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Welcome

Welcome to the documentation for Datalayer, a platform for Jupyter Runtimes with GPU or CPU power, directly from your favorite Local Machine Learning IDEs such as JupyterLab, VS Code and CLI.

Don't want to install a client..., no problem, just consume the features from the SaaS.

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Runtimes are essentially managed Jupyter Kernels with added features, including resource allocation and a credit-based system.

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 Python script itself, allowing you to run your code remotely across various systems.

Datalayer helps you scale your AI and Machine Learning workflows without the need to change your existing code. Our platform is designed to seamlessly integrate into your workflows and supercharge your computations with the processing power you need.

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Whether you're an AI Engineer, Machine Learning Engineer, Data Scientist or Researcher, Datalayer enhances your productivity and performance by providing powerful Runtimes capabilities.

This documentation will guide you through the platform's features and help you get started with your first Runtime.

It is structured as follows:

  • The Features section provides an overview of Datalayer capabilities.
  • The SaaS, JupyterLab, VS Code and CLI sections explain how to use the platform features from different interfaces.
  • The Cases section showcases some use cases.