Merge pull request #201136 from EMaher/enewman/updatesForNicole

GPU, Adobe CC, Canvas, and nested virtualization update

Author: jborsecnik

articles/lab-services/TOC.yml

@@ -269,6 +269,8 @@
         href: how-to-add-user-lab-owner.md
       - name: Manage labs in a lab account
         href: manage-labs-1.md
+    - name: Set up GPU VMs when using lab accounts
+      href: how-to-setup-lab-gpu-1.md
     - name: Az.LabServices PowerShell module for lab accounts
       href: reference-powershell-module.md     
 - name: Reference

articles/lab-services/class-type-adobe-creative-cloud.md

@@ -47,6 +47,12 @@ The size of VM that you need to use for your lab depends on the types of project
 > [!WARNING]
 > The **Small GPU (Visualization)** virtual machine size is configured to enable a high-performing graphics experience and meets [Adobe’s system requirements for each application](https://helpx.adobe.com/creative-cloud/system-requirements.html).  Make sure to choose Small GPU (Visualization) not Small GPU (Compute).  For more information about this virtual machine size, see the article on [how to set up a lab with GPUs](./how-to-setup-lab-gpu.md).
 
+#### GPU drivers
+
+When you create the lab, we recommend that you install the GPU drivers by selecting the **Install GPU drivers** option in the lab creation wizard.  You should also validate that the GPU drivers are correctly installed.  For more information, read the following sections:
+- [Ensure that the appropriate GPU drivers are installed](../lab-services/how-to-setup-lab-gpu.md#ensure-that-the-appropriate-gpu-drivers-are-installed)
+- [Validate the installed drivers](../lab-services/how-to-setup-lab-gpu.md#validate-the-installed-drivers)
+
 ## Template machine configuration
 
 ### Creative Cloud deployment package
@@ -90,6 +96,16 @@ Consider saving your template VM for future use.  To save the template VM, see [
 - When self-service is *enabled*, the template VM’s image will have Creative Cloud desktop installed.  Teachers can then reuse this image to create labs and to choose which Creative Cloud apps to install.  This helps reduce IT overhead since teachers can independently set up labs and have full control over installing the Creative Cloud apps required for their classes.
 - When self-service is *disabled*, the template VM’s image will already have the specified Creative Cloud apps installed.  Teachers can reuse this image to create labs; however, they won’t be able to install additional Creative Cloud apps.
 
+### Troubleshooting
+
+Adobe Creative Cloud may show an error saying *Your graphics processor is incompatible* when the GPU drivers or the GPU is not configured correctly.
+
+:::image type="content" source="./media/class-type-adobe-creative-cloud/gpu-driver-error.png" alt-text="Screenshot of Adobe Creative Cloud showing an error message that the graphics processor is incompatible.":::
+
+To fix this issue:
+- Ensure that you selected the Small GPU *(Visualization)* size when you created your lab.  You can see the VM size used by the lab on the lab's [Template page](../lab-services/how-to-create-manage-template.md).
+- Try [manually installing the Small GPU Visualization drivers](../lab-services/how-to-setup-lab-gpu.md#install-the-small-gpu-visualization-drivers). 
+
 ## Cost
 
 In this section, we’ll look at a possible cost estimate for this class.  We’ll use a class of 25 students with 20 hours of scheduled class time.  Also, each student gets 10 hours quota for homework or assignments outside scheduled class time. The virtual machine size we chose was **Small GPU (Visualization)**, which is 160 lab units.

articles/lab-services/how-to-enable-nested-virtualization-template-vm.md

@@ -57,3 +57,14 @@ To use the automated setup for nested virtualization with Windows Server 2016 or
 ### Using Windows tools to enable nested virtualization
 
 To configure nested virtualization for Windows Server 2016 or 2019 manually, see [Enable nested virtualization on a template virtual machine in Azure Lab Services manually](how-to-enable-nested-virtualization-template-vm-ui.md).  Instructions will also cover configuring networking so the Hyper-V VMs have internet access.
+
+### Processor compatibility
+
+The nested virtualization VM sizes may use different processors as shown in the following table:
+
+ Size | Series | Processor |
+| ---- | ----- |  ----- |
+| Medium (nested virtualization) | [Standard_D4s_v4](../virtual-machines/dv4-dsv4-series.md) |  3rd Generation Intel® Xeon® Platinum 8370C (Ice Lake) or the Intel® Xeon® Platinum 8272CL (Cascade Lake) |
+| Large (nested virtualization) | [Standard_D8s_v4](../virtual-machines/dv4-dsv4-series.md) | 3rd Generation Intel® Xeon® Platinum 8370C (Ice Lake) or the Intel® Xeon® Platinum 8272CL (Cascade Lake) |
+
+Each time that a template VM or a student VM is stopped and started, the underlying processor may change.  To help ensure that nested VMs work consistently across processors, try enabling [processor compatibility mode](/windows-server/virtualization/hyper-v/manage/processor-compatibility-mode-hyper-v) on the nested VMs.  It's recommended to enable **Processor Compatibility** mode on the template VM's nested VMs before publishing or exporting the image.  You should also test the performance of the nested VMs with the **Processor Compatibility** mode enabled to ensure performance isn't negatively impacted.  For more information, see [ramifications of using processor compatibility mode](/windows-server/virtualization/hyper-v/manage/processor-compatibility-mode-hyper-v.md#ramifications-of-using-processor-compatibility-mode).

