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Set up a lab to teach data science with Python and Jupyter Notebooks

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Important

Azure Lab Services will be retired on June 28, 2027. For more information, see the retirement guide.

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.

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.

Note

This article references features available in lab plans, which replaced lab accounts.

Prerequisites

  • To set up this lab, you need access to an Azure subscription. Discuss with your organization's administrator to see if you can get access to an existing Azure subscription. If you don't have an Azure subscription, create a free account before you begin.

Configure lab plan settings

After you have an Azure subscription, you can create a lab plan in Azure Lab Services. For more information about creating a new lab plan, see Quickstart: Set up resources to create labs. You can also use an existing lab plan.

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.

  • Select one of the following Azure Marketplace images, depending on your operating system requirements:

    • Data Science Virtual Machine – Windows Server 2019/Windows Server 2022
    • Data Science Virtual Machine – Ubuntu 20.04

  • Alternately, create a custom VM image:

    The Data Science VM images in the Azure Marketplace are already configured with Jupyter Notebooks. 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.

    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.

Create a lab

Template machine configuration

After you create a lab, create a template VM that is based on the virtual machine size and image you choose. Configure the template VM with everything you want to provide to your students for this class. For more information, see Create and manage a template in Azure Lab Services.

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:

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

If you're using the Alternative Small GPU (Compute) size, we recommend that you verify that the Data Science frameworks and libraries are properly set up to use GPUs. You might need to install a different version of the NVIDIA drivers and CUDA toolkit. To configure the GPUs, you should consult the framework's or library's documentation.

For example, to validate that TensorFlow uses the GPU, connect to the template VM and run the following Python-TensorFlow code in Jupyter Notebooks:

import tensorflow as tf
from tensorflow.python.client import device_lib

print(device_lib.list_local_devices())

If the output from this code looks like the following result, TensorFlow isn't using the GPU:

[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 15833696144144374634
]

Continuing with this example, see TensorFlow GPU Support for guidance. TensorFlow guidance covers:

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.

[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 15833696144144374634
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 11154792128
locality {
  bus_id: 1
  links {
  }
}
incarnation: 2659412736190423786
physical_device_desc: "device: 0, name: NVIDIA Tesla K80, pci bus id: 0001:00:00.0, compute capability: 3.7"
]

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.

Publish the template machine

To make the lab VM available for lab users, publish the template. The lab VM has all the local tools and notebooks that you configured previously.

Connect to Jupyter Notebooks

The following sections show different ways for lab users to connect to Jupyter Notebooks on the lab VM.

Use Jupyter Notebooks on the lab VM

Lab users can connect from their local machine to the lab VM and then use Jupyter Notebooks inside the lab VM.

If you use a Windows-based lab VM, lab users can connect to their lab VMs through remote desktop (RDP). For more information, see how to connect to a Windows lab VM.

If you use a Linux-based lab VM, lab users can connect to their lab VMs through SSH or by using X2Go. For more information, see how to connect to a Linux lab VM.

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. 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.

  2. Verify that the Linux-based lab VM is running.

  3. Select the Connect icon > Connect via SSH to get the SSH connection command.

    The SSH connection command looks like the following example:

    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.

  4. 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 like the following example.

    ssh –L 8888:localhost:8888 -p 12345 student@ml-lab-00000000-0000-0000-0000-000000000000.eastus.cloudapp.azure.com

  5. Press Enter to run the command.

  6. When prompted, provide the lab VM password to connect to the lab VM.

  7. When you connect to the VM, start the Jupyter server using this command:

    jupyter notebook

    The command outputs a URL for the Jupyter server in the terminal. The URL should look like this example:

    http://localhost:8888/?token=8c09ecfc93e6a8cbedf9c66dffdae19670a64acc1d37

  8. 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. You can follow the instructions on how to 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.

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.

The template image can now be published to the lab. For more information, see Publish the template VM.

As you set up your lab, see the following articles: