Merge pull request #220809 from sdgilley/sdg-rename-rstudio

RStudio Workbench -> Posit Workbench

Author: prmerger-automator[bot]

articles/machine-learning/concept-compute-instance.md

@@ -72,7 +72,7 @@ Following tools and environments are already installed on the compute instance:
 |----|:----:|
 |R kernel||
 
-You can [Add RStudio](how-to-create-manage-compute-instance.md#add-custom-applications-such-as-rstudio-preview) when you create the instance.
+You can [Add RStudio or Posit Workbench (formerly RStudio Workbench)](how-to-create-manage-compute-instance.md#add-custom-applications-such-as-rstudio-or-posit-workbench-preview) when you create the instance.
 
 |**PYTHON** tools & environments|Details|
 |----|----|
@@ -92,7 +92,7 @@ Python packages are all installed in the **Python 3.8 - AzureML** environment. C
 
 Notebooks and Python scripts are stored in the default storage account of your workspace in Azure file share.  These files are located under your “User files” directory. This storage makes it easy to share notebooks between compute instances. The storage account also keeps your notebooks safely preserved when you stop or delete a compute instance.
 
-The Azure file share account of your workspace is mounted as a drive on the compute instance. This drive is the default working directory for Jupyter, Jupyter Labs, and RStudio. This means that the notebooks and other files you create in Jupyter, JupyterLab, or RStudio are automatically stored on the file share and available to use in other compute instances as well.
+The Azure file share account of your workspace is mounted as a drive on the compute instance. This drive is the default working directory for Jupyter, Jupyter Labs, RStudio, and Posit Workbench. This means that the notebooks and other files you create in Jupyter, JupyterLab, RStudio, or Posit are automatically stored on the file share and available to use in other compute instances as well.
 
 The files in the file share are accessible from all compute instances in the same workspace. Any changes to these files on the compute instance will be reliably persisted back to the file share.
 

articles/machine-learning/concept-mlflow.md

@@ -47,7 +47,7 @@ You can also use MLflow to [Query & compare experiments and runs with MLflow](ho
 
 
 > [!IMPORTANT]
-> - MLflow in R support is limited to tracking experiment's metrics, parameters and models on Azure Machine Learning jobs. Interactive training on RStudio or Jupyter Notebooks with R kernels is not supported. Model management and registration is not supported using the MLflow R SDK. As an alternative, use Azure ML CLI or Azure ML studio for model registration and management. View the following [R example about using the MLflow tracking client with Azure Machine Learning](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/single-step/r).
+> - MLflow in R support is limited to tracking experiment's metrics, parameters and models on Azure Machine Learning jobs. Interactive training on RStudio, Posit (formerly RStudio Workbench) or Jupyter Notebooks with R kernels is not supported. Model management and registration is not supported using the MLflow R SDK. As an alternative, use Azure ML CLI or Azure ML studio for model registration and management. View the following [R example about using the MLflow tracking client with Azure Machine Learning](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/single-step/r).
 > - MLflow in Java support is limited to tracking experiment's metrics and parameters on Azure Machine Learning jobs. Artifacts and models can't be tracked using the MLflow Java SDK. As an alternative, use the `Outputs` folder in jobs along with the method `mlflow.save_model` to save models (or artifacts) you want to capture. View the following [Java example about using the MLflow tracking client with the Azure Machine Learning](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/single-step/java/iris).
 
 ## Model registries with MLflow

articles/machine-learning/concept-secure-code-best-practice.md

@@ -49,7 +49,7 @@ __Recommended actions__:
 
 ## Azure ML compute instance
 
-Azure Machine Learning compute instance hosts __Jupyter__ and __Jupyter Lab__. When using either, cells in a notebook or code in can output HTML documents or fragments that contain malicious code. When the output is rendered, the code can be executed. The same threats also apply when using __RStudio__ hosted on a compute instance.
+Azure Machine Learning compute instance hosts __Jupyter__ and __Jupyter Lab__. When using either, cells in a notebook or code in can output HTML documents or fragments that contain malicious code. When the output is rendered, the code can be executed. The same threats also apply when using __RStudio__ and __Posit Workbench (formerly RStudio Workbench)__ hosted on a compute instance.
 
