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Random Segmentation fault with embedding.search after upserting #498
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tested with fresh conda environment, empty embedding instance and same condition. (e.g. 10 read/s, then rlock with an upsert+persist, 10 read/s continues). I thought some of my installation could be dodgy but it seems a bit random that seg fault happens in tqdm🤔
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This is a tricky one, unfortunately I don't have a good answer for you. There is a known issue between Faiss and PyTorch on macOS regarding OpenMP conflicts. More information on this issue can be found in the following links. Perhaps one of those can give you a hint on things to try.
Some ideas from those threads.
I also recommend upvoting some of those other issues to attempt to get them more visibility. This issue appears to be the root cause. |
I was able to get the macOS build scripts to work with the latest version of PyTorch and setting the OMP_NUM_THREADS=1 variable. This is probably the best bet. |
thanks will give this a go tonight. Which version of pytorch are you building with? |
@davidmezzetti Thanks again for the help and advice. A bit of further digging suggests the random seg fault I'm experiencing is not related to this library or mac specific setup at all. crash logs are consistent suggesting that pyobjc(as subdependency) is the culprit. Isolating the pyobjc part seemed to have resolved the issue. @davidmezzetti Cheers for looking into this!
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the root cause of this seemed to be that
the fix was to use |
Glad you were able to resolve it. |
I'm getting segmentation fault "randomly" when using with FastApi. It usually happens after the index is updated.
3 example errors below when running some tests where the setup is:
RLock A
)embedding.upsert
+embedding.persist
for ~2k docs tasking roughly 5 seconds to index. (withRLock A
)I'm a little stuck as the memory usage doesn't seem too bad with 800mb(its an existing index+db. about 400mb without loading the index). Any idea how I can debug this?
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