articles/lab-services/how-to-get-started-create-lab-within-canvas.md

@@ -116,19 +116,38 @@ The student list for the course is automatically synced with the course roster.
 
 This section outlines common error messages that you may see, along with the steps to resolve them.
 
+- Insufficient permissions to create lab.
+
+  In Canvas, an educator will see a message indicating that they don't have sufficient permission. Educators should contact their Azure admin so they can be [added as a **Lab Creator**](tutorial-setup-lab-plan.md#add-a-user-to-the-lab-creator-role).  For example, educators can be added as a **Lab Creator** to the resource group that contains their lab.
+
+- Message that there isn't enough capacity to create lab VMs.
+
+  [Request a limit increase](capacity-limits.md#request-a-limit-increase) which needs to be done by an Azure Labs Services administrator.
+
 - Student sees warning that the lab isn't available yet.
 
   In Canvas, you'll see the following message if the educator hasn't published the lab yet.  Educators must [publish the lab](tutorial-setup-lab.md#publish-a-lab) and [sync users](how-to-manage-user-lists-within-canvas.md#sync-users) for students to have access to a lab.
 
   :::image type="content" source="./media/how-to-get-started-create-labs-within-canvas/troubleshooting-lab-isnt-available-yet.png" alt-text="Troubleshooting -> This lab is not available yet":::
 
-- Insufficient permissions to create lab.
+- Student or educator is prompted to grant access.
 
-  In Canvas, an educator will see a message indicating that they don't have sufficient permission. Educators should contact their Azure admin so they can be [added as a **Lab Creator**](tutorial-setup-lab-plan.md#add-a-user-to-the-lab-creator-role).
+  Before a student or educator can first access their lab, some browsers require that they first grant Azure Lab Services access to the browser's local storage.  To grant access, educators and students should click the **Grant access** button when they are prompted:
 
-- Message that there isn't enough capacity to create lab VMs.
+  :::image type="content" source="./media/how-to-get-started-create-labs-within-canvas/canvas-grant-access-prompt.png" alt-text="Screenshot of page to grant Azure Lab Services access to use local storage for the browser.":::
+
+  Educators and students will see the message **Access granted** when access is successfully granted to Azure Lab Services.  The educator or student should then reload the browser window to start using Azure Lab Services.
+
+  :::image type="content" source="./media/how-to-get-started-create-labs-within-canvas/canvas-access-granted-success.png" alt-text="Screenshot of access granted page in Azure Lab Services.":::
+
+  > [!IMPORTANT]
+  > Ensure that students and educators are using an up-to-date version of their browser.  For older browser versions, students and educators may experience issues with being able to successfully grant access to Azure Lab Services.
+
+  - Educator isn't prompted for their credentials after they click sign-in.
+  
+  When an educator accesses Azure Lab Services within their course, they may be prompted to sign in.  Ensure that the browser's settings allow popups from the url of your Canvas instance, otherwise the popup may be blocked by default.
 
-  [Request a limit increase](capacity-limits.md#request-a-limit-increase).  
+    :::image type="content" source="./media/how-to-get-started-create-labs-within-canvas/canvas-sign-in.png" alt-text="Azure Lab Services sign-in screen.":::
 
