Skip to main content

Kernels

Datalayer introduces Remote Kernels to seamlessly scale your data science and AI workflows.

info

A Jupyter Kernel is the computational engine that executes the code in your Jupyter Notebooks or Python scripts. It operates independently of the Notebook or script itself, allowing you to run your code remotely across various systems.

Types of Kernels​

Kernels can be hosted in your browser, on your local Jupyter Server, or remotely.

  • Browser Kernels: These kernels run as web workers directly within your browser, enabling seamless interaction without the need for additional software installations.
  • Local Kernels: In this traditional setup, a Jupyter Server runs locally, and each execution spawns a local process to handle computations.
  • Remote Kernels: These kernels run on a separate machine from your local device, leveraging potentially more powerful CPU, RAM, and GPU resources.

You can easily access and consume kernels from various interfaces, including JupyterLab, the CLI, or VS Code.

Launching Remote Kernels​

You can launch remote kernels from the "Kernels" tab in Datalayer or via the "Home" page of the JupyterLab extension (see JupyterLab for installation instructions).

Kernels interface

When launching a remote kernel, you'll be prompted to configure a few options:

Remote kernel launcher

Environment​

You will be prompted to select an environment (see Environments for more details).

Time Reservation​

This defines the maximum execution time for the kernel. After this time, the kernel will be automatically terminated to prevent overconsumption of credits (for more information, see Credits).

note

More options to store data and share it between remote and local environments will be available soon, providing greater ease and flexibility in managing and accessing your data.

Example Notebook​

You can enable the option to automatically open a preloaded example notebook when the kernel starts. This feature is available only for JupyterLab and provides a ready-to-use notebook, helping you quickly familiarize yourself with the environment and start working right away.

Kernels Management​

You can view and manage running kernels under the "Kernels" tab, where you can delete, interrupt, or restart them as needed.

Kernel management
note

Future releases will introduce advanced kernel management features, including the ability to pause kernels. This will allow you to save a kernel's current state and resume it later.