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[BUG] (WIP) Binder visualization errors #193
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Should fix visualizations
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Thanks for doing this Jason. I'm fine even it increases the loading time; I prefer the binder instance to be able to plot the results rather than returning an error. On a slightly related note, as for the error we experienced with the local |
I can give it a shot with adding the |
[ENH] export utils to PYTHONPATH
The |
What about adding it to If not, we may need to explicitly add it in the Jupyter notebook as well?
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Yeah, one thing we could do is add a |
Adding it to |
Thanks for trying @kaitj . Maybe now that the workshop is over we could transition to |
I just tried to run the lesson using This makes me wonder how other lessons have run on such a low memory or how this one was run at the first workshop ever. If that cannot be changed, we may need to start thinking about the CONP hosting that was discussed at some point @kaitj @josephmje @jerdra . |
@jhlegarreta does this occur when you try to run the notebook in isolation or when you progress through lessons? In the latter case, manually shutting down notebooks frees up memory from previous episodes; closing the notebook tab isn't enough to free memory. Nonetheless you bring up a good point re: better hosting solutions, I'll reach out to Loic from CONP again about any progress he's made with using our lessons w/Neurolibre |
Ah, I did not know that. Thanks @jedra. Have tried again running the deterministic tractography notebook in isolation, and I've been able to run it once; trying to free resources and re-run it again was making the notebook to stall again at a later point (where memory usage was 1.85/2.00 GB) 😟. So it looks like 2 GB is still too low as a memory resource. Either way, I would add a note to warn users about this memory management. I have not been able to find any relevant mention in the official doc, though: @jedra do you happen to know where this is stated? Thanks. |
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Thanks @josephmje. So that's about binder. Was wondering about the Jupyter side too. |
I think the 2 GB limit is only on Binder's side. If you were to run the notebooks locally, you'd be limited by your own computer's resources. |
Yes, but I would assume that for the notebooks that are not manually shut down memory is not freed as Jerry said. I was wondering where that is said in the Jupyter documentation. |
@jhlegarreta I'm not sure where in Jupyter its mentioned tbh. I noticed this in the fMRI lessons where in JupyterHub upon closing a tab for a given lesson you'll may notice that there is still a small black circle beside the notebook - this indicates that the kernel is still running. Closest I can find is this discussion: And this pull request on JupyterHub: Looks like you can update a Jupyter setting |
Thanks for the explanation @jedra 💯. I added the related notes in PR #200. I did not add the links to the issues/PR since I thought that might look somehow weird/requires the effort to go through all of the discussions. But can be changed at a later time/if documentation becomes available. Also, it looks that there is a |
@jhlegarreta np! That's the approach that we're taking with the fMRI notebooks as well! You can also right-click notebooks with a black circle to shut them down :) |
Added
libgl1-mesa-glx
to theapt.txt
for binder. This fixes the visualization error with fury, but increases the build time for the binder notebook.Still causing issues with the visualization, so need to look into this more for binder.
Can test by spinning up a Binder notebook using the
fix_lib_error
branch.