 ## Next steps
 

articles/lab-services/how-to-setup-lab-gpu-1.md

@@ -0,0 +1,130 @@
+---
+title: Set up a lab with GPUs in Azure Lab Services when using lab accounts | Microsoft Docs
+description: Learn how to set up a lab with graphics processing unit (GPU) virtual machines when using lab accounts. 
+author: nicolela
+ms.topic: how-to
+ms.date: 06/26/2020
+ms.author: nicolela
+---
+
+# Set up GPU virtual machines in labs contained within lab accounts
+
+[!INCLUDE [preview note](./includes/lab-services-new-update-note.md)]
+
+This article shows you how to do the following tasks:
+
+- Choose between *visualization* and *compute* graphics processing units (GPUs).
+- Ensure that the appropriate GPU drivers are installed.
+
+## Choose between visualization and compute GPU sizes
+
+On the first page of the lab creation wizard, in the **Which virtual machine size do you need?** drop-down list, you select the size of the VMs that are needed for your class.  
+
+![Screenshot of the "New lab" pane for selecting a VM size](./media/how-to-setup-gpu-1/lab-gpu-selection.png)
+
+In this process, you have the option of selecting either **Visualization** or **Compute** GPUs.  It's important to choose the type of GPU that's based on the software that your students will use.  
+
+As described in the following table, the *compute* GPU size is intended for compute-intensive applications.  For example, the [Deep Learning in Natural Language Processing class type](./class-type-deep-learning-natural-language-processing.md) uses the **Small GPU (Compute)** size.  The compute GPU is suitable for this type of class, because students use deep learning frameworks and tools that are provided by the [Data Science Virtual Machine image](https://azuremarketplace.microsoft.com/marketplace/apps/microsoft-dsvm.ubuntu-1804) to train deep learning models with large sets of data.
+
+| Size | vCPUs | RAM | Description |
+| ---- | ----- | --- | ----------- |
+| Small GPU (Compute) | 6  vCPUs | 56 GB RAM  | [Standard_NC6](../virtual-machines/nc-series.md). This size is best suited for compute-intensive applications such as artificial intelligence (AI) and deep learning. |
+
+The *visualization* GPU sizes are intended for graphics-intensive applications.  For example, the [SOLIDWORKS engineering class type](./class-type-solidworks.md) shows using the **Small GPU (Visualization)** size.  The visualization GPU is suitable for this type of class, because students interact with the SOLIDWORKS 3D computer-aided design (CAD) environment for modeling and visualizing solid objects.
+
+| Size | vCPUs | RAM | Description |
+| ---- | ----- | --- | ----------- |
+| Small GPU (Visualization) | 6 vCPUs | 56 GB RAM  | [Standard_NV6](../virtual-machines/nv-series.md).  This size is best suited for remote visualization, streaming, gaming, and encoding that use frameworks such as OpenGL and DirectX. |
+| Medium GPU (Visualization) | 12 vCPUs  | 112 GB RAM  | [Standard_NV12](../virtual-machines/nv-series.md?bc=%2fazure%2fvirtual-machines%2flinux%2fbreadcrumb%2ftoc.json&toc=%2fazure%2fvirtual-machines%2flinux%2ftoc.json).  This size is best suited for remote visualization, streaming, gaming, and encoding that use frameworks such as OpenGL and DirectX. |
+
+> [!NOTE]
+> You may not see some of these VM sizes in the list when creating a lab. The list is populated based on the current capacity of the lab's location. For availability of VMs, see [Products available by region](https://azure.microsoft.com/regions/services/?products=virtual-machines).
+
+## Ensure that the appropriate GPU drivers are installed
+
+To take advantage of the GPU capabilities of your lab VMs, ensure that the appropriate GPU drivers are installed.  In the lab creation wizard, when you select a GPU VM size, you can select the **Install GPU drivers** option.  
+
+![Screenshot of the "New lab" showing the "Install GPU drivers" option](./media/how-to-setup-gpu-1/lab-gpu-drivers.png)
+
+As shown in the preceding image, this option is enabled by default, which ensures that recently released drivers are installed for the type of GPU and image that you selected:
+
+- When you select a *compute* GPU size, your lab VMs are powered by the [NVIDIA Tesla K80](https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/tesla-product-literature/Tesla-K80-BoardSpec-07317-001-v05.pdf) GPU.  In this case, recent [Compute Unified Device Architecture (CUDA)](http://developer.download.nvidia.com/compute/cuda/2_0/docs/CudaReferenceManual_2.0.pdf) drivers are installed, which enables high-performance computing.
+- When you select a *visualization* GPU size, your lab VMs are powered by the [NVIDIA Tesla M60](https://images.nvidia.com/content/tesla/pdf/188417-Tesla-M60-DS-A4-fnl-Web.pdf) GPU and [GRID technology](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/solutions/resources/documents1/NVIDIA_GRID_vPC_Solution_Overview.pdf).  In this case, recent GRID drivers are installed, which enables the use of graphics-intensive applications.
+
+> [!IMPORTANT]
+> The **Install GPU drivers** option only installs the drivers when they aren't present on your lab's image.  For example, the GPU drivers are already installed on the Azure marketplace's [Data Science image](../machine-learning/data-science-virtual-machine/overview.md#whats-included-on-the-dsvm).  If you create a lab using the Data Science image and choose to **Install GPU drivers**, the drivers won't be updated to a more recent version.  To update the drivers, you will need to manually install them as explained in the next section.  
+
+### Install the drivers manually
+
+You might need to install a different version of the drivers than the version that Azure Lab Services installs for you.  This section shows how to manually install the appropriate drivers, depending on whether you're using a *compute* GPU or a *visualization* GPU.
+
+#### Install the compute GPU drivers
+
+To manually install drivers for the *compute* GPU size, by doing the following steps:
+
+1. In the lab creation wizard, when you're [creating your lab](./how-to-manage-labs.md), disable the **Install GPU drivers** setting.
+
+1. After your lab is created, connect to the template VM to install the appropriate drivers.
+
+   ![Screenshot of the NVIDIA Driver Downloads page](./media/how-to-setup-gpu-1/nvidia-driver-download.png)
+
+   a. In a browser, go to the [NVIDIA Driver Downloads page](https://www.nvidia.com/Download/index.aspx).  
+   b. Set the **Product Type** to **Tesla**.  
+   c. Set the **Product Series** to **K-Series**.  
+   d. Set the **Operating System** according to the type of base image you selected when you created your lab.  
+   e. Set the **CUDA Toolkit** to the version of CUDA driver that you need.  
+   f. Select **Search** to look for your drivers.  
+   g. Select **Download** to download the installer.  
+   h. Run the installer so that the drivers are installed on the template VM.  
+1. Validate that the drivers are installed correctly by following the instructions in the [Validate the installed drivers](how-to-setup-lab-gpu.md#validate-the-installed-drivers) section.
+1. After you've installed the drivers and other software that are required for your class, select **Publish** to create your students' VMs.
+
+> [!NOTE]
+> If you're using a Linux image, after you've downloaded the installer, install the drivers by following the instructions in [Install CUDA drivers on Linux](../virtual-machines/linux/n-series-driver-setup.md?toc=%2fazure%2fvirtual-machines%2flinux%2ftoc.json#install-cuda-drivers-on-n-series-vms).
+
+#### Install the visualization GPU drivers
+
+To manually install drivers for the *visualization* GPU sizes, follow these steps:
+
+1. In the lab creation wizard, when you're [creating your lab](./how-to-manage-labs.md), disable the **Install GPU drivers** setting.
+1. After your lab is created, connect to the template VM to install the appropriate drivers.
+1. Install the GRID drivers that are provided by Microsoft on the template VM by following the instructions for your operating system:
+   - [Windows NVIDIA GRID drivers](../virtual-machines/windows/n-series-driver-setup.md#nvidia-grid-drivers)
+   - [Linux NVIDIA GRID drivers](../virtual-machines/linux/n-series-driver-setup.md?toc=%2fazure%2fvirtual-machines%2flinux%2ftoc.json#nvidia-grid-drivers)
+  
+1. Restart the template VM.
+1. Validate that the drivers are installed correctly by following the instructions in the [Validate the installed drivers](how-to-setup-lab-gpu.md#validate-the-installed-drivers) section.
+1. After you've installed the drivers and other software that are required for your class, select **Publish** to create your students' VMs.
+
+### Validate the installed drivers
+
+This section describes how to validate that your GPU drivers are properly installed.
+
+#### Windows images
+
+1. Follow the instructions in the "Verify driver installation" section of [Install NVIDIA GPU drivers on N-series VMs running Windows](../virtual-machines/windows/n-series-driver-setup.md#verify-driver-installation).
+1. If you're using a *visualization* GPU, you can also:
+    - View and adjust your GPU settings in the NVIDIA Control Panel. To do so, in **Windows Control Panel**, select **Hardware**, and then select **NVIDIA Control Panel**.
+
+      ![Screenshot of Windows Control Panel showing the NVIDIA Control Panel link](./media/how-to-setup-gpu-1/control-panel-nvidia-settings.png)
+
+    - View your GPU performance by using **Task Manager**.  To do so, select the **Performance** tab, and then select the **GPU** option.
+
+       ![Screenshot showing the Task Manager GPU Performance tab](./media/how-to-setup-gpu-1/task-manager-gpu.png)
+
+      > [!IMPORTANT]
+      > The NVIDIA Control Panel settings can be accessed only for *visualization* GPUs.  If you attempt to open the NVIDIA Control Panel for a compute GPU, you'll get the following error: "NVIDIA Display settings are not available.  You are not currently using a display attached to an NVIDIA GPU."  Similarly, the GPU performance information in Task Manager is provided only for visualization GPUs.
+
+ Depending on your scenario, you may also need to do additional validation to ensure the GPU is properly configured.  Read the class type about [Python and Jupyter Notebooks](class-type-jupyter-notebook.md#template-machine-configuration) that explains an example where specific versions of drivers are needed.
+
+#### Linux images
+
+Follow the instructions in the "Verify driver installation" section of [Install NVIDIA GPU drivers on N-series VMs running Linux](../virtual-machines/linux/n-series-driver-setup.md#verify-driver-installation).
+
+## Next steps
+
+See the following articles:
+
+- [Create and manage labs](how-to-manage-labs.md)
+- [SOLIDWORKS computer-aided design (CAD) class type](class-type-solidworks.md)
+- [MATLAB (matrix laboratory) class type](class-type-matlab.md)