datalab-kernel 0.1.0

A standalone Jupyter kernel for DataLab with optional live synchronization

DataLab-Kernel

A standalone Jupyter kernel providing seamless, reproducible access to DataLab workspaces, with optional live synchronization to the DataLab GUI.

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Overview

DataLab-Kernel is a custom Jupyter kernel designed to bridge DataLab and the Jupyter ecosystem.

It enables scientists and engineers to:

• run reproducible analyses in Jupyter notebooks,
• interact transparently with DataLab’s internal workspace when DataLab is running,
• share notebooks that can be replayed with or without DataLab,
• combine narrative, code, and results without sacrificing interactive visualization.

DataLab-Kernel is not a replacement for DataLab’s GUI.
It is a complementary execution layer that turns DataLab into a hybrid scientific platform: GUI-driven when needed, notebook-driven when appropriate.

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Key Features

• Single, stable user API

  • workspace for data access and persistence
  • plotter for visualization
  • sigima for scientific processing

• Two execution modes, one notebook

  • Live mode: automatic synchronization with a running DataLab instance
  • Standalone mode: notebook-only execution, fully reproducible

• Reproducibility by design

  • Analyses can be saved and reloaded using .h5 files
  • Notebooks run unchanged across environments

• Performance-aware

  • Optimized data handling when DataLab is attached
  • No unnecessary serialization for large datasets

• Decoupled architecture

  • Installable independently of DataLab
  • DataLab is a privileged host, not a requirement

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Typical Usage

img = workspace.get("i042")
filtered = sigima.proc.image.butterworth(img, cut_off=0.2)
workspace.add("filtered_i042", filtered)
plotter.plot("filtered_i042")

Depending on the execution context:

• the result appears inline in the notebook,
• and, if DataLab is running, it also appears automatically in the DataLab GUI, with views and metadata kept in sync.

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Execution Modes

Live Mode (DataLab-attached)

• DataLab launches a Jupyter server and starts kernel-datalab.
• The kernel detects DataLab at runtime.
• Workspace operations and visualizations are synchronized with the GUI.

Two connection methods are supported:

• Web API (recommended): HTTP/JSON connection using DATALAB_WORKSPACE_URL and DATALAB_WORKSPACE_TOKEN environment variables
• XML-RPC (legacy): Automatic connection when DataLab is running with remote control enabled

Standalone Mode (Notebook-only)

• The kernel is used like any standard Jupyter kernel.
• No DataLab installation or GUI is required.
• Data are managed locally and persisted to .h5 files.

The same notebook runs unchanged in both modes.

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Installation

Standalone usage

pip install datalab-kernel sigima
jupyter lab

Then select DataLab Kernel from the kernel list.

With DataLab

When installed alongside DataLab, the kernel is automatically available and can be launched directly from the DataLab interface.

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Persistence and Sharing

Workspace state can be saved and reloaded:

workspace.save("analysis.h5")
workspace.load("analysis.h5")

This enables:

• sharing notebooks and data with collaborators,
• replaying analyses without DataLab,
• resuming workflows inside DataLab by reopening the associated project.

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Documentation

• User contract and behavior: see plans/specification.md
• Vision and architectural principles: see plans/architecture.md

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Project Status

DataLab-Kernel is under active design and development.

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License

This project is released under an open-source license (see LICENSE file).