Merge pull request #268702 from TimShererWithAquent/us194307d

Freshness Pass 194290 (fourth PR):

Author: Stacyrch140

articles/lab-services/class-type-jupyter-notebook.md

@@ -5,67 +5,70 @@ description: Learn how to set up a lab VM in Azure Lab Services to teach data sc
 services: lab-services
 ms.service: lab-services
 ms.custom: devx-track-python
-author: ntrogh
-ms.author: nicktrog
+author: RoseHJM
+ms.author: rosemalcolm
 ms.topic: how-to
-ms.date: 02/17/2023
+ms.date: 03/12/2024
+#customer intent: As an educator, I want to configure a lab using Azure Lab Services so that students can practice with Jupyter Notebooks.
 ---
 
 # Set up a lab to teach data science with Python and Jupyter Notebooks
 
-[!INCLUDE [preview note](./includes/lab-services-new-update-focused-article.md)]
+This article describes how to set up a template virtual machine (VM) in Azure Lab Services that includes tools for teaching students to use Jupyter Notebooks. You also learn how lab users can connect to notebooks on their virtual machines.
 
-This article outlines how to set up a [template virtual machine (VM)](./classroom-labs-concepts.md#template-virtual-machine) in Azure Lab Services with the tools for teaching students to use Jupyter Notebooks. You also learn how to lab users can connect to notebooks on their virtual machines.
+Jupyter Notebooks is an open-source project that enables you to easily combine rich text and executable Python source code on a single canvas, known as a *notebook*. Run a notebook to create a linear record of inputs and outputs. Those outputs can include text, tables of information, scatter plots, and more.
 
-[Jupyter Notebooks](https://jupyter-notebook.readthedocs.io/) is an open-source project that enables you to easily combine rich text and executable Python source code on a single canvas, known as a notebook. Running a notebook results in a linear record of inputs and outputs. Those outputs can include text, tables of information, scatter plots, and more.
+[!INCLUDE [preview note](./includes/lab-services-new-update-focused-article.md)]
 
-## Lab configuration
+## Prerequisites
 
-[!INCLUDE [must have subscription](./includes/lab-services-class-type-subscription.md)]
+- [!INCLUDE [must have subscription](./includes/lab-services-class-type-subscription.md)]
 
-### Lab plan settings
+## Configure lab plan settings
 
 [!INCLUDE [must have lab plan](./includes/lab-services-class-type-lab-plan.md)]
 
-This lab uses one of the Data Science Virtual Machine Azure Marketplace images as the base VM image. You first need to enable these images in your lab plan. This lets lab creators then select the image as a base image for their lab.
+This lab uses one of the Data Science Virtual Machine images as the base VM image. These images are available in Azure Marketplace. This option lets lab creators then select the image as a base image for their lab. You need to enable these images in your lab plan.
+
+Follow these steps to [enable these Azure Marketplace images available to lab creators](specify-marketplace-images.md).
 
-1. Follow these steps to [enable these Azure Marketplace images available to lab creators](specify-marketplace-images.md). Select one of the following Azure Marketplace images, depending on your OS requirements:
+- Select one of the following Azure Marketplace images, depending on your operating system requirements:
 
-    - **Data Science Virtual Machine – Windows Server 2019**
-    - **Data Science Virtual Machine – Ubuntu 18.04**
-    
-1. Alternately, create a custom VM image:
+  - **Data Science Virtual Machine – Windows Server 2019/Windows Server 2022**
+  - **Data Science Virtual Machine – Ubuntu 20.04**
 
-    The Data Science VMs images in the Azure Marketplace are already configured with Jupyter Notebooks. These images, however, also include many other development and modeling tools for data science. If you don't want those extra tools and want a lightweight setup with only Jupyter notebooks, create a custom VM image. For an example, [Installing JupyterHub on Azure](http://tljh.jupyter.org/en/latest/install/azure.html). 
+- Alternately, create a custom VM image:
 
-    After you create the custom image, upload the image to a compute gallery to use it with Azure Lab Services. Learn more about [using compute gallery in Azure Lab Services](how-to-attach-detach-shared-image-gallery.md).
+  The Data Science VM images in the Azure Marketplace are already configured with [Jupyter Notebooks](https://jupyter-notebook.readthedocs.io/). These images also include other development and modeling tools for data science. If you don't need those extra tools and want a lightweight setup with only Jupyter notebooks, create a custom VM image. For an example, see [Installing JupyterHub on Azure](http://tljh.jupyter.org/en/latest/install/azure.html).
 
