Merge pull request #223172 from Blackmist/npip-ga

Refactoring for no public IP GA

Author: 19BMG00

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

@@ -18,11 +18,11 @@ ms.date: 10/19/2022
 
 An Azure Machine Learning compute instance is a managed cloud-based workstation for data scientists.  Each compute instance has only one owner, although you can share files between multiple compute instances. 
 
-Compute instances make it easy to get started with Azure Machine Learning development as well as provide management and enterprise readiness capabilities for IT administrators.
+Compute instances make it easy to get started with Azure Machine Learning development and provide management and enterprise readiness capabilities for IT administrators.
 
 Use a compute instance as your fully configured and managed development environment in the cloud for machine learning. They can also be used as a compute target for training and inferencing for development and testing purposes.
 
-For compute instance Jupyter functionality to work, ensure that web socket communication is not disabled. Please ensure your network allows websocket connections to *.instances.azureml.net and *.instances.azureml.ms.
+For compute instance Jupyter functionality to work, ensure that web socket communication isn't disabled. Ensure your network allows websocket connections to *.instances.azureml.net and *.instances.azureml.ms.
 
 > [!IMPORTANT]
 > Items marked (preview) in this article are currently in public preview.
@@ -36,12 +36,12 @@ A compute instance is a fully managed cloud-based workstation optimized for your
 |Key benefits|Description|
 |----|----|
 |Productivity|You can build and deploy models using integrated notebooks and the following tools in Azure Machine Learning studio:<br/>-  Jupyter<br/>-  JupyterLab<br/>-  VS Code (preview)<br/>Compute instance is fully integrated with Azure Machine Learning workspace and studio. You can share notebooks and data with other data scientists in the workspace.<br/> 
-|Managed & secure|Reduce your security footprint and add compliance with enterprise security requirements. Compute instances  provide robust management policies and secure networking configurations such as:<br/><br/>- Autoprovisioning from Resource Manager templates or Azure Machine Learning SDK<br/>- [Azure role-based access control (Azure RBAC)](../role-based-access-control/overview.md)<br/>- [Virtual network support](./how-to-secure-training-vnet.md#compute-cluster)<br/> - Azure policy to disable SSH access<br/> - Azure policy to enforce creation in a virtual network <br/> - Auto-shutdown/auto-start based on schedule <br/>- TLS 1.2 enabled |
+|Managed & secure|Reduce your security footprint and add compliance with enterprise security requirements. Compute instances  provide robust management policies and secure networking configurations such as:<br/><br/>- Autoprovisioning from Resource Manager templates or Azure Machine Learning SDK<br/>- [Azure role-based access control (Azure RBAC)](../role-based-access-control/overview.md)<br/>- [Virtual network support](./how-to-secure-training-vnet.md)<br/> - Azure policy to disable SSH access<br/> - Azure policy to enforce creation in a virtual network <br/> - Auto-shutdown/auto-start based on schedule <br/>- TLS 1.2 enabled |
 |Preconfigured&nbsp;for&nbsp;ML|Save time on setup tasks with pre-configured and up-to-date ML packages, deep learning frameworks, GPU drivers.|
 |Fully customizable|Broad support for Azure VM types including GPUs and persisted low-level customization such as installing packages and drivers makes advanced scenarios a breeze. You can also use setup scripts to automate customization |
 
-* Secure your compute instance with **[No public IP (preview)](./how-to-secure-training-vnet.md)**.
-* The compute instance is also a secure training compute target similar to [compute clusters](how-to-create-attach-compute-cluster.md), but it is single node. 
+* Secure your compute instance with **[No public IP](./how-to-secure-training-vnet.md)**.
+* The compute instance is also a secure training compute target similar to [compute clusters](how-to-create-attach-compute-cluster.md), but it's single node. 
 * You can [create a compute instance](how-to-create-manage-compute-instance.md?tabs=python#create) yourself, or an administrator can **[create a compute instance on your behalf](how-to-create-manage-compute-instance.md?tabs=python#create-on-behalf-of-preview)**.
 * You can also **[use a setup script (preview)](how-to-customize-compute-instance.md)**  for an automated way to customize and configure the compute instance as per your needs.
 * To save on costs, **[create  a schedule](how-to-create-manage-compute-instance.md#schedule-automatic-start-and-stop)** to automatically start and stop the compute instance, or [enable idle shutdown](how-to-create-manage-compute-instance.md#enable-idle-shutdown-preview)
@@ -98,9 +98,9 @@ The files in the file share are accessible from all compute instances in the sam
 
 You can also clone the latest Azure Machine Learning samples to your folder under the user files directory in the workspace file share.
 
-Writing small files can be slower on network drives than writing to the compute instance local disk itself.  If you are writing many small files, try using a directory directly on the compute instance, such as a `/tmp` directory. Note these files will not be accessible from other compute instances.
+Writing small files can be slower on network drives than writing to the compute instance local disk itself.  If you're writing many small files, try using a directory directly on the compute instance, such as a `/tmp` directory. Note these files won't be accessible from other compute instances.
 
-Do not store training data on the notebooks file share. You can use the `/tmp` directory on the compute instance for your temporary data.  However, do not write very large files of data on the OS disk of the compute instance. OS disk on compute instance has 128 GB capacity. You can also store temporary training data on temporary disk mounted on /mnt. Temporary disk size is configurable based on the VM size chosen and can store larger amounts of data if a higher size VM is chosen. You can also mount [datastores and datasets](v1/concept-azure-machine-learning-architecture.md#datasets-and-datastores). Any software packages you install are saved on the OS disk of compute instance. Please note customer managed key encryption is currently not supported for OS disk. The OS disk for compute instance is encrypted with Microsoft-managed keys. 
+Don't store training data on the notebooks file share. You can use the `/tmp` directory on the compute instance for your temporary data.  However, don't write large files of data on the OS disk of the compute instance. OS disk on compute instance has 128-GB capacity. You can also store temporary training data on temporary disk mounted on /mnt. Temporary disk size is based on the VM size chosen and can store larger amounts of data if a higher size VM is chosen. You can also mount [datastores and datasets](v1/concept-azure-machine-learning-architecture.md#datasets-and-datastores). Any software packages you install are saved on the OS disk of compute instance. Note customer managed key encryption is currently not supported for OS disk. The OS disk for compute instance is encrypted with Microsoft-managed keys. 
 
 ## Create
 
@@ -118,9 +118,9 @@ Other ways to create a compute instance:
 * With [Azure Machine Learning SDK](how-to-create-manage-compute-instance.md?tabs=python#create)
 * From the [CLI extension for Azure Machine Learning](how-to-create-manage-compute-instance.md?tabs=azure-cli#create)
 
-The dedicated cores per region per VM family quota and total regional quota, which applies to compute instance creation, is unified and shared with Azure Machine Learning training compute cluster quota. Stopping the compute instance does not release quota to ensure you will be able to restart the compute instance. Please do not stop the compute instance through the OS terminal by doing a sudo shutdown.
+The dedicated cores per region per VM family quota and total regional quota, which applies to compute instance creation, is unified and shared with Azure Machine Learning training compute cluster quota. Stopping the compute instance doesn't release quota to ensure you'll be able to restart the compute instance. Don't stop the compute instance through the OS terminal by doing a sudo shutdown.
 
-Compute instance comes with P10 OS disk. Temp disk type depends on the VM size chosen. Currently, it is not possible to change the OS disk type.
+Compute instance comes with P10 OS disk. Temp disk type depends on the VM size chosen. Currently, it isn't possible to change the OS disk type.
 
 
 ## Compute target

articles/machine-learning/concept-secure-network-traffic-flow.md

@@ -115,7 +115,7 @@ When you create a compute instance or compute cluster, the following resources a
 * A Network Security Group with required outbound rules. These rules allow __inbound__ access from the Azure Machine Learning (TCP on port 44224) and Azure Batch service (TCP on ports 29876-29877).
 
     > [!IMPORTANT]
-    > If you use a firewall to block internet access into the VNet, you must configure the firewall to allow this traffic. For example, with Azure Firewall you can create user-defined routes. For more information, see [How to use Azure Machine Learning with a firewall](how-to-access-azureml-behind-firewall.md#inbound-configuration).
+    > If you use a firewall to block internet access into the VNet, you must configure the firewall to allow this traffic. For example, with Azure Firewall you can create user-defined routes. For more information, see [Configure inbound and outbound network traffic](how-to-access-azureml-behind-firewall.md).
 
 * A load balancer with a public IP.
 
@@ -126,7 +126,7 @@ Also allow __outbound__ access to the following service tags. For each tag, repl
 
 Data access from your compute instance or cluster goes through the private endpoint of the Storage Account for your VNet.
 
-If you use Visual Studio Code on a compute instance, you must allow other outbound traffic. For more information, see [How to use Azure Machine Learning with a firewall](how-to-access-azureml-behind-firewall.md).
+If you use Visual Studio Code on a compute instance, you must allow other outbound traffic. For more information, see [Configure inbound and outbound network traffic](how-to-access-azureml-behind-firewall.md).
 
 :::image type="content" source="./media/concept-secure-network-traffic-flow/compute-instance-and-cluster.png" alt-text="Diagram of traffic flow when using compute instance or cluster":::