Merge pull request #203495 from MicrosoftDocs/release-ase-pro2sku

Release ase pro2sku--scheduled release at 10am of 7/01

Author: huypub

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

@@ -7,17 +7,17 @@ author: alkohli
 ms.service: databox
 ms.subservice: edge
 ms.topic: how-to
-ms.date: 05/26/2022
+ms.date: 06/24/2022
 ms.author: alkohli
 #Customer intent: As an IT admin, I want the flexibility to deploy a single GPU virtual machine (VM) quickly in the portal or use templates to deploy and manage multiple GPU VMs efficiently on my Azure Stack Edge Pro GPU device.
 ---
 
 
 # Deploy GPU VMs on your Azure Stack Edge Pro GPU device
 
-[!INCLUDE [applies-to-GPU-and-pro-r-skus](../../includes/azure-stack-edge-applies-to-gpu-pro-r-sku.md)]
+[!INCLUDE [applies-to-gpu-pro-pro2-and-pro-r-skus](../../includes/azure-stack-edge-applies-to-gpu-pro-pro-2-pro-r-sku.md)]
 
-This article how to create a GPU VM in the Azure portal or by using the Azure Resource Manager templates.
+This article describes how to create a GPU VM in the Azure portal or by using the Azure Resource Manager templates.
 
 Use the Azure portal to quickly deploy a single GPU VM. You can install the GPU extension during or after VM creation. Or use Azure Resource Manager templates to efficiently deploy and manage multiple GPU VMs.
 
@@ -39,11 +39,11 @@ 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 NCasT4-v3-series](azure-stack-edge-gpu-virtual-machine-sizes.md#ncast4_v3-series-preview).
+    - 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)
 
-    - To install the GPU extension during deployment, on the **Advanced** tab, choose **Select an extension to install**. Then select a GPU extension to install. GPU extensions are only available for a virtual machine with a [VM size from NCasT4-v3-series](azure-stack-edge-gpu-virtual-machine-sizes.md#ncast4_v3-series-preview).
+    - To install the GPU extension during deployment, on the **Advanced** tab, choose **Select an extension to install**. Then select a GPU extension to install. GPU extensions are only available for a virtual machine with a [VM size from N-series](azure-stack-edge-gpu-virtual-machine-sizes.md#n-series-gpu-optimized).
 
         > [!NOTE]
         > If you're using a Red Hat image, you'll need to install the GPU extension after VM deployment. Follow the steps in [Install GPU extension](azure-stack-edge-gpu-deploy-virtual-machine-install-gpu-extension.md).
@@ -73,7 +73,7 @@ Follow these steps when deploying GPU VMs on your device using Azure Resource Ma
 
 1. To create GPU VMs, follow all the steps in [Deploy VM on your Azure Stack Edge using templates](azure-stack-edge-gpu-deploy-virtual-machine-templates.md), with these configuration requirements: 
 
-    - When specifying GPU VM sizes, make sure to use the NCasT4-v3-series in the `CreateVM.parameters.json`, which are supported for GPU VMs. For more information, see [Supported VM sizes for GPU VMs](azure-stack-edge-gpu-virtual-machine-sizes.md#ncast4_v3-series-preview).
+    - When specifying GPU VM sizes, make sure to use the NCasT4-v3-series in the `CreateVM.parameters.json`, which are supported for GPU VMs. For more information, see [Supported VM sizes for GPU VMs](azure-stack-edge-gpu-virtual-machine-sizes.md#n-series-gpu-optimized).
 
        ```json
            "vmSize": {
@@ -112,7 +112,7 @@ If you didn't install the GPU extension when you created the VM, follow these st
 
 1. In **Details**, select **+ Add extension**. Then select a GPU extension to install.
 
-    GPU extensions are only available for a virtual machine with a [VM size from NCasT4-v3-series](azure-stack-edge-gpu-virtual-machine-sizes.md#ncast4_v3-series-preview). If you prefer, you can [install the GPU extension after deployment](azure-stack-edge-gpu-deploy-gpu-virtual-machine.md#install-gpu-extension-after-deployment).
+    GPU extensions are only available for a virtual machine with a [VM size from N-series](azure-stack-edge-gpu-virtual-machine-sizes.md#n-series-gpu-optimized). If you prefer, you can [install the GPU extension after deployment](azure-stack-edge-gpu-deploy-gpu-virtual-machine.md#install-gpu-extension-after-deployment).
 
