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Because output is cached and because we live in an imperfect world -- it's possible that some external API was unavailable coincidentally during an execution, in this case it may be necessary to re-execute the appyter. Currently I can manually mark an appyter to be re-executed by removing metadata.nbexecute part of the instance's notebook, but it may make sense to allow users to request a re-execution to mitigate permanently broken appyter instances.
Because some appyters can simply be flawed -- we can't just assume errors mean we should re-execute; in any case, the current recommended way to deal with these situations is to submit an issue and let us investigate manually.
The text was updated successfully, but these errors were encountered:
Because output is cached and because we live in an imperfect world -- it's possible that some external API was unavailable coincidentally during an execution, in this case it may be necessary to re-execute the appyter. Currently I can manually mark an appyter to be re-executed by removing
metadata.nbexecute
part of the instance's notebook, but it may make sense to allow users to request a re-execution to mitigate permanently broken appyter instances.Because some appyters can simply be flawed -- we can't just assume errors mean we should re-execute; in any case, the current recommended way to deal with these situations is to submit an issue and let us investigate manually.
The text was updated successfully, but these errors were encountered: