Learn BEAKERX with Real Code Examples

Updated Nov 26, 2025

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Installation Setup

Install Jupyter Notebook or JupyterLab

Install BeakerX via pip (`pip install beakerx`) and enable extensions

Requires Python 3.x and Java runtime environment for JVM languages

Optional: configure kernels for additional languages

Launch notebook via standard Jupyter interface

Environment Setup

Install Python 3.x and Jupyter Notebook or JupyterLab

Install Java runtime for JVM languages

Install BeakerX and enable extensions

Launch notebook server and create new notebook

Select kernels and begin coding with widgets and plots

Config Files

Notebook files (.ipynb) standard Jupyter format

Kernel configurations per installed language

Widget configuration embedded in notebook metadata

Optional JVM environment settings

No built-in persistent cloud configuration

Cli Commands

jupyter notebook - launch notebook server

pip install beakerx - install BeakerX

beakerx install - enable BeakerX extensions

%magic commands - BeakerX cell magics

Notebook cells executed per selected kernel

Internationalization

UI primarily in English

Supports Unicode in code, markdown, and plots

Accessible globally via local or hosted Jupyter servers

Documentation available online in multiple languages

Widgets handle international characters and data

Accessibility

Works on modern browsers with Jupyter server

Keyboard shortcuts for navigation and execution

Widgets accessible via mouse or keyboard

Screen reader compatibility relies on Jupyter

Cross-platform support on Windows, Mac, Linux

Ui Styling

Jupyter notebook interface enhanced with BeakerX widgets

Interactive plots and tables integrated inline

Toolbar for cell execution and kernel selection

Markdown and rich text for documentation

Responsive interface supporting multiple languages

State Management

Notebook state persisted in .ipynb file

Cell outputs saved within notebook

Interactive widgets maintain state during session

Undo/redo supported in notebook interface

Polyglot kernel state isolated per kernel

Data Management

Use local files or import libraries

Persistent notebook storage on disk or version control

Interactive tables manipulate in-memory datasets

Plot objects stored in notebook outputs

No built-in cloud storage, relies on Jupyter environment