Welcome
Welcome to the documentation for Datalayer, a platform for running Remote Jupyter Kernels with GPU or CPU power, directly from your favorite Local Data Science IDEs such as JupyterLab, VS Code and CLI. Don't want to install a client, no problem, just consume the features from the SaaS.
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 Data Science 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 a Data Scientist, AI engineer or Researcher, Datalayer enhances your productivity and performance by providing powerful Remote Kernel capabilities.
This documentation will guide you through the platform's features and help you get started with your first Remote Kernel.
📄️ SaaS
Datalayer is available as a Software as a Service (SaaS).
📄️ JupyterLab
Datalayer integrates seamlessly as a JupyterLab extension, allowing you to manage and interact with Remote Kernels directly from your familiar JupyterLab interface. The extension mirrors the functionality available on Datalayer SaaS once you log in.
📄️ VS Code
Datalayer can be used from VS Code.
📄️ CLI
Datalayer provides a Command Line Interface (CLI) allowing that support the platform features on Notebooks and Python files with Remote Kernels.
🗃️ Accounts
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🗃️ Kernels
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🗃️ Environments
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🗃️ Storage
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🗃️ Cases
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