jupyter-live-kernel

Stateful execution connects to a live Jupyter kernel, allowing variables and imports to persist across multiple Python code executions. This is ideal for iterative data-science exploration and machine-learning experimentation where you run code step-by-step.

Yes, a live Jupyter kernel provides an interactive Python REPL that preserves variables and imports. By running code in the same kernel session, your data persists across executions for continuous iterative exploration.

Yes, you need a running JupyterLab server and the hamelnb script to communicate via the Jupyter REST API. This setup enables the live kernel connection required for stateful Python execution.

Stateful execution via a live Jupyter kernel is the best way for step-by-step API testing, as it keeps your Python environment loaded and variables intact. You can run code iteratively without reloading imports or losing your session state.

Yes, the stateful Jupyter kernel is explicitly designed for data-science notebooks and machine-learning model prototyping. It allows you to execute data exploration code iteratively while preserving your loaded datasets and variables across each run.

Standard Python REPLs lose variables because they execute in isolated sessions without state persistence. Connecting to a live Jupyter kernel solves this by running your code in the same kernel session, ensuring variables and imports persist across executions.