-### Lab settings
+  After you create the custom image, upload the image to a compute gallery to use it with Azure Lab Services. Learn more about [using compute gallery in Azure Lab Services](how-to-attach-detach-shared-image-gallery.md).
 
-1. Create a lab for your lab plan:
+## Create a lab
 
-    [!INCLUDE [create lab](./includes/lab-services-class-type-lab.md)] Specify the following lab settings:
+- Create a lab for your lab plan:
 
-    | Lab settings | Value |
-    | ------------ | ------------------ |
-    | Virtual machine size | Select **Small** or **Medium** for a basic setup accessing Jupyter Notebooks. Select **Alternative Small GPU (Compute)** for compute-intensive and network-intensive applications used in Artificial Intelligence and Deep Learning classes. |
-    | Virtual machine image | Choose **[Data Science Virtual Machine – Windows Server 2019](https://azuremarketplace.microsoft.com/marketplace/apps/microsoft-dsvm.dsvm-win-2019)** or **[Data Science Virtual Machine – Ubuntu](https://azuremarketplace.microsoft.com/marketplace/apps?search=Data%20science%20Virtual%20machine&page=1&filters=microsoft%3Blinux)** depending on your OS needs. |
-    | Template virtual machine settings | Select **Use virtual machine without customization.**.
+  [!INCLUDE [create lab](./includes/lab-services-class-type-lab.md)] Specify the following lab settings:
 
-1. When you create a lab with the **Alternative Small GPU (Compute)** size, follow these steps to [install GPU drivers](./how-to-setup-lab-gpu.md#ensure-that-the-appropriate-gpu-drivers-are-installed).
+  | Lab settings | Value |
+  | ------------ | ------------------ |
+  | Virtual machine size | Select **Small** or **Medium** for a basic setup to access Jupyter Notebooks. Select **Alternative Small GPU (Compute)** for compute-intensive and network-intensive applications used in Artificial Intelligence and Deep Learning classes. |
+  | Virtual machine image | Choose [Data Science Virtual Machine – Windows Server 2019](https://azuremarketplace.microsoft.com/marketplace/apps/microsoft-dsvm.dsvm-win-2019), [Data Science Virtual Machine – Windows Server 2022](https://azuremarketplace.microsoft.com/marketplace/apps/microsoft-dsvm.dsvm-win-2022),  or [Data Science Virtual Machine – Ubuntu](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/microsoft-dsvm.ubuntu-2004). |
+  | Template virtual machine settings | Select **Use virtual machine without customization.** |
 
-    These process installs recent NVIDIA drivers and the Compute Unified Device Architecture (CUDA) toolkit, which you need to enable high-performance computing with the GPU. For more information, see [Set up a lab with GPU virtual machines](./how-to-setup-lab-gpu.md).
+- When you create a lab with the **Alternative Small GPU (Compute)** size, [install GPU drivers](./how-to-setup-lab-gpu.md#ensure-that-the-appropriate-gpu-drivers-are-installed).
 
-## Template machine configuration
+  This process installs recent NVIDIA drivers and the Compute Unified Device Architecture (CUDA) toolkit, which you need to enable high-performance computing with the GPU. For more information, see [Set up a lab with GPU virtual machines](./how-to-setup-lab-gpu.md).
+
+### Template machine configuration
 
 [!INCLUDE [configure template vm](./includes/lab-services-class-type-template-vm.md)]
 
 The Data Science VM images come with many of data science frameworks and tools required for this type of class. For example, the images include:
 
-- [Jupyter Notebooks](http://jupyter-notebook.readthedocs.io/): A web application that allows data scientists to take raw data, run computations, and see the results all in the same environment. It runs locally in the template VM.  
+- [Jupyter Notebooks](http://jupyter-notebook.readthedocs.io/): A web application that allows data scientists to take raw data, run computations, and see the results in the same environment. It runs locally in the template VM.  
 - [Visual Studio Code](https://code.visualstudio.com/): An integrated development environment (IDE) that provides a rich interactive experience when writing and testing a notebook. For more information, see [Working with Jupyter Notebooks in Visual Studio Code](https://code.visualstudio.com/docs/python/jupyter-support).
 
