Merge pull request #213676 from jesscioffi/main

Updates for hiding of Ubuntu 18

Author: Stacyrch140

articles/machine-learning/data-science-virtual-machine/dsvm-tools-data-platforms.md

@@ -6,10 +6,10 @@ keywords: data science tools, data science virtual machine, tools for data scien
 services: machine-learning
 ms.service: data-science-vm
 
-author: timoklimmer
-ms.author: tklimmer
+author: jesscioffi
+ms.author: jcioffi
 ms.topic: conceptual
-ms.date: 04/29/2021
+ms.date: 10/04/2022
 
 ---
 
@@ -24,7 +24,7 @@ The following data platform tools are supported on the DSVM.
 | Category | Value |
 | ------------- | ------------- |
 | What is it?   | A local relational database instance      |
-| Supported DSVM editions      | Windows 2019, Ubuntu 18.04 (SQL Server 2019)   |
+| Supported DSVM editions      | Windows 2019, Linux (SQL Server 2019)   |
 | Typical uses      | <ul><li>Rapid development locally with smaller dataset</li><li>Run In-database R</li></ul> |
 | Links to samples      | <ul><li>A small sample of a New York City dataset is loaded into the SQL database:<br/>  `nyctaxi`</li><li>Jupyter sample showing Microsoft Machine Learning Server and in-database analytics can be found at:<br/> `~notebooks/SQL_R_Services_End_to_End_Tutorial.ipynb`</li></ul> |
 | Related tools on the DSVM       | <ul><li>SQL Server Management Studio</li><li>ODBC/JDBC drivers</li><li>pyodbc, RODBC</li></ul> |
@@ -114,4 +114,4 @@ Libraries to access data from Azure Blob storage or Azure Data Lake Storage, usi
 
 For the Spark instance on the DSVM to access data stored in Blob storage or Azure Data Lake Storage, you must create and configure the `core-site.xml` file based on the template found in $SPARK_HOME/conf/core-site.xml.template. You must also have the appropriate credentials to access Blob storage and Azure Data Lake Storage. (Note that the template files use placeholders for Blob storage and Azure Data Lake Storage configurations.)
 
-For more detailed info about creating Azure Data Lake Storage service credentials, see [Authentication with Azure Data Lake Storage Gen1](../../data-lake-store/data-lake-store-service-to-service-authenticate-using-active-directory.md). After the credentials for Blob storage or Azure Data Lake Storage are entered in the core-site.xml file, you can reference the data stored in those sources through the URI prefix of wasb:// or adl://.
+For more detailed info about creating Azure Data Lake Storage service credentials, see [Authentication with Azure Data Lake Storage Gen1](../../data-lake-store/data-lake-store-service-to-service-authenticate-using-active-directory.md). After the credentials for Blob storage or Azure Data Lake Storage are entered in the core-site.xml file, you can reference the data stored in those sources through the URI prefix of wasb:// or adl://.

articles/machine-learning/data-science-virtual-machine/dsvm-tools-deep-learning-frameworks.md

@@ -22,15 +22,15 @@ Deep learning frameworks on the DSVM are listed below.
 | Category | Value |
 |--|--|
 | Version(s) supported | 11 |
-| Supported DSVM editions | Windows Server 2019<br>Ubuntu 18.04 |
+| Supported DSVM editions | Windows Server 2019<br>Linux |
 | How is it configured / installed on the DSVM? | _nvidia-smi_ is available on the system path. |
 | How to run it | Open a command prompt (on Windows) or a terminal (on Linux), and then run _nvidia-smi_. |
 ## [Horovod](https://github.com/uber/horovod)
 
 | Category | Value |
 | ------------- | ------------- |
 | Version(s) supported | 0.21.3|
-| Supported DSVM editions      | Ubuntu 18.04 |
+| Supported DSVM editions      | Linux |
 | How is it configured / installed on the DSVM?  | Horovod is installed in Python 3.5 |
 | How to run it      | Activate the correct environment at the terminal, and then run Python. |
 
@@ -40,7 +40,7 @@ Deep learning frameworks on the DSVM are listed below.
 | Category | Value |
 |--|--|
 | Version(s) supported |  |
-| Supported DSVM editions | Windows Server 2019<br>Ubuntu 18.04 |
+| Supported DSVM editions | Windows Server 2019<br>Linux |
 | What is it for? | NVIDIA tool for querying GPU activity |
 | How is it configured / installed on the DSVM? | `nvidia-smi` is on the system path. |
 | How to run it | On a virtual machine **with GPU's**, open a command prompt (on Windows) or a terminal (on Linux), and then run `nvidia-smi`. |
@@ -49,8 +49,8 @@ Deep learning frameworks on the DSVM are listed below.
 
