Handling numpy
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 25/100
Research direction
No source file or test is named. Start by reproducing the WorkerLostError while a worker processes a loop of large .npy files with np.load, then inspect worker termination details and memory behavior beyond local hardware monitoring. Done means identifying a reproducible cause and a documented resolution.
Written by the indexing model from the issue text.
Description
Hello everyone, I'm not very familiar with Celery, so I'm seeking help. Currently, I'm working with NumPy using Celery, and during a loop where I'm loading .npy files with np.load, I'm encountering a "WorkerLostError." I've already tried memory mapping, and the .npy files are quite large (about 1500 x 3000). I've been continuously monitoring my local hardware, but there's no indication of overflow. Does anyone know how to resolve this issue?
- Dominant language
- Python
- Stars
- 436
- Forks
- 273
- Avg merge
- 4d 19h
- Merged PRs (30d)
- 5
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