-The **Data Science Virtual Machine – Ubuntu** image is already provisioned with X2GO server to enable lab users to use a graphical desktop experience.
+The **Data Science Virtual Machine – Ubuntu** image is provisioned with X2Go server to enable lab users to use a graphical desktop experience.
 
 ### Enabling tools to use GPUs
 
@@ -80,7 +83,7 @@ from tensorflow.python.client import device_lib
 print(device_lib.list_local_devices())
 ```
 
-If the output from the above code looks like the following, TensorFlow isn't using the GPU:
+If the output from this code looks like the following result, TensorFlow isn't using the GPU:
 
 ```python
 [name: "/device:CPU:0"
@@ -92,13 +95,13 @@ incarnation: 15833696144144374634
 ]
 ```
 
-Continuing with the above example, see [TensorFlow GPU Support](https://www.tensorflow.org/install/gpu) for guidance.  TensorFlow guidance covers:
+Continuing with this example, see [TensorFlow GPU Support](https://www.tensorflow.org/install/gpu) for guidance. TensorFlow guidance covers:
 
 - Required version of the [NVIDIA drivers](https://www.nvidia.com/drivers)
-- Required version of the [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive).
-- Instructions to install [NVIDIA CUDA Deep Neural Network library (cudDNN)](https://developer.nvidia.com/cudnn).
+- Required version of the [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive)
+- Instructions to install [NVIDIA CUDA Deep Neural Network library (cudDNN)](https://developer.nvidia.com/cudnn)
 
-After you've followed TensorFlow's steps to configure the GPU, when you rerun the test code, you should see output similar to the following output.
+After you follow TensorFlow's steps to configure the GPU, when you rerun the test code, you should see results similar to the following output.
 
