NBSafety is a drop-in replacement for Jupyter’s Python 3 kernel that makes it easier to reason about hidden state in computational notebooks. It does so by highlighting cells that reference data that have become stale (due to out-of-order cell executions). It also highlights cells that resolve such data staleness, as depicted below:
In the above example, the user creates three cells and runs them from top to bottom. The user then edits and reruns cell 1. However, there is an implicit dependency from
y, and the desirable behavior in most cases is for
y to reflect the updated value of
f. Rerunning cell 3 without first rerunning cell 2 will therefore yield a semantically stale result, so NBSafety gives cell 3 a stale highlight. Furthermore, it gives cell 2 a refresher highlight, since it contains a reference to an updated
f that can be used to “refresh”
NBSafety accomplishes its magic using a combination of a runtime tracer (to build the implicit dependency graph) and a static checker (to provide warnings before running a cell), both of which are deeply aware of Python’s data model. In particular, NBSafety requires minimal to no changes in user behavior, opting to get out of the way unless absolutely necessary and letting you use notebooks the way you prefer. For more information on NBSafety’s implementation, please see the technical report.
pip install nbsafety
If using JupyterLab, we highly recommend installing the companion extension:
jupyter labextension install jupyterlab-nbsafety
Because NBSafety is implemented as a custom Jupyter kernel, it works for both Jupyter notebooks and JupyterLab (if using JupyterLab, the additional labextension is recommended). To run an NBSafety kernel, select “Python 3 (nbsafety)” from the list of notebook types in Jupyter’s “New” dropdown dialogue. For JupyterLab, similarly select “Python 3 (nbsafety)” from the list of available kernels in the Launcher tab.
|Jupyter Notebook Entrypoint:||Jupyter Lab Entrypoint:|