> My question is: How difficult would it be to create an "%autoreload 3"
that would work 100% of the time ?

It's probably really hard to really reach 100%, especially when code rely
on C-extension package like NumPy.
We'll be happy to review and merge PR on autoreload, but someone need to do
the work, and figure out how to reload or fix the reload case you are
interested in.
-- 
Matthias


On Thu, 23 Aug 2018 at 23:45, oscar6echo <[email protected]>
wrote:

> There is a lot of work and attention invested in JupyterLab to improve the
> user experience. It's great !
>
> However there is a type of workflow I have not heard much, but I see very
> often in my company: People develop Python packages partly in notebooks,
> partly in IDEs (VS Code, PyCharm, etc). When a piece of code is more or
> less ok, it moves from a notebook cell to the .py file part of a package
> (IDEs are better suited to deal with large Python packages). This package
> is imported in the top cell of the notebook. Because developing is a lot of
> trial and error, we use the autoreload IPython magic a lot: "%load_ext
> autoreload, %autoreload 2". Unfortunately this magic works 80% of the time.
> If the package has a new file of a class a new method typically, it won't
> work. Then a dev has to restart the kernel.
>
> My question is: How difficult would it be to create an "%autoreload 3"
> that would work 100% of the time ?
> Has anybody else got this problem ?
> Does anybody else use Jupyter and and IDE in parallel ? If so any tip ?
>
> Thx
>
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