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Disable rerun of code execution at application start-up #508
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Or TUTORIAL_QUICK_RESTORE env variable Short-circuit complete evaluation of user code on restore for large tutorials. Fixes rstudio#508
I added a A side-effect of the current implementation of the quick restore method is that stored previous exercise output isn't restored. Users will have to click Run Code or Submit Answer to explicitly see the exercise output. I have another version of this same feature in a branch at gadenbuie/learnr@quick-restore-2 that will use the stored output (if any). Where and how previous output is stored depends on your setup. @mstackhouse, Would you be able to test the quick-restore-2 version and see if that also works in your case? It'd be nice to be able to restore previous feedback, if its available and if that doesn't add too much overhead at tutorial load. |
@gadenbuie Sure! We'd be happy to! I do want to confirm that tutorial.quick_restore works - we've tested that implementation out already when you pushed yesterday. |
Hey @gadenbuie, Thanks for the update! Can you give a short reprex I can test this with? I'm not sure in what cases the output is stored but we are currently using the default tutorial storage option. |
Hi @gadenbuie, we are building some large tutorials, and quick_restore, where the output is saved, would be great to have! |
Thanks @gadenbuie! We are using this one already, but we were wondering about the quick-restore-2 you mentioned above. Do you have plans to make that available? I think it is a great feature to have for large tutorials like the ones we are building. Thanks a lot for the package btw, it is amazing! |
Hi @yassinza, I rebased the remotes::install_github("gadenbuie/learnr@quick-restore-2")
remotes::install_github("rstudio/learnr#794") Let me know how it goes! |
Hi @gadenbuie, thanks for the quick response! |
@yassinza Unfortunately, this isn't something that we currently save in the tutorial storage. |
My team has searched around to see if this is possible, but we don't think it is.
Currently, when you open a learnr application, each of the exercise blocks executes to restore the results from the last session. When a learnr application gets larger, this can become quite intensive - particularly if the application will be used in a larger environment with a shared external evaluator, and in turn cause exercise chunks to hang while waiting for others' code to finish.
An ideal scenario would be to restore the code executed to the exercise chunk, but not rerun the exercise itself.
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