 ```python
 [name: "/device:CPU:0"
@@ -120,15 +123,15 @@ physical_device_desc: "device: 0, name: NVIDIA Tesla K80, pci bus id: 0001:00:00
 ]
 ```
 
-### Provide notebooks for the class
+## Provide notebooks for the class
 
 The next task is to provide lab users with notebooks that you want them to use. You can save notebooks locally on the template VM so each lab user has their own copy.
 
 If you want to use sample notebooks from Azure Machine Learning, see [how to configure an environment with Jupyter Notebooks](/azure/machine-learning/how-to-configure-environment#jupyter-notebooks).
 
 ### Publish the template machine
 
-You make the lab VM available for lab users, you have to [publish the template](how-to-create-manage-template.md#publish-the-template-vm). The lab VM has all the local tools and notebooks that you configured previously.
+To make the lab VM available for lab users, [publish the template](how-to-create-manage-template.md#publish-the-template-vm). The lab VM has all the local tools and notebooks that you configured previously.
 
 ## Connect to Jupyter Notebooks
 
@@ -144,65 +147,63 @@ If you use a Linux-based lab VM, lab users can connect to their lab VMs through
 
 ### SSH tunnel to Jupyter server on the VM
 
-For Linux-based labs, you can also connect directly from your local computer to the Jupyter server inside the lab VM. The SSH protocol enables port forwarding between the local computer and a remote server (in our case, the user's lab VM). An application that is running on a certain port on the server is **tunneled** to the mapping port on the local computer. 
+For Linux-based labs, you can also connect directly from your local computer to the Jupyter server inside the lab VM. The SSH protocol enables port forwarding between the local computer and a remote server. This is the user's lab VM. An application that runs on a certain port on the server is *tunneled* to the mapping port on the local computer.
 
 Follow these steps to configure an SSH tunnel between a user's local machine and the Jupyter server on the lab VM:
 
-1. Go to the [Azure Lab Services website](https://labs.azure.com)
+1. Go to the [Azure Lab Services website](https://labs.azure.com).
 
 1. Verify that the Linux-based [lab VM is running](how-to-use-lab.md#start-or-stop-the-vm).
 
 1. Select the **Connect** icon > **Connect via SSH** to get the SSH connection command.
 
-    The SSH connection command looks like the following:
+    The SSH connection command looks like the following example:
 
     ```shell
     ssh -p 12345 student@ml-lab-00000000-0000-0000-0000-000000000000.eastus2.cloudapp.azure.com
     ```
 
     Learn more about [how to connect to a Linux VM](connect-virtual-machine.md#connect-to-a-linux-lab-vm-using-ssh).
 
-1. On your local computer, launch a terminal or command prompt, and copy the SSH connection string to it. Then, add `-L 8888:localhost:8888` to the command string, which creates the **tunnel** between the ports. 
+1. On your local computer, launch a terminal or command prompt, and copy the SSH connection string to it. Then, add `-L 8888:localhost:8888` to the command string, which creates the tunnel between the ports.
 
-    The final command should look as follows:
+    The final command should look like the following example.
 
     ```shell
     ssh –L 8888:localhost:8888 -p 12345 student@ml-lab-00000000-0000-0000-0000-000000000000.eastus.cloudapp.azure.com
      ```
 
-1. Press **ENTER** to run the command.
+1. Press **Enter** to run the command.
 1. When prompted, provide the lab VM password to connect to the lab VM.
-1. When you’re connected to the VM, start the Jupyter server using this command:
+1. When you connect to the VM, start the Jupyter server using this command:
 
     ```bash
     jupyter notebook
     ```
 
-    The command outputs a URL for the Jupyter server in the terminal. The URL should look like:
+    The command outputs a URL for the Jupyter server in the terminal. The URL should look like this example:
 
     ```output
     http://localhost:8888/?token=8c09ecfc93e6a8cbedf9c66dffdae19670a64acc1d37
     ```
 
-1. Paste this URL into a browser on your local computer to connect and work on your Jupyter Notebook.
+1. To connect to your Jupyter Notebook and work on it, paste this URL into a browser on your local computer.
 
     > [!NOTE]
     > Visual Studio Code also enables a great [Jupyter Notebook editing experience](https://code.visualstudio.com/docs/python/jupyter-support). You can follow the instructions on [how to connect to a remote Jupyter server](https://code.visualstudio.com/docs/python/jupyter-support#_connect-to-a-remote-jupyter-server) and use the same URL from the previous step to connect from VS Code instead of from the browser.
 
-## Cost estimate
+## Estimate cost
 
-This section provides a cost estimate for running this class for 25 lab users. There are 20 hours of scheduled class time. Also, each user gets 10 hours quota for homework or assignments outside scheduled class time. The VM size we chose was alternative small GPU (compute), which is 139 lab units. If you want to use the Small (20 lab units) or Medium size (42 lab units), you can replace the lab unit part in the equation below with the correct number.  
+This section provides a cost estimate for running this class for 25 lab users. There are 20 hours of scheduled class time. Also, each user gets 10 hours quota for homework or assignments outside scheduled class time. The VM size chosen was alternative small GPU (compute), which is 139 lab units. If you want to use the Small (20 lab units) or Medium size (42 lab units), you can replace the lab unit part in the equation here with the correct number.  
 
 Here's an example of a possible cost estimate for this class:
-25 lab users \* (20 scheduled hours + 10 quota hours) \* 139 lab units \* 0.01 USD per hour = 1042.5 USD
+`25 lab users * (20 scheduled hours + 10 quota hours) * 139 lab units * 0.01 USD per hour = 1042.5 USD`.
 
 >[!IMPORTANT]
 >This cost estimate is for example purposes only. For current details on pricing, see [Azure Lab Services Pricing](https://azure.microsoft.com/pricing/details/lab-services/).
 
-## Conclusion
+## Related content
 
 In this article, you learned how to create a lab for a Jupyter Notebooks class and how user can connect to their notebooks on the lab VM. You can use a similar setup for other machine learning classes.
 
-## Next steps
-
 [!INCLUDE [next steps for class types](./includes/lab-services-class-type-next-steps.md)]