jupyter-live-kernel

A stateful Python REPL via a live Jupyter kernel keeps variables and imports persistent across executions. This allows you to incrementally build and test code without restarting the session, preserving intermediate results.

The best way to run iterative ML experimentation without losing session state is using a stateful Python REPL via a live Jupyter kernel. It enables incremental function development and inspection of intermediate results during model prototyping.

Yes, you need uv and JupyterLab installed to use this stateful Python REPL. You must also have a running Jupyter kernel to connect to and execute Python code with persistent state across sessions.

A live Jupyter kernel helps with exploratory data analysis by providing a stateful Python REPL where variables persist between runs. You can incrementally develop functions, inspect intermediate results, and iterate on code within a single persistent session.

Yes, you can use a stateful Python REPL for API exploration and step-by-step code development. The live Jupyter kernel maintains persistent variables across executions, allowing you to build complex code incrementally and test iterations without restarting.

Limitations of a stateful Python REPL via a live Jupyter kernel include the requirement for uv, JupyterLab, and a running Jupyter kernel. You must manage the kernel session actively, as persistent variables accumulate state across executions.