Using Jupyter with ML Runtimes

JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data.

You can use JupyterLab to configure and arrange the user interface to support a wide range of workflows in data science, scientific computing, and machine learning. JupyterLab allows you to use extensions that add new components and integrate with existing ones.

You must install any files necessary for JupyterLab, including configuration, customization, extensions, and kernels, into /home/cdsw for each project.

Code Completion in JupyterLab

You can use JupyterLab to write and run code in notebooks or in a traditional .py file. Completion works out of the box in notebooks and consoles. If you are working in a .py file in the text editor, you must create a console to provide the completion suggestions. To do this:

  1. Open the .py file.
  2. Right click on the document and choose "Create console for editor".
  3. Choose the option appropriate to the type of completion you want to perform.

    For example, to get completions on "math", you must first "import math" in the console.

Set JupyterLab Timeouts

You can control JupyterLab timeouts with the following environment variables:

JUPYTER_KERNEL_TIMEOUT_SECONDS
JUPYTER_SERVER_TIMEOUT_SECONDS