Merge pull request #293827 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-docs (branch main)

Author: v-alje

articles/azure-compute-fleet/attribute-based-vm-selection.md

@@ -236,7 +236,7 @@ The following list of VM attributes are supported and provide examples of config
 
 - Optional
 - The `acceleratorManufacturers` is specified as a list
-- Valid values are *AMD*, *Nvidia*, and *Xilinx*
+- Valid values are *AMD*, *NVIDIA*, and *Xilinx*
 - `acceleratorSupport` should be set to *Included* or *Required* to use this VM attribute
 - If `acceleratorSupport` is set to *Excluded*, this VM attribute can't be used
 - The default for `acceleratorManufacturers`, if not specified, is *ANY* of the valid values

articles/bastion/kerberos-authentication-portal.md

@@ -12,7 +12,7 @@ ms.author: cherylmc
 
 # Configure Bastion for Kerberos authentication using the Azure portal
 
-This article shows you how to configure Azure Bastion to use Kerberos authentication. Kerberos authentication can be used with both the Basic and the Standard Bastion SKUs. For more information about Kerberos authentication, see the [Kerberos authentication overview](/windows-server/security/kerberos/kerberos-authentication-overview). For more information about Azure Bastion, see [What is Azure Bastion?](bastion-overview.md)
+This article shows you how to configure Azure Bastion to use Kerberos authentication. Kerberos authentication can be used with Basic SKU tier or higher for Azure Bastion. For more information about Kerberos authentication, see the [Kerberos authentication overview](/windows-server/security/kerberos/kerberos-authentication-overview). For more information about Azure Bastion, see [What is Azure Bastion?](bastion-overview.md)
 
 ## Considerations
 

articles/cost-management-billing/reservations/exchange-and-refund-azure-reservations.md

@@ -34,21 +34,21 @@ When you exchange a reservation, you can change your term from one-year to three
 
 Not all reservations are eligible for exchange. For example, you can't exchange the following reservations:
 
-- Azure Databricks reserved capacity
+- Azure Databricks Pre-purchase plan
 - Azure OpenAI provisioned throughput
 - Synapse Analytics Pre-purchase plan
 - Red Hat plans
 - SUSE Linux plans
 - Microsoft Defender for Cloud Pre-Purchase Plan
 - Microsoft Sentinel Pre-Purchase Plan
 
-You can also refund reservations, but the sum total of all canceled reservation commitment in your billing scope (such as EA, Microsoft Customer Agreement, and Microsoft Partner Agreement) can't exceed USD 50,000 in a 12 month rolling window.
+You can also refund reservations, but the sum total of all canceled reservation commitment in your billing scope (such as EA, Microsoft Customer Agreement - Billing Profile, and Microsoft Partner Agreement - Customer) can't exceed USD 50,000 in a 12 month rolling window.
 
 *Microsoft is not currently charging early termination fees for reservation refunds. We might charge the fees for refunds made in the future. We currently don't have a date for enabling the fee.*
 
 The following reservations aren't eligible for refunds:
 
-- Azure Databricks reserved capacity
+- Azure Databricks Pre-purchase plan
 - Synapse Analytics Pre-purchase plan
 - Azure VMware solution by CloudSimple
 - Red Hat plans

articles/databox-online/TOC.yml

@@ -404,7 +404,7 @@
         items:
           - name: IoT Edge on VM
             href: azure-stack-edge-gpu-deploy-iot-edge-linux-vm.md
-          - name: Nvidia DeepStream module
+          - name: NVIDIA DeepStream module
             href: azure-stack-edge-deploy-nvidia-deepstream-module.md
       - name: Troubleshoot IoT Edge issues
         href: azure-stack-edge-gpu-troubleshoot-iot-edge.md

articles/databox-online/azure-stack-edge-deploy-nvidia-deepstream-module.md

@@ -1,6 +1,6 @@
 ---
-title: Deploy the Nvidia DeepStream module on Ubuntu VM on Azure Stack Edge Pro with GPU | Microsoft Docs
-description: Learn how to deploy the Nvidia Deepstream module on an Ubuntu virtual machine that is running on your Azure Stack Edge Pro GPU device.
+title: Deploy the NVIDIA DeepStream module on Ubuntu VM on Azure Stack Edge Pro with GPU | Microsoft Docs
+description: Learn how to deploy the NVIDIA Deepstream module on an Ubuntu virtual machine that is running on your Azure Stack Edge Pro GPU device.
 services: databox
 author: alkohli
 
@@ -10,11 +10,11 @@ ms.date: 06/28/2022
 ms.author: alkohli
 ---
 
-# Deploy the Nvidia DeepStream module on Ubuntu VM on Azure Stack Edge Pro with GPU
+# Deploy the NVIDIA DeepStream module on Ubuntu VM on Azure Stack Edge Pro with GPU
 
 [!INCLUDE [applies-to-pro-gpu-and-pro-2-and-pro-r-skus](../../includes/azure-stack-edge-applies-to-gpu-pro-pro-2-pro-r-sku.md)]
 
-This article walks you through deploying Nvidia’s DeepStream module on an Ubuntu VM running on your Azure Stack Edge device. The DeepStream module is supported only on GPU devices. 
+This article walks you through deploying NVIDIA’s DeepStream module on an Ubuntu VM running on your Azure Stack Edge device. The DeepStream module is supported only on GPU devices. 
 
 ## Prerequisites
 

articles/databox-online/azure-stack-edge-gpu-configure-gpu-modules.md

@@ -18,7 +18,7 @@ ms.author: alkohli
 
 Your Azure Stack Edge Pro device contains one or more Graphics Processing Unit (GPU). GPUs are a popular choice for AI computations as they offer parallel processing capabilities and are faster at image rendering than Central Processing Units (CPUs). For more information on the GPU contained in your Azure Stack Edge Pro device, go to [Azure Stack Edge Pro device technical specifications](azure-stack-edge-gpu-technical-specifications-compliance.md).
 
-This article describes how to configure and run a module on the GPU on your Azure Stack Edge Pro device. In this article, you will use a publicly available container module **Digits** written for Nvidia T4 GPUs. This procedure can be used to configure any other modules published by Nvidia for these GPUs.
+This article describes how to configure and run a module on the GPU on your Azure Stack Edge Pro device. In this article, you will use a publicly available container module **Digits** written for NVIDIA T4 GPUs. This procedure can be used to configure any other modules published by NVIDIA for these GPUs.
 
 ## Prerequisites
 
@@ -81,7 +81,7 @@ To configure a module to use the GPU on your Azure Stack Edge Pro device to run
 
 10. In the **Add IoT Edge Module** tab:
 
-    1. Provide the **Image URI**. You will use the publicly available Nvidia module **Digits** here. 
+    1. Provide the **Image URI**. You will use the publicly available NVIDIA module **Digits** here. 
 
     2. Set **Restart policy** to **always**.
 
@@ -97,7 +97,7 @@ To configure a module to use the GPU on your Azure Stack Edge Pro device to run
 
        ![Configure module to use GPU 11](media/azure-stack-edge-gpu-configure-gpu-modules/configure-gpu-7.png)
 
-       For more information on environment variables that you can use with the Nvidia GPU, go to [nVidia container runtime](https://github.com/NVIDIA/nvidia-container-runtime#environment-variables-oci-spec).
+       For more information on environment variables that you can use with the NVIDIA GPU, go to [nVidia container runtime](https://github.com/NVIDIA/nvidia-container-runtime#environment-variables-oci-spec).
 
     > [!NOTE]
     > A module can use one, both or no GPUs.
@@ -125,4 +125,4 @@ To configure a module to use the GPU on your Azure Stack Edge Pro device to run
 
 ## Next steps
 
-- Learn more about [Environment variables that you can use with the Nvidia GPU](https://github.com/NVIDIA/nvidia-container-runtime#environment-variables-oci-spec).
+- Learn more about [Environment variables that you can use with the NVIDIA GPU](https://github.com/NVIDIA/nvidia-container-runtime#environment-variables-oci-spec).

articles/databox-online/azure-stack-edge-gpu-connect-powershell-interface.md

@@ -62,7 +62,7 @@ If the compute role is configured on your device, you can also get the GPU drive
 
 ## Enable Multi-Process Service (MPS)
 
-A Multi-Process Service (MPS) on Nvidia GPUs provides a mechanism where GPUs can be shared by multiple jobs, where each job is allocated some percentage of the GPU's resources. MPS is a preview feature on your Azure Stack Edge Pro GPU device. To enable MPS on your device, follow these steps:
+A Multi-Process Service (MPS) on NVIDIA GPUs provides a mechanism where GPUs can be shared by multiple jobs, where each job is allocated some percentage of the GPU's resources. MPS is a preview feature on your Azure Stack Edge Pro GPU device. To enable MPS on your device, follow these steps:
 
 [!INCLUDE [Enable MPS](../../includes/azure-stack-edge-gateway-enable-mps.md)]
 

articles/databox-online/azure-stack-edge-gpu-deploy-compute-acceleration.md

@@ -25,8 +25,8 @@ Compute acceleration is a term used specifically for Azure Stack Edge devices wh
 
 The article will discuss compute acceleration only using GPU or VPU for the following devices:
 
-- **Azure Stack Edge Pro GPU** - These devices can have 1 or 2 Nvidia T4 Tensor Core GPU. For more information, see [NVIDIA T4](https://www.nvidia.com/en-us/data-center/tesla-t4/).
-- **Azure Stack Edge Pro R** - These devices have 1 Nvidia T4 Tensor Core GPU. For more information, see [NVIDIA T4](https://www.nvidia.com/en-us/data-center/tesla-t4/).
+- **Azure Stack Edge Pro GPU** - These devices can have 1 or 2 NVIDIA T4 Tensor Core GPU. For more information, see [NVIDIA T4](https://www.nvidia.com/en-us/data-center/tesla-t4/).
+- **Azure Stack Edge Pro R** - These devices have 1 NVIDIA T4 Tensor Core GPU. For more information, see [NVIDIA T4](https://www.nvidia.com/en-us/data-center/tesla-t4/).
 - **Azure Stack Edge Mini R** - These devices have 1 Intel Movidius Myriad X VPU. For more information, see [Intel Movidius Myriad X VPU](https://www.movidius.com/MyriadX).
 
 

articles/databox-online/azure-stack-edge-gpu-deploy-gpu-virtual-machine.md

@@ -38,7 +38,7 @@ Follow these steps when deploying GPU VMs on your device via the Azure portal:
 
 1. To create GPU VMs, follow all the steps in [Deploy VM on your Azure Stack Edge using Azure portal](azure-stack-edge-gpu-deploy-virtual-machine-portal.md), with these configuration requirements:
 
-    - On the **Basics** tab, select a [VM size from N-series, optimized for GPUs](azure-stack-edge-gpu-virtual-machine-sizes.md#n-series-gpu-optimized). Based on the GPU model on your device, Nvidia T4 or Nvidia A2, the dropdown list will display the corresponding supported GPU VM sizes.  
+    - On the **Basics** tab, select a [VM size from N-series, optimized for GPUs](azure-stack-edge-gpu-virtual-machine-sizes.md#n-series-gpu-optimized). Based on the GPU model on your device, NVIDIA T4 or NVIDIA A2, the dropdown list will display the corresponding supported GPU VM sizes.  
 
        ![Screenshot of Basics tab for "Add a virtual machine" in Azure Stack Edge. Size option, with a supported VM size for GPU VMs, is highlighted.](media/azure-stack-edge-gpu-deploy-gpu-virtual-machine/basics-vm-size-for-gpu.png)
 
@@ -97,7 +97,7 @@ After the VM is created, you can [deploy the GPU extension using the extension t
 
 ## Install GPU extension after deployment
 
-To take advantage of the GPU capabilities of Azure N-series VMs, Nvidia GPU drivers must be installed. From the Azure portal, you can install the GPU extension during or after VM deployment. If you're using templates, you'll install the GPU extension after you create the VM.
+To take advantage of the GPU capabilities of Azure N-series VMs, NVIDIA GPU drivers must be installed. From the Azure portal, you can install the GPU extension during or after VM deployment. If you're using templates, you'll install the GPU extension after you create the VM.
 
 ---
 

articles/databox-online/azure-stack-edge-gpu-deploy-iot-edge-gpu-sharing.md

@@ -21,7 +21,7 @@ Before you begin, make sure that:
 
 1. You've access to a client system with a [Supported operating system](azure-stack-edge-gpu-system-requirements.md#supported-os-for-clients-connected-to-device). If using a Windows client, the system should run PowerShell 5.0 or later to access the device.
 
-1. Save the following deployment `json` on your local system. You'll use information from this file to run the IoT Edge deployment. This deployment is based on [Simple CUDA containers](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#running-simple-containers) that are publicly available from Nvidia. 
+1. Save the following deployment `json` on your local system. You'll use information from this file to run the IoT Edge deployment. This deployment is based on [Simple CUDA containers](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#running-simple-containers) that are publicly available from NVIDIA. 
 
     ```json
     {
@@ -118,7 +118,7 @@ The first step is to verify that your device is running required GPU driver and
 
     `Get-HcsGpuNvidiaSmi`
 
-1. In the Nvidia smi output, make a note of the GPU version and the CUDA version on your device. If you are running Azure Stack Edge 2102 software, this version would correspond to the following driver versions:
+1. In the NVIDIA smi output, make a note of the GPU version and the CUDA version on your device. If you are running Azure Stack Edge 2102 software, this version would correspond to the following driver versions:
 
     - GPU driver version: 460.32.03
     - CUDA version: 11.2
@@ -152,7 +152,7 @@ The first step is to verify that your device is running required GPU driver and
     [10.100.10.10]: PS>  
     ```
 
-1. Keep this session open as you will use it to view the Nvidia smi output throughout the article.
+1. Keep this session open as you will use it to view the NVIDIA smi output throughout the article.
 
 
 ## Deploy without context-sharing
@@ -216,7 +216,7 @@ For detailed instructions, see [Connect to and manage a Kubernetes cluster via k
 
 ### Deploy modules via portal
 
-Deploy IoT Edge modules via the Azure portal. You'll deploy publicly available Nvidia CUDA sample modules that run n-body simulation.
+Deploy IoT Edge modules via the Azure portal. You'll deploy publicly available NVIDIA CUDA sample modules that run n-body simulation.
 
 1. Make sure that the IoT Edge service is running on your device.
 
@@ -316,7 +316,7 @@ Deploy IoT Edge modules via the Azure portal. You'll deploy publicly available N
     ```
     There are two pods, `cuda-sample1-97c494d7f-lnmns` and `cuda-sample2-d9f6c4688-2rld9` running on your device.
 
-1. While both the containers are running the n-body simulation, view the GPU utilization from the Nvidia smi output. Go to the PowerShell interface of the device and run `Get-HcsGpuNvidiaSmi`.
+1. While both the containers are running the n-body simulation, view the GPU utilization from the NVIDIA smi output. Go to the PowerShell interface of the device and run `Get-HcsGpuNvidiaSmi`.
 
     Here is an example output when both the containers are running the n-body simulation:
 
@@ -349,7 +349,7 @@ Deploy IoT Edge modules via the Azure portal. You'll deploy publicly available N
     ```
     As you can see, there are two containers running with n-body simulation on GPU 0. You can also view their corresponding memory usage.
 
-1. Once the simulation has completed, the Nvidia smi output will show that there are no processes running on the device.
+1. Once the simulation has completed, the NVIDIA smi output will show that there are no processes running on the device.
 
     ```powershell
     [10.100.10.10]: PS>Get-HcsGpuNvidiaSmi
@@ -460,7 +460,7 @@ You can now deploy the n-body simulation on two CUDA containers when MPS is runn
     Created nvidia-mps.service
     [10.100.10.10]: PS>    
     ```
-1. Get the Nvidia smi output from the PowerShell interface of the device. You can see the `nvidia-cuda-mps-server` process or the MPS service is running on the device.
+1. Get the NVIDIA smi output from the PowerShell interface of the device. You can see the `nvidia-cuda-mps-server` process or the MPS service is running on the device.
 
     Here is an example output:
 
@@ -548,7 +548,7 @@ You can now deploy the n-body simulation on two CUDA containers when MPS is runn
     PS C:\WINDOWS\system32>    
     ```      
 
-1. Get the Nvidia smi output from the PowerShell interface of the device when both the containers are running the n-body simulation. Here is an example output. There are three processes, the `nvidia-cuda-mps-server` process (type C) corresponds to the MPS service and the `/tmp/nbody` processes (type M + C) correspond to the n-body workloads deployed by the modules. 
+1. Get the NVIDIA smi output from the PowerShell interface of the device when both the containers are running the n-body simulation. Here is an example output. There are three processes, the `nvidia-cuda-mps-server` process (type C) corresponds to the MPS service and the `/tmp/nbody` processes (type M + C) correspond to the n-body workloads deployed by the modules. 
 
     ```powershell
     [10.100.10.10]: PS>Get-HcsGpuNvidiaSmi