 | Category | Value |
 |--|--|
-| Version(s) supported | 1.9.0 (Ubuntu 18.04, Windows 2019) |
-| Supported DSVM editions | Windows Server 2019<br>Ubuntu 18.04 |
+| Version(s) supported | 1.9.0 (Linux, Windows 2019) |
+| Supported DSVM editions | Windows Server 2019<br>Linux |
 | How is it configured / installed on the DSVM? | Installed in Python, conda environments 'py38_default', 'py38_pytorch' |
 | How to run it | Terminal: Activate the correct environment, and then run Python.<br/>* [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-ubuntu-data-science-virtual-machine): Connect, and then open the PyTorch directory for samples. |
 
@@ -59,6 +59,6 @@ Deep learning frameworks on the DSVM are listed below.
 | Category | Value |
 |--|--|
 | Version(s) supported | 2.5 |
-| Supported DSVM editions | Windows Server 2019<br>Ubuntu 18.04 |
+| Supported DSVM editions | Windows Server 2019<br>Linux |
 | How is it configured / installed on the DSVM? | Installed in Python, conda environments 'py38_default', 'py38_tensorflow' |
 | How to run it | Terminal: Activate the correct environment, and then run Python. <br/> * Jupyter: Connect to [Jupyter](provision-vm.md) or [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-ubuntu-data-science-virtual-machine), and then open the TensorFlow directory for samples. |

articles/machine-learning/data-science-virtual-machine/dsvm-tools-development.md

@@ -43,6 +43,6 @@ The Data Science Virtual Machine (DSVM) bundles several popular tools in a highl
 | Category | Value |
 |--|--|
 | What is it? | Client IDE for Python language |
-| Supported DSVM versions | Windows 2019, Ubuntu 18.04 |
+| Supported DSVM versions | Windows 2019, Linux |
 | Typical uses | Python development |
 | How to use and run it | Desktop shortcut (`C:\Program Files\tk`) on Windows. Desktop shortcut (`/usr/bin/pycharm`) on Linux |

articles/machine-learning/data-science-virtual-machine/dsvm-tools-languages.md

@@ -25,7 +25,7 @@ artificial intelligence (AI) applications. Here are some of the notable ones.
 | Category | Value |
 |--|--|
 | Language versions supported | Python 3.8 |
-| Supported DSVM editions | Windows Server 2019, Ubuntu 18.04 |
+| Supported DSVM editions | Windows Server 2019, Linux |
 | How is it configured / installed on the DSVM? | There is multiple `conda` environments whereby each of these has different Python packages pre-installed. To list all available environments in your machine, run `conda env list`. |
 
 ### How to use and run it

articles/machine-learning/data-science-virtual-machine/dsvm-tools-productivity.md

@@ -15,7 +15,7 @@ ms.date: 05/12/2021
 
 In addition to the data science and programming tools, the DSVM contains productivity tools to help you capture and share insights with your colleagues. Microsoft 365 is the most productive and most secure Office experience for enterprises, allowing your teams to work together seamlessly from anywhere, anytime. With Power BI Desktop you can go from data to insight to action. And the Microsoft Edge browser is a modern, fast, and secure Web browser. 
 
-| Tool | Windows 2019 Server DSVM | Ubuntu 18.04 DSVM | Usage notes |
+| Tool | Windows 2019 Server DSVM | Linux DSVM | Usage notes |
 |--|:-:|:-:|:-|
 | [Microsoft 365](https://www.microsoft.com/microsoft-365) (Word, Excel, PowerPoint) | <span class='green-check'>&#9989; </span> | <span class='red-x'>&#10060; </span> |  |
 | [Microsoft Teams](https://www.microsoft.com/microsoft-teams) | <span class='green-check'>&#9989; </span> | <span class='red-x'>&#10060; </span> |  |

articles/machine-learning/data-science-virtual-machine/dsvm-tutorial-bicep.md

@@ -13,7 +13,7 @@ ms.custom: subject-armqs, mode-arm
 
 # Quickstart: Create an Ubuntu Data Science Virtual Machine using Bicep
 
-This quickstart will show you how to create an Ubuntu 18.04 Data Science Virtual Machine using Bicep. Data Science Virtual Machines are cloud-based virtual machines preloaded with a suite of data science and machine learning frameworks and tools. When deployed on GPU-powered compute resources, all tools and libraries are configured to use the GPU.
+This quickstart will show you how to create an Ubuntu Data Science Virtual Machine using Bicep. Data Science Virtual Machines are cloud-based virtual machines preloaded with a suite of data science and machine learning frameworks and tools. When deployed on GPU-powered compute resources, all tools and libraries are configured to use the GPU.
 
