I may try later. For now, the problem has been fixed by manually creating a context and passing that to each function so that it can be used in `Thread`. I also pop() the context manually. Strangely, I just used pycuda.autoinit.context before, which should work in my opinion. Maybe it's an issue with pycuda.autoinit being imported several times or somethin?
Anyway, I appreciate your quick responses and helpfulness. > On 31. Jul 2018, at 12:27, Bogdan Opanchuk <bog...@opanchuk.net> wrote: > > Well, that's strange then. As far as I understand, scikit-cuda uses the > existing context (unless explicitly asked to create a new one), and Reikna > should too if you pass it the reference from pycuda.autoinit. I guess I can't > say anything more definite without seeing the code. Any change you can reduce > it to a shareable size while preserving the errors? > > On Tue, Jul 31, 2018 at 6:05 PM, Rasmus Diederichsen > <rasmusdiederich...@gmail.com <mailto:rasmusdiederich...@gmail.com>> wrote: > I do aim to use only one context (I don't know why I would want more than > one; the stuff I'm doing is mostly serial but the memory shouldn't leave the > GPU). > >> On 31. Jul 2018, at 09:16, Bogdan Opanchuk <bog...@opanchuk.net >> <mailto:bog...@opanchuk.net>> wrote: >> >> First of all, are you using multiple contexts or a single one? If you only >> have one context, `Thread(pycuda.autoinit.context)` should be enough for >> Reikna (don't know about scikit-cuda, though). >> >> Now if you have several contexts, things become more complicated. CUDA >> maintains a global context stack, if you want to use a specific context, you >> have to make sure it is on top of the stack (using corresponding PyCUDA >> functions). For Reikna, you must use a `Thread` object created for a >> specific context (or anything derived from it) only when that context is >> active. >> >> On Tue, Jul 31, 2018 at 5:07 PM, Rasmus Diederichsen >> <rasmusdiederich...@gmail.com <mailto:rasmusdiederich...@gmail.com>> wrote: >> I don't know cuda really and use the 3 libraries mentioned in the subject. I >> time and again run into CUDA errors of the form "Invalid resource handle" >> which appear in some cases, but not in others, i.e. the same function will >> error or not depending on what ran before it, even if totally unrelated. I >> gather from this that it has to do with state in CUDA, possibly mismanaging >> contexts. But since it's hard to reproduce without all my code, I can't open >> issues about this, so hopefully someone can just give me general pointers. >> >> I am using the `pycuda.autoinit.context` for all the Reikna `Thread`s. >> Neither reikna nor scikit-cuda seem to have functionality for setting >> context globally or passing it to functions >> I have tried using my own context instead, but meet the same issues >> >> What are the steps in general to ensure these issues don't come up? How can >> I handle this when multiple files are in play? >> >> Cheers, >> Rasmus >> >> _______________________________________________ >> PyCUDA mailing list >> PyCUDA@tiker.net <mailto:PyCUDA@tiker.net> >> https://lists.tiker.net/listinfo/pycuda >> <https://lists.tiker.net/listinfo/pycuda> >> >> > >
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