acrolinx

Author: Larry Franks

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.
@@ -41,7 +41,7 @@ A compute instance is a fully managed cloud-based workstation optimized for your
 |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](./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. 
+* 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/how-to-run-jupyter-notebooks.md

@@ -40,13 +40,13 @@ Use the **Notebooks** section of your workspace to edit and run Jupyter notebook
 
 ## Edit a notebook
 
-To edit a notebook, open any notebook located in the **User files** section of your workspace. Click on the cell you wish to edit.  If you don't have any notebooks in this section, see [Create and manage files in your workspace](how-to-manage-files.md).
+To edit a notebook, open any notebook located in the **User files** section of your workspace. Select the cell you wish to edit.  If you don't have any notebooks in this section, see [Create and manage files in your workspace](how-to-manage-files.md).
 
 You can edit the notebook without connecting to a compute instance.  When you want to run the cells in the notebook, select or create a compute instance.  If you select a stopped compute instance, it will automatically start when you run the first cell.
 
 When a compute instance is running, you can also use code completion, powered by [Intellisense](https://code.visualstudio.com/docs/editor/intellisense), in any Python notebook.
 
-You can also launch Jupyter or JupyterLab from the notebook toolbar.  Azure Machine Learning does not provide updates and fix bugs from Jupyter or JupyterLab as they are Open Source products outside of the boundary of Microsoft Support.
+You can also launch Jupyter or JupyterLab from the notebook toolbar.  Azure Machine Learning doesn't provide updates and fix bugs from Jupyter or JupyterLab as they're Open Source products outside of the boundary of Microsoft Support.
 
 ## Focus mode
 
@@ -168,7 +168,7 @@ To run a notebook or a Python script, you first connect to a running [compute in
 
     :::image type="content" source="media/how-to-run-jupyter-notebooks/start-compute.png" alt-text="Start compute instance":::
 
-Once you are connected to a compute instance, use the toolbar to run all cells in the notebook, or Control + Enter to run a single selected cell. 
+Once you're connected to a compute instance, use the toolbar to run all cells in the notebook, or Control + Enter to run a single selected cell. 
 
 Only you can see and use the compute instances you create.  Your **User files** are stored separately from the VM and are shared among all compute instances in the workspace.
 
@@ -190,15 +190,15 @@ Select the tool to show the variable explorer window.
 
 ## Navigate with a TOC
 
-On the notebook toolbar, use the  **Table of contents** tool to display or hide the table of contents.  Start a markdown cell with a heading to add it to the table of contents. Click on an entry in the table to scroll to that cell in the notebook.  
+On the notebook toolbar, use the  **Table of contents** tool to display or hide the table of contents.  Start a markdown cell with a heading to add it to the table of contents. Select an entry in the table to scroll to that cell in the notebook.  
 
 :::image type="content" source="media/how-to-run-jupyter-notebooks/table-of-contents.png" alt-text="Screenshot: Table of contents in the notebook":::
 
 ## Change the notebook environment
 
 The notebook toolbar allows you to change the environment on which your notebook runs.  
 
-These actions will not change the notebook state or the values of any variables in the notebook:
+These actions won't change the notebook state or the values of any variables in the notebook:
 
 |Action  |Result  |
 |---------|---------| --------|
@@ -253,7 +253,7 @@ Similar to Jupyter Notebooks, Azure Machine Learning studio notebooks have a mod
 
 ### Command mode shortcuts
 
-A cell is in command mode when there is no text cursor prompting you to type. When a cell is in Command mode, you can edit the notebook as a whole but not type into individual cells. Enter command mode by pressing `ESC` or using the mouse to select outside of a cell's editor area.  The left border of the active cell is blue and solid, and its **Run** button is blue.
+A cell is in command mode when there's no text cursor prompting you to type. When a cell is in Command mode, you can edit the notebook as a whole but not type into individual cells. Enter command mode by pressing `ESC` or using the mouse to select outside of a cell's editor area.  The left border of the active cell is blue and solid, and its **Run** button is blue.
 