 [!INCLUDE [About Bicep](../../../includes/resource-manager-quickstart-bicep-introduction.md)]
 
@@ -34,7 +34,7 @@ The following resources are defined in the Bicep file:
 * [Microsoft.Network/virtualNetworks](/azure/templates/microsoft.network/virtualnetworks)
 * [Microsoft.Network/publicIPAddresses](/azure/templates/microsoft.network/publicipaddresses)
 * [Microsoft.Storage/storageAccounts](/azure/templates/microsoft.storage/storageaccounts)
-* [Microsoft.Compute/virtualMachines](/azure/templates/microsoft.compute/virtualmachines): Create a cloud-based virtual machine. In this template, the virtual machine is configured as a Data Science Virtual Machine running Ubuntu 18.04.
+* [Microsoft.Compute/virtualMachines](/azure/templates/microsoft.compute/virtualmachines): Create a cloud-based virtual machine. In this template, the virtual machine is configured as a Data Science Virtual Machine running Ubuntu.
 
 ## Deploy the Bicep file
 

articles/machine-learning/data-science-virtual-machine/dsvm-tutorial-resource-manager.md

@@ -13,7 +13,7 @@ ms.custom: subject-armqs, mode-arm
 
 # Quickstart: Create an Ubuntu Data Science Virtual Machine using an ARM template
 
-This quickstart will show you how to create an Ubuntu 18.04 Data Science Virtual Machine using an Azure Resource Manager template (ARM template). Data Science Virtual Machines are cloud-based virtual machines preloaded with a suite of data science and machine learning frameworks and tools. When deployed on GPU-powered compute resources, all tools and libraries are configured to use the GPU.
+This quickstart will show you how to create an Ubuntu Data Science Virtual Machine using an Azure Resource Manager template (ARM template). Data Science Virtual Machines are cloud-based virtual machines preloaded with a suite of data science and machine learning frameworks and tools. When deployed on GPU-powered compute resources, all tools and libraries are configured to use the GPU.
 
 [!INCLUDE [About Azure Resource Manager](../../../includes/resource-manager-quickstart-introduction.md)]
 
@@ -40,7 +40,7 @@ The following resources are defined in the template:
 * [Microsoft.Network/virtualNetworks](/azure/templates/microsoft.network/virtualnetworks)
 * [Microsoft.Network/publicIPAddresses](/azure/templates/microsoft.network/publicipaddresses)
 * [Microsoft.Storage/storageAccounts](/azure/templates/microsoft.storage/storageaccounts)
-* [Microsoft.Compute/virtualMachines](/azure/templates/microsoft.compute/virtualmachines): Create a cloud-based virtual machine. In this template, the virtual machine is configured as a Data Science Virtual Machine running Ubuntu 18.04.
+* [Microsoft.Compute/virtualMachines](/azure/templates/microsoft.compute/virtualmachines): Create a cloud-based virtual machine. In this template, the virtual machine is configured as a Data Science Virtual Machine running Ubuntu.
 
 ## Deploy the template
 

articles/machine-learning/data-science-virtual-machine/dsvm-ubuntu-intro.md

@@ -13,21 +13,26 @@ ms.custom: mode-other
 
 # Quickstart: Set up the Data Science Virtual Machine for Linux (Ubuntu)
 
-Get up and running with the Ubuntu 18.04 and Ubuntu 20.04 Data Science Virtual Machines.
+> [!IMPORTANT]
+> Items marked (preview) in this article are currently in public preview.
+> The preview version is provided without a service level agreement, and it's not recommended for production workloads. Certain features might not be supported or might have constrained capabilities.
+> For more information, see [Supplemental Terms of Use for Microsoft Azure Previews](https://azure.microsoft.com/support/legal/preview-supplemental-terms/).
+
+Get up and running with the Ubuntu 20.04 Data Science Virtual Machine and Azure DSVM for PyTorch (preview).
 