 ![Illustration showing 2 steps to use the "Plus Add Extension" button on the virtual machine "Details" pane to add a GPU extension to a VM on an Azure Stack Edge device.](media/azure-stack-edge-gpu-deploy-gpu-virtual-machine/add-extension-after-deployment-02.png)
 

articles/databox-online/azure-stack-edge-gpu-deploy-sample-module.md

@@ -7,13 +7,13 @@ author: alkohli
 ms.service: databox
 ms.subservice: edge
 ms.topic: how-to
-ms.date: 02/22/2021
+ms.date: 06/28/2022
 ms.author: alkohli
 ---
 
 # Deploy a GPU enabled IoT module on Azure Stack Edge Pro GPU device
 
-[!INCLUDE [applies-to-GPU-and-pro-r-skus](../../includes/azure-stack-edge-applies-to-gpu-pro-r-sku.md)]
+[!INCLUDE [applies-to-gpu-pro-pro2-and-pro-r-skus](../../includes/azure-stack-edge-applies-to-gpu-pro-pro-2-pro-r-sku.md)]
 
 This article describes how to deploy a GPU enabled IoT Edge module on your Azure Stack Edge Pro GPU device. 
 

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

@@ -7,14 +7,14 @@ author: alkohli
 ms.service: databox
 ms.subservice: edge
 ms.topic: how-to
-ms.date: 05/26/2022
+ms.date: 06/28/2022
 ms.author: alkohli
 #Customer intent: As an IT admin, I need to understand how install GPU extension on GPU virtual machines (VMs) on my Azure Stack Edge Pro GPU device.
 ---
 
 # Install GPU extension on VMs for your Azure Stack Edge Pro GPU device
 
-[!INCLUDE [applies-to-GPU-and-pro-r-skus](../../includes/azure-stack-edge-applies-to-gpu-pro-r-sku.md)]
+[!INCLUDE [applies-to-gpu-pro-pro2-and-pro-r-skus](../../includes/azure-stack-edge-applies-to-gpu-pro-pro-2-pro-r-sku.md)]
 
 This article describes how to install GPU driver extension to install appropriate Nvidia drivers on the GPU VMs running on your Azure Stack Edge device. The article covers installation steps for installing a GPU extension using Azure Resource Manager templates on both Windows and Linux VMs.
 

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

@@ -129,7 +129,7 @@ Follow these steps to create a VM after you've created a VM image.
     |Virtual machine name     | Enter a name for the new virtual machine.        |
     |Edge resource group     | Create a new resource group for all the resources associated with the VM.        |
     |Image     | Select from the VM images available on the device.        |
-    |Size     | Choose from the [Supported VM sizes](azure-stack-edge-gpu-virtual-machine-sizes.md).<br>For a GPU VM, select a [VM size from NCasT4-v3-series](azure-stack-edge-gpu-virtual-machine-sizes.md#ncast4_v3-series-preview). |
+    |Size     | Choose from the [Supported VM sizes](azure-stack-edge-gpu-virtual-machine-sizes.md).<br>For a GPU VM, select a [VM size from NCasT4-v3-series](azure-stack-edge-gpu-virtual-machine-sizes.md#n-series-gpu-optimized). |
     |Username     | Use the default username **azureuser** for the admin to sign in to the VM.        |
     |Authentication type    | Choose from an SSH public key or a user-defined password.       |
     |SSH public key | Displayed when you select the **SSH public key** authentication type. Paste in the SSH public key. |

articles/databox-online/azure-stack-edge-gpu-overview-gpu-virtual-machines.md

@@ -7,14 +7,14 @@ author: alkohli
 ms.service: databox
 ms.subservice: edge
 ms.topic: conceptual
-ms.date: 07/13/2021
+ms.date: 06/28/2022
 ms.author: alkohli
 #Customer intent: As an IT admin, I need to understand how to deploy and manage GPU-accelerated VM workloads on my Azure Stack Edge Pro GPU devices.
 ---
 
 # GPU virtual machines for Azure Stack Edge Pro GPU devices
 
-[!INCLUDE [applies-to-GPU-and-pro-r-skus](../../includes/azure-stack-edge-applies-to-gpu-pro-r-sku.md)]
+[!INCLUDE [applies-to-gpu-pro2-pro-r-and-mini-r-skus](../../includes/azure-stack-edge-applies-to-gpu-pro-pro-2-pro-r-sku.md)]
 
 GPU-accelerated workloads on an Azure Stack Edge Pro GPU device require a GPU virtual machine. This article provides an overview of GPU VMs, including supported OSs, GPU drivers, and VM sizes. Deployment options for GPU VMs used with Kubernetes clusters also are discussed.
 

articles/databox-online/azure-stack-edge-gpu-troubleshoot-virtual-machine-gpu-extension-installation.md

@@ -7,12 +7,12 @@ author: v-dalc
 ms.service: databox
 ms.subservice: edge
 ms.topic: how-to
-ms.date: 05/26/2022
+ms.date: 06/28/2022
 ms.author: alkohli
 ---
 # Troubleshoot GPU extension issues for GPU VMs on Azure Stack Edge Pro GPU
 
-[!INCLUDE [applies-to-gpu-and-pro-r-skus](../../includes/azure-stack-edge-applies-to-gpu-pro-r-sku.md)]
+[!INCLUDE [applies-to-gpu-pro-pro2-and-pro-r-skus](../../includes/azure-stack-edge-applies-to-gpu-pro-pro-2-pro-r-sku.md)]
 
 This article gives guidance for resolving the most common issues that cause installation of the GPU extension on a GPU VM to fail on an Azure Stack Edge Pro GPU device.
 
@@ -105,7 +105,7 @@ If the installation failed during the package download, that error indicates the
 
 **Error description:** A GPU VM must be either Standard_NC4as_T4_v3 or Standard_NC8as_T4_v3 size. If any other VM size is used, the GPU extension will fail to be attached.
 
-**Suggested solution:** Create a VM with the Standard_NC4as_T4_v3 or Standard_NC8as_T4_v3 VM size. For more information, see [Supported VM sizes for GPU VMs](azure-stack-edge-gpu-virtual-machine-sizes.md#ncast4_v3-series-preview). For information about specifying the size, see [Create GPU VMs](./azure-stack-edge-gpu-deploy-gpu-virtual-machine.md#create-gpu-vms).
+**Suggested solution:** Create a VM with the Standard_NC4as_T4_v3 or Standard_NC8as_T4_v3 VM size. For more information, see [Supported VM sizes for GPU VMs](azure-stack-edge-gpu-virtual-machine-sizes.md#n-series-gpu-optimized). For information about specifying the size, see [Create GPU VMs](./azure-stack-edge-gpu-deploy-gpu-virtual-machine.md#create-gpu-vms).
 
 
 ## Image OS is not supported

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

@@ -51,7 +51,7 @@ To figure out the size and the number of VMs that you can deploy on your device,
 
 For the usable compute and memory on your device, see the [Compute and memory specifications](azure-stack-edge-gpu-technical-specifications-compliance.md#compute-and-memory-specifications) for your device model. 
 
-For a GPU virtual machine, you must use a [VM size from the NCasT4-v3-series](azure-stack-edge-gpu-virtual-machine-sizes.md#ncast4_v3-series-preview).
+For a GPU virtual machine, you must use a [VM size from the NCasT4-v3-series](azure-stack-edge-gpu-virtual-machine-sizes.md#n-series-gpu-optimized).
 
 
 ### VM limits

articles/databox-online/azure-stack-edge-pro-2-deploy-install.md

@@ -208,7 +208,7 @@ Before you start cabling your device, you need the following things:
 - Your Azure Stack Edge Pro 2 physical device, unpacked, and rack mounted.
 - One power cable (included in the device package).
 - At least one 1-GbE RJ-45 network cable to connect to the Port 1. Port 1 and Port 2 the two 10/1-GbE network interfaces on your device.
-- One 100-GbE QSFP28 passive direct attached cable (tested in-house) for each data network interface Port 3 and Port 4 to be configured. Here is an example of the QSFP28 DAC connector: 
+- One 100-GbE QSFP28 passive direct attached cable (Microsoft validated) for each data network interface Port 3 and Port 4 to be configured. Here is an example of the QSFP28 DAC connector: 
 
     ![Example of a QSFP28 DAC connector](./media/azure-stack-edge-pro-2-deploy-install/qsfp28-dac-connector.png)
 
@@ -227,7 +227,7 @@ Before you start cabling your device, you need the following things:
 - One power cable for each device node (included in the device package).
 - Access to one power distribution unit for each device node.
 - At least two 1-GbE RJ-45 network cables per device to connect to Port 1 and Port2. These are the two 10/1-GbE network interfaces on your device. 
-- A 100-GbE QSFP28 passive direct attached cable (tested in-house) for each data network interface Port 3 and Port 4 to be configured on each device. The total number needed would depend on the network topology you will deploy. Here is an example QSFP28 DAC connector: 
+- A 100-GbE QSFP28 passive direct attached cable (Microsoft validated) for each data network interface Port 3 and Port 4 to be configured on each device. The total number needed would depend on the network topology you will deploy. Here is an example QSFP28 DAC connector: 
 
     ![Example of a QSFP28 DAC connector](./media/azure-stack-edge-pro-2-deploy-install/qsfp28-dac-connector.png)
 
@@ -243,25 +243,25 @@ Before you start cabling your device, you need the following things:
 
 ### Device front panel
 
-The front panel on Azure Stack Edge Pro 2 device:
+On your device:
 
-- The front panel has disk drives and a power button.
+- The front panel has disk drives and a power button. The front panel has:
 
     - Has six disk slots in the front of your device.
-    - Slots 0 to Slot 3 contain data disks. Slots 4 and 5 are empty.
+    - Has 2, 4, or 6 data disks in the 6 available slots depending on the specific hardware configuration.
 
     ![Disks and power button on the front plane of a device](./media/azure-stack-edge-pro-2-deploy-install/front-plane-labeled-1.png)
 
 ### Device back plane
 
-- The back plane of Azure Stack Edge Pro 2 device has:
+On your device:
 
-    ![Ports on the back plane of a device](./media/azure-stack-edge-pro-2-deploy-install/backplane-ports-1.png)
+- The back plane has:
 
     -  Four network interfaces:
 
         - Two 10/1-Gbps interfaces, Port 1 and Port 2.
-        - Two 100-Gbps interfaces, PORT 3 and PORT 4.
+        - Two 100-Gbps interfaces, Port 3 and Port 4.
 
     - A baseboard management controller (BMC).      
 
@@ -272,6 +272,9 @@ The front panel on Azure Stack Edge Pro 2 device:
 
     - Two Wi-Fi Sub miniature version A (SMA) connectors located on the faceplate of PCIe card slot located below Port 3 and Port 4. The Wi-Fi antennas are installed on these connectors.
 
+    - Two, one, or no Graphical Processing Units (GPUs).
+
+    ![Diagram that shows ports on the back plane of a device.](./media/azure-stack-edge-pro-2-deploy-install/backplane-ports-1.png)  
 
 
 ### Power cabling

articles/databox-online/azure-stack-edge-pro-2-overview.md

@@ -7,7 +7,7 @@ author: alkohli
 ms.service: databox
 ms.subservice: edge
 ms.topic: overview
-ms.date: 03/04/2022
+ms.date: 06/24/2022
 ms.author: alkohli
 #Customer intent: As an IT admin, I need to understand what Azure Stack Edge Pro 2 is and how it works so I can use it to process and transform data before sending to Azure.
 ---
@@ -34,7 +34,7 @@ Azure Stack Edge Pro 2 has the following capabilities:
 
 |Capability |Description  |
 |---------|---------|
-|Accelerated AI inferencing| Enabled by the compute acceleration card. Depending on your compute needs, you may choose a model that comes with or without Graphical Processing Units (GPUs). <br> For more information, see [GPU sharing on your Azure Stack Edge device](azure-stack-edge-gpu-sharing.md).|
+|Accelerated AI inferencing| Enabled by the compute acceleration card. Depending on your compute needs, you may choose a model that comes with one, two or no Graphical Processing Units (GPUs). <br> For more information, see [Technical specifications for Azure Stack Edge Pro 2](azure-stack-edge-pro-2-technical-specifications-compliance.md).|
 |Edge computing      |Supports VM and containerized workloads to allow analysis, processing, and filtering of data. <br>For information on VM workloads, see [VM overview on Azure Stack Edge](azure-stack-edge-gpu-virtual-machine-overview.md).<br>For containerized workloads, see [Kubernetes overview on Azure Stack Edge](azure-stack-edge-gpu-kubernetes-overview.md)</li></ul> |
 |Data access     | Direct data access from Azure Storage Blobs and Azure Files using cloud APIs for additional data processing in the cloud. Local cache on the device is used for fast access of most recently used files.|
 |Cloud-managed     |Device and service are managed via the Azure portal.|

articles/databox-online/azure-stack-edge-pro-2-safety.md

@@ -7,7 +7,7 @@ author:   sipastak
 ms.service: databox
 ms.subservice: edge
 ms.topic: conceptual
-ms.date: 03/02/2022
+ms.date: 06/24/2022
 ms.author: sipastak
 ---
 
@@ -124,6 +124,7 @@ This equipment is designed to operate in the following environment:
 * Relative humidity specifications
     * Storage: 5% to 95% relative humidity
     * Operating: 5% to 85% relative humidity
+      * For models with GPU(s), derate allowable max operating temperature by 1°C/210m (2.6°F/1000ft) above 950m (3,117ft).
 * Maximum altitude specifications
     * Operating: 3,050 meters (10,000 feet)
     * Storage: 9,150 meters (30,000 feet)