So I totally agree that this is a real problem and worth solving. But my finely-honed instincts tell me that customizing your kernel from within the running kernel is *dangerous* and *error* prone. (Yes, I know you can already do this with bangs.) 4/13
— Joel Grus (@joelgrus) December 12, 2018
I was told that "yes, this is an antipattern if you're running notebooks locally, but it's good if you're running them remotely". The challenge is that (as I understand it), *notebooks themselves don't make this distinction*. 6/13
— Joel Grus (@joelgrus) December 12, 2018
The other half of my concern is that modifying the kernel from within the kernel (you guessed it) makes your code harder to reason about. For example, you could imagine a situation like
— Joel Grus (@joelgrus) December 12, 2018
[1] import torch
[2] x = torch.tensor(1)
[3] %pip install torch==1.0.0
[4] y = x.sum()
8/13