 ## Prerequisites
 
-To create an Ubuntu 18.04 or Ubuntu 20.04 Data Science Virtual Machine, you must have an Azure subscription. [Try Azure for free](https://azure.com/free).
+To create an Ubuntu 20.04 Data Science Virtual Machine or an Azure DSVM for PyTorch, you must have an Azure subscription. [Try Azure for free](https://azure.com/free).
 
 >[!NOTE]
 >Azure free accounts don't support GPU enabled virtual machine SKUs.
 
 ## Create your Data Science Virtual Machine for Linux
 
-Here are the steps to create an instance of the Data Science Virtual Machine from Ubuntu 18.04 or Ubuntu 20.04:
+Here are the steps to create an instance of the Ubuntu 20.04 Data Science Virtual Machine or the Azure DSVM for PyTorch:
 
 1. Go to the [Azure portal](https://portal.azure.com). You might be prompted to sign in to your Azure account if you're not already signed in.
-1. Find the virtual machine listing by typing in "data science virtual machine" and selecting "Data Science Virtual Machine- Ubuntu 18.04" or "Data Science Virtual Machine- Ubuntu 20.04"
+1. Find the virtual machine listing by typing in "data science virtual machine" and selecting "Data Science Virtual Machine- Ubuntu 20.04" or "Azure DSVM for PyTorch (preview)"
 
 1. On the next window, select **Create**.
 
@@ -72,7 +77,7 @@ You can access the Ubuntu DSVM in one of three ways:
 
 ### SSH
 
-If you configured your VM with SSH authentication, you can logon using the account credentials that you created in the **Basics** section of step 3 for the text shell interface. On Windows, you can download an SSH client tool like [PuTTY](https://www.putty.org). If you prefer a graphical desktop (X Window System), you can use X11 forwarding on PuTTY.
+If you configured your VM with SSH authentication, you can log on using the account credentials that you created in the **Basics** section of step 3 for the text shell interface. On Windows, you can download an SSH client tool like [PuTTY](https://www.putty.org). If you prefer a graphical desktop (X Window System), you can use X11 forwarding on PuTTY.
 
 > [!NOTE]
 > The X2Go client performed better than X11 forwarding in testing. We recommend using the X2Go client for a graphical desktop interface.

articles/machine-learning/data-science-virtual-machine/linux-dsvm-walkthrough.md

@@ -32,7 +32,7 @@ Before you can use a Linux DSVM, you must have the following prerequisites:
 The [spambase](https://archive.ics.uci.edu/ml/datasets/spambase) dataset is a relatively small set of data that contains 4,601 examples. The dataset is a convenient size for demonstrating some of the key features of the DSVM because it keeps the resource requirements modest.
 
 > [!NOTE]
-> This walkthrough was created by using a D2 v2-size Linux DSVM (Ubuntu 18.04 Edition). You can use a DSVM this size to complete the procedures that are demonstrated in this walkthrough.
+> This walkthrough was created by using a D2 v2-size Linux DSVM. You can use a DSVM this size to complete the procedures that are demonstrated in this walkthrough.
 
 If you need more storage space, you can create additional disks and attach them to your DSVM. The disks use persistent Azure storage, so their data is preserved even if the server is reprovisioned due to resizing or is shut down. To add a disk and attach it to your DSVM, complete the steps in [Add a disk to a Linux VM](../../virtual-machines/linux/add-disk.md?toc=%2fazure%2fvirtual-machines%2flinux%2ftoc.json). The steps for adding a disk use the Azure CLI, which is already installed on the DSVM. You can complete the steps entirely from the DSVM itself. Another option to increase storage is to use [Azure Files](../../storage/files/storage-how-to-use-files-linux.md).
 

articles/machine-learning/data-science-virtual-machine/overview.md

@@ -25,7 +25,6 @@ The Data Science Virtual Machine (DSVM) is a customized VM image on the Azure cl
 The DSVM is available on:
 
 + Windows Server 2019
-+ Ubuntu 18.04 LTS
 + Ubuntu 20.04 LTS
 
 Additionally, we are excited to offer Azure DSVM for PyTorch (preview), which is an Ubuntu 20.04 image from Azure Marketplace that is optimized for large, distributed deep learning workloads. It comes preinstalled and validated with the latest PyTorch version to reduce setup costs and accelerate time to value. It comes packaged with various optimization functionalities (ONNX Runtime​, DeepSpeed​, MSCCL​, ORTMoE​, Fairscale​, Nvidia Apex​), as well as an up-to-date stack with the latest compatible versions of Ubuntu, Python, PyTorch, CUDA.