Kernels

Datalayer introduces Browser and Remote Jupyter Kernels on top of the well-known Local Jupyter Kernels to seamlessly scale your Data Science and AI workflows.

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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.

📄️ Lifecycle

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

📄️ Snapshots

A Kernel Snapshot is a saved state of a Kernel. It can be used to save the current state of a Kernel, and to restore it later.

📄️ Credits

The usage of Remote Kernels is based on a credit consumption model. When you open an account, you begin with a set of free credits to get you started.

📄️ Secrets

Secrets are a way to store sensitive information such as passwords, API keys, and other credentials.