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The whole notebook doesn't recompute only cells that are dependent on the cell that changed. This is extremely powerful because you never end up with stale cell

The whole notebook doesn't recompute only cells that are dependent on the cell that changed. This is extremely powerful because you never end up with stale cells that are showing incorrect values.

legerdemain 5y ago
This is extremely counterproductive, because I want results you're calling "stale" to use as a reference or inspiration. I don't want to destroy old results just because I changed some parameter value to test an idea.
- KMag 5y ago
  It improves reproducibility, consistency, and sharing, but reduces convenience for some operations. It's a trade-off in favor of programming in the large. If you don't want to recompute dependent nodes, then use new names for your experiments rather than redefining old functions and variables. Yes, in some ways this is less convenient for you, but it's more convenient for people receiving your notebooks, that the notebook is always in a consistent state and reproducible. Maybe it doesn't work well for your workflow, particularly if you're not sharing notebooks and keeping your notebooks small. On the other hand, if your workflow requires significant amounts of leaving notebooks in an inconsistent state, you may end up saving yourself significant frustration with larger notebooks and losing work due to losing track of your mental tracking of inconsistencies. Also, if you hit a state that you really don't want to lose, you should probably do a quick git commit. You can always squash commits later if needed. It might be worth changing your workflow, or it might not.
  - ChrisRackauckas 5y ago
    I think this is the interesting point though. Many people want to use Jupyter notebooks so that it looks reproducible. Not to make it actually reproducible. God forbid it actually has to be re-ran, it could have different results! I think that's my main notebook gripe: they make it look like if you run the code you'll get these results, but that's not even close to the case. Many people abuse this. At this point, I pretty much assume anything in a Jupyter notebook isn't reproducible.
    - lungben 5y ago
      Yes. A Jupyter notebook is only reproducible in my opinion if you can hit "Restart Kernell and execute all cells" and get the same result. Otherwise, it should never been shared with other people or even contain relevant analysis you may need for yourself later. But this is not enough - also the library dependencies need to be fixed. Pluto will make this very easy in the near-future: https://github.com/fonsp/Pluto.jl/pull/844 https://github.com/fonsp/Pluto.jl/pull/844
- oivey 5y ago
  If you’re so attached to that data, you should probably do something to save it other than let it sit in RAM or maybe an old plot in a random notebook.
  - legerdemain 5y ago
    I'm not sure what exactly you're trying to do: harangue me into using this half-baked notebook replacement, or just telling me how to do my job.
- cwyers 5y ago
  Then instead of reassigning new data to foo, just assign new data to foo2. You can still use the notebook to experiment, what you are doing is removing ambiguity.

The whole notebook doesn't recompute only cells that are dependent on the cell that changed. This is extremely powerful because you never end up with stale cells that are showing incorrect values.

legerdemain 5y ago

This is extremely counterproductive, because I want results you're calling "stale" to use as a reference or inspiration. I don't want to destroy old results just because I changed some parameter value to test an idea.

KMag 5y ago
It improves reproducibility, consistency, and sharing, but reduces convenience for some operations. It's a trade-off in favor of programming in the large. If you don't want to recompute dependent nodes, then use new names for your experiments rather than redefining old functions and variables. Yes, in some ways this is less convenient for you, but it's more convenient for people receiving your notebooks, that the notebook is always in a consistent state and reproducible. Maybe it doesn't work well for your workflow, particularly if you're not sharing notebooks and keeping your notebooks small. On the other hand, if your workflow requires significant amounts of leaving notebooks in an inconsistent state, you may end up saving yourself significant frustration with larger notebooks and losing work due to losing track of your mental tracking of inconsistencies. Also, if you hit a state that you really don't want to lose, you should probably do a quick git commit. You can always squash commits later if needed. It might be worth changing your workflow, or it might not.
- ChrisRackauckas 5y ago
  I think this is the interesting point though. Many people want to use Jupyter notebooks so that it looks reproducible. Not to make it actually reproducible. God forbid it actually has to be re-ran, it could have different results! I think that's my main notebook gripe: they make it look like if you run the code you'll get these results, but that's not even close to the case. Many people abuse this. At this point, I pretty much assume anything in a Jupyter notebook isn't reproducible.
  - lungben 5y ago
    Yes. A Jupyter notebook is only reproducible in my opinion if you can hit "Restart Kernell and execute all cells" and get the same result. Otherwise, it should never been shared with other people or even contain relevant analysis you may need for yourself later. But this is not enough - also the library dependencies need to be fixed. Pluto will make this very easy in the near-future: https://github.com/fonsp/Pluto.jl/pull/844 https://github.com/fonsp/Pluto.jl/pull/844
oivey 5y ago
If you’re so attached to that data, you should probably do something to save it other than let it sit in RAM or maybe an old plot in a random notebook.
- legerdemain 5y ago
  I'm not sure what exactly you're trying to do: harangue me into using this half-baked notebook replacement, or just telling me how to do my job.
cwyers 5y ago
Then instead of reassigning new data to foo, just assign new data to foo2. You can still use the notebook to experiment, what you are doing is removing ambiguity.