python-joblib
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- Developed at devel:languages:python
- Sources inherited from project openSUSE:Factory
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Source Files
Filename | Size | Changed |
---|---|---|
joblib-0.12.1.tar.gz | 0000268415 262 KB | |
python-joblib.changes | 0000005197 5.08 KB | |
python-joblib.spec | 0000002235 2.18 KB |
Revision 2 (latest revision is 26)
- Enable tests - specfile: * remove devel requirement - update to version 0.12.1: * Make sure that any exception triggered when serializing jobs in the queue will be wrapped as a PