nbsafety adds a layer of protection to computational notebooks by solving the
stale dependency problem when executing cells out-of-order. Here's an
example in action:
When the first cell is rerun, the second cell now contains a reference to an
f and is suggested for re-execution with a turquoise highlight. The
third cell contains a reference to a stale
y is stale due to its
dependency on an old value of
f. As such, the third cell is marked as unsafe
for re-execution with a red highlight. Once the second cell is rerun, it is
now suggested to re-execute the third cell in order to refresh its stale
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.
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.
pip install nbsafety
The kernel ships with an extension that highlights cells with live references to stale symbols using red UI elements. It furthermore uses turquoise highlights for cells with live references to updated symbols, as well as for cells that resolve staleness.
To run an
nbsafety kernel in Jupyter, 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:|
pip uninstall nbsafety
Code in this project licensed under the BSD-3-Clause License.
|Stephen Macke||Ray Gong||Shreya Shankar|