    :::image type="content" source="media/how-to-run-jupyter-notebooks/command-mode.png" alt-text="Notebook cell in command mode ":::
 
@@ -295,7 +295,7 @@ A cell is in command mode when there is no text cursor prompting you to type. Wh
 
 ### Edit mode shortcuts
 
-Edit mode is indicated by a text cursor prompting you to type in the editor area. When a cell is in edit mode, you can type into the cell. Enter edit mode by pressing `Enter` or using the mouse to select on a cell's editor area. The left border of the active cell is green and hatched, and its **Run** button is green. You also see the cursor prompt in the cell in Edit mode.
+Edit mode is indicated by a text cursor prompting you to type in the editor area. When a cell is in edit mode, you can type into the cell. Enter edit mode by pressing `Enter` or select a cell's editor area. The left border of the active cell is green and hatched, and its **Run** button is green. You also see the cursor prompt in the cell in Edit mode.
 
    :::image type="content" source="media/how-to-run-jupyter-notebooks/edit-mode.png" alt-text="Notebook cell in edit mode":::
 
@@ -335,9 +335,9 @@ Using the following keystroke shortcuts, you can more easily navigate and run co
 
 * **Connecting to a notebook**: If you can't connect to a notebook, ensure that web socket communication is **not** disabled. For compute instance Jupyter functionality to work, web socket communication must be enabled. Ensure your [network allows websocket connections](how-to-access-azureml-behind-firewall.md?tabs=ipaddress#microsoft-hosts) to *.instances.azureml.net and *.instances.azureml.ms. 
 
-* **Private endpoint**: When a compute instance is deployed in a workspace with a private endpoint, it can be only be [accessed from within virtual network](./how-to-secure-training-vnet.md). If you are using custom DNS or hosts file, add an entry for < instance-name >.< region >.instances.azureml.ms with the private IP address of your workspace private endpoint. For more information see the [custom DNS](./how-to-custom-dns.md?tabs=azure-cli) article.
+* **Private endpoint**: When a compute instance is deployed in a workspace with a private endpoint, it can be only be [accessed from within virtual network](./how-to-secure-training-vnet.md). If you're using custom DNS or hosts file, add an entry for < instance-name >.< region >.instances.azureml.ms with the private IP address of your workspace private endpoint. For more information, see the [custom DNS](./how-to-custom-dns.md?tabs=azure-cli) article.
 
-* **Kernel crash**: If your kernel crashed and was restarted, you can run the following command to look at jupyter log and find out more details: `sudo journalctl -u jupyter`. If kernel issues persist, consider using a compute instance with more memory.
+* **Kernel crash**: If your kernel crashed and was restarted, you can run the following command to look at Jupyter log and find out more details: `sudo journalctl -u jupyter`. If kernel issues persist, consider using a compute instance with more memory.
 
 * **Kernel not found** or **Kernel operations were disabled**: When using the default Python 3.8 kernel on a compute instance, you may get an error such as "Kernel not found" or "Kernel operations were disabled". To fix, use one of the following methods:
     * Create a new compute instance. This will use a new image where this problem has been resolved.
@@ -346,7 +346,7 @@ Using the following keystroke shortcuts, you can more easily navigate and run co
 
 * **Expired token**: If you run into an expired token issue, sign out of your Azure ML studio, sign back in, and then restart the notebook kernel.
 
-* **File upload limit**: When uploading a file through the notebook's file explorer, you are limited files that are smaller than 5TB. If you need to upload a file larger than this, we recommend that you use the SDK to upload the data to a datastore. For more information, see [Create data assets](how-to-create-data-assets.md?tabs=Python-SDK).
+* **File upload limit**: When uploading a file through the notebook's file explorer, you're limited files that are smaller than 5 TB. If you need to upload a file larger than this, we recommend that you use the SDK to upload the data to a datastore. For more information, see [Create data assets](how-to-create-data-assets.md?tabs=Python-SDK).
 
 ## Next steps