compute option](media/project-compute-tier-direct.png)
 
+You obtain these values from the DSVM page in the Azure portal.
 
-## Accessing Azure Notebooks Files from DSVM
+## Accessing Azure Notebooks files from the DSVM
 
-To preserve parity of file paths with free compute you are only able to open one project at a time on a DSVM. If you would like to open a new project after opening a project, you will need to shutdown the open project first.
-
-When a project is run on a VM, the files are mounted on the root directory of the Jupyter server (the directory shown in JupyterHub), replacing the default Azure Notebooks files. When you shut down the VM using the **Shutdown** button on the notebook UI, Azure Notebooks restores the default files.
+To preserve parity of file paths with the **Free Compute** tier, you are able to only open one project at a time on a DSVM. To open a new project, you myst shut down the open project first.
 
 ![Shutdown button in Azure Notebooks](media/shutdown.png)
+
+When a project is run on a VM, the files are mounted on the root directory of the Jupyter server (the directory shown in JupyterHub), replacing the default Azure Notebooks files. When you shut down the VM using the **Shutdown** button on the notebook UI, Azure Notebooks restores the default files.