 __Possible threats__:
 * Cross site scripting (XSS)

articles/machine-learning/how-to-access-azureml-behind-firewall.md

@@ -41,7 +41,7 @@ The following are well-known ports used by services listed in this article. If a
 | 80 | Unsecured web traffic (HTTP) |
 | 443 | Secured web traffic (HTTPS) |
 | 445 | SMB traffic used to access file shares in Azure File storage |
-| 8787 | Used when connecting to RStudio on a compute instance |
+| 8787 | Used when connecting to RStudio or Posit Workbench (formerly RStudio Workbench) on a compute instance |
 | 18881 | Used to connect to the language server to enable IntelliSense for notebooks on a compute instance. |
 
 ## Required public internet access
@@ -97,7 +97,7 @@ These rule collections are described in more detail in [What are some Azure Fire
     | **\*.anaconda.org** | Used to get repo data. |
     | **pypi.org** | Used to list dependencies from the default index, if any, and the index isn't overwritten by user settings. If the index is overwritten, you must also allow **\*.pythonhosted.org**. |
     | **cloud.r-project.org** | Used when installing CRAN packages for R development. |
-    | **ghcr.io**</br>**pkg-containers.githubusercontent.com** | Used by the Custom Applications feature on a compute instance to pull images from Github Container Repository (ghcr.io). For example, the RStudio Workbench image is hosted here. |
+    | **ghcr.io**</br>**pkg-containers.githubusercontent.com** | Used by the Custom Applications feature on a compute instance to pull images from Github Container Repository (ghcr.io). For example, the RStudio or Posit Workbench image is hosted here. |
     | **\*pytorch.org** | Used by some examples based on PyTorch. |
     | **\*.tensorflow.org** | Used by some examples based on Tensorflow. |
     | **\*vscode.dev**</br>**\*vscode-unpkg.net**</br>**\*vscode-cdn.net**</br>**\*vscodeexperiments.azureedge.net**</br>**default.exp-tas.com** | Required to access vscode.dev (Visual Studio Code for the Web) |

articles/machine-learning/how-to-access-terminal.md

@@ -43,7 +43,7 @@ To access the terminal:
 
 In addition to the steps above, you can also access the terminal from:
 
-* RStudio (See [Add RStudio](how-to-create-manage-compute-instance.md?tabs=python#setup-rstudio-workbench)): Select the **Terminal** tab on top left.
+* RStudio or Posit Workbench (formerly RStudio Workbench) (See [Add custom applications such as RStudio or Posit Workbench)](how-to-create-manage-compute-instance.md?tabs=python#add-custom-applications-such-as-rstudio-or-posit-workbench-preview)): Select the **Terminal** tab on top left.
 * Jupyter Lab:  Select the **Terminal** tile under the **Other** heading in the Launcher tab.
 * Jupyter:  Select **New>Terminal** on top right in the Files tab.
 * SSH to the machine, if you enabled SSH access when the compute instance was created.
@@ -68,9 +68,9 @@ To integrate Git with your Azure Machine Learning workspace, see  [Git integrati
 
  Install packages from a terminal window. Install Python packages into the **Python 3.8 - AzureML** environment.  Install R packages into the **R** environment.
 
-Or you can install packages directly in Jupyter Notebook or RStudio:
+Or you can install packages directly in Jupyter Notebook, RStudio, or Posit Workbench (formerly RStudio Workbench):
 
-* RStudio ([Add RStudio](how-to-create-manage-compute-instance.md#add-custom-applications-such-as-rstudio-preview)): Use the **Packages** tab on the bottom right, or the **Console** tab on the top left.  
+* RStudio or Posit Workbench(see [Add custom applications such as RStudio or Posit Workbench](how-to-create-manage-compute-instance.md#add-custom-applications-such-as-rstudio-or-posit-workbench-preview)): Use the **Packages** tab on the bottom right, or the **Console** tab on the top left.  
 * Python: Add install code and execute in a Jupyter Notebook cell.
 
 > [!NOTE]

articles/machine-learning/how-to-configure-private-link.md

@@ -264,7 +264,7 @@ In some situations, you may want to allow someone to connect to your secured wor
 > [!WARNING]
 > When connecting over the public endpoint while the workspace uses a private endpoint to communicate with other resources:
 > * __Some features of studio will fail to access your data__. This problem happens when the _data is stored on a service that is secured behind the VNet_. For example, an Azure Storage Account. 
-> * Using Jupyter, JupyterLab, and RStudio on a compute instance, including running notebooks, __is not supported__.
+> * Using Jupyter, JupyterLab, RStudio, or Posit Workbench (formerly RStudio Workbench) on a compute instance, including running notebooks, __is not supported__.
 
 To enable public access, use the following steps:
 

articles/machine-learning/how-to-create-manage-compute-instance.md

@@ -277,6 +277,7 @@ The data scientist can start, stop, and restart the compute instance. They can u
 * Jupyter
 * JupyterLab
 * RStudio
+* Posit Workbench (formerly RStudio Workbench)
 * Integrated notebooks
 
 ## Schedule automatic start and stop
@@ -537,42 +538,42 @@ arm_access_token = get_access_token_msi("https://management.azure.com")
 > [!NOTE]
 > To use Azure CLI with the managed identity for authentication, specify the identity client ID as the username when logging in: ```az login --identity --username $DEFAULT_IDENTITY_CLIENT_ID```.
 
-## Add custom applications such as RStudio (preview)
+## Add custom applications such as RStudio or Posit Workbench (preview)
 
 > [!IMPORTANT]
 > Items marked (preview) below 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/).
 
-You can set up other applications, such as RStudio, when creating a compute instance. Follow these steps in studio to set up a custom application on your compute instance
+You can set up other applications, such as RStudio, or Posit Workbench (formerly RStudio Workbench), when creating a compute instance. Follow these steps in studio to set up a custom application on your compute instance
 
 1.	Fill out the form to [create a new compute instance](?tabs=azure-studio#create)
 1.	Select **Next: Advanced Settings**
 1.	Select **Add application** under the **Custom application setup (RStudio Workbench, etc.)** section
 
 :::image type="content" source="media/how-to-create-manage-compute-instance/custom-service-setup.png" alt-text="Screenshot showing Custom Service Setup.":::
 
-### Setup RStudio Workbench
+### Setup Posit Workbench (formerly RStudio Workbench)
 
-RStudio is one of the most popular IDEs among R developers for ML and data science projects. You can easily set up RStudio Workbench to run on your compute instance, using your own RStudio license, and access the rich feature set that RStudio Workbench offers.
+Posit is one of the most popular IDEs among R developers for ML and data science projects. You can easily set up Posit Workbench to run on your compute instance, using your own Posit license, and access the rich feature set that Posit Workbench offers.
 
 1.	Follow the steps listed above to **Add application** when creating your compute instance.
-1.	Select **RStudio Workbench (bring your own license)** in the **Application** dropdown and enter your RStudio Workbench license key in the **License key** field. You can get your RStudio Workbench license or trial license [from RStudio](https://www.rstudio.com/). 
-1. Select **Create** to add RStudio Workbench application to your compute instance.
+1.	Select **Posit Workbench (bring your own license)** in the **Application** dropdown and enter your Posit Workbench license key in the **License key** field. You can get your Posit Workbench license or trial license [from posit](https://posit.co). 
+1. Select **Create** to add Posit Workbench application to your compute instance.
 
-:::image type="content" source="media/how-to-create-manage-compute-instance/rstudio-workbench.png" alt-text="Screenshot shows RStudio settings." lightbox="media/how-to-create-manage-compute-instance/rstudio-workbench.png":::
+:::image type="content" source="media/how-to-create-manage-compute-instance/rstudio-workbench.png" alt-text="Screenshot shows Posit Workbench settings." lightbox="media/how-to-create-manage-compute-instance/rstudio-workbench.png":::
 
 [!INCLUDE [private link ports](../../includes/machine-learning-private-link-ports.md)]
 
 > [!NOTE]
-> * Support for accessing your workspace file store from RStudio is not yet available.
-> * When accessing multiple instances of RStudio, if you see a "400 Bad Request. Request Header Or Cookie Too Large" error, use a new browser or access from a browser in incognito mode.
-> * Shiny applications are not currently supported on RStudio Workbench.
+> * Support for accessing your workspace file store from Posit Workbench is not yet available.
+> * When accessing multiple instances of Posit Workbench, if you see a "400 Bad Request. Request Header Or Cookie Too Large" error, use a new browser or access from a browser in incognito mode.
+> * Shiny applications are not currently supported on Posit Workbench.
  
 
-### Setup RStudio open source
+### Setup RStudio (open source)
 
-To use RStudio open source, set up a custom application as follows:
+To use RStudio, set up a custom application as follows:
 
 1.	Follow the steps listed above to **Add application** when creating your compute instance.
 1.	Select **Custom Application** on the **Application** dropdown 
@@ -620,7 +621,7 @@ Access the custom applications that you set up in studio:
 > [!NOTE]
 > It might take a few minutes after setting up a custom application until you can access it via the links above. The amount of time taken will depend on the size of the image used for your custom application. If you see a 502 error message when trying to access the application, wait for some time for the application to be set up and try again.
 
-Once you launch **RStudio**, you may not see any of your files, even after specifying the correct **Bind mounts** above.  If this happens:
+Once you launch **RStudio** or **Posit Workbench**, you may not see any of your files, even after specifying the correct **Bind mounts** above.  If this happens:
 
 1. Select the **...** at the far right of the Files pane
 1. For the **Path to folder**, type `/home/azureuser/cloudfiles/code`

articles/machine-learning/v1/how-to-configure-private-link.md

@@ -190,7 +190,7 @@ In some situations, you may want to allow someone to connect to your secured wor
 > [!WARNING]
 > When connecting over the public endpoint while the workspace uses a private endpoint to communicate with other resources:
 > * __Some features of studio will fail to access your data__. This problem happens when the _data is stored on a service that is secured behind the VNet_. For example, an Azure Storage Account. 
-> * Using Jupyter, JupyterLab, and RStudio on a compute instance, including running notebooks, __is not supported__.
+> * Using Jupyter, JupyterLab, RStudio, or Posit Workbench (formerly RStudio Workbench) on a compute instance, including running notebooks, __is not supported__.
 
 To enable public access, use the following steps:
 

articles/machine-learning/v1/how-to-create-manage-compute-instance.md

@@ -201,7 +201,7 @@ In the examples below, the name of the compute instance is **instance**
 
 ---
 
-[Azure RBAC](../../role-based-access-control/overview.md) allows you to control which users in the workspace can create, delete, start, stop, restart a compute instance. All users in the workspace contributor and owner role can create, delete, start, stop, and restart compute instances across the workspace. However, only the creator of a specific compute instance, or the user assigned if it was created on their behalf, is allowed to access Jupyter, JupyterLab, and RStudio on that compute instance. A compute instance is dedicated to a single user who has root access.  That user has access to Jupyter/JupyterLab/RStudio running on the instance. Compute instance will have single-user sign in and all actions will use that user’s identity for Azure RBAC and attribution of experiment runs. SSH access is controlled through public/private key mechanism.
+[Azure RBAC](../../role-based-access-control/overview.md) allows you to control which users in the workspace can create, delete, start, stop, restart a compute instance. All users in the workspace contributor and owner role can create, delete, start, stop, and restart compute instances across the workspace. However, only the creator of a specific compute instance, or the user assigned if it was created on their behalf, is allowed to access Jupyter, JupyterLab, RStudio, and Posit Workbench (formerly RStudio Workbench) on that compute instance. A compute instance is dedicated to a single user who has root access.  That user has access to Jupyter/JupyterLab/RStudio/Posit Workbench running on the instance. Compute instance will have single-user sign in and all actions will use that user’s identity for Azure RBAC and attribution of experiment runs. SSH access is controlled through public/private key mechanism.
 
 These actions can be controlled by Azure RBAC:
 * *Microsoft.MachineLearningServices/workspaces/computes/read*

includes/machine-learning-private-link-ports.md

@@ -7,4 +7,4 @@ ms.author: sgilley
 ---
 
 > [!IMPORTANT]
-> If using a private link workspace, ensure that the docker image, pkg-containers.githubusercontent.com and ghcr.io are accessible. Also, use a published port in the range 8704-8993. For RStudio Workbench, ensure that the license is accessible by providing network access to https://www.wyday.com. 
+> If using a private link workspace, ensure that the docker image, pkg-containers.githubusercontent.com and ghcr.io are accessible. Also, use a published port in the range 8704-8993. For Posit Workbench (formerly RStudio Workbench), ensure that the license is accessible by providing network access to https://www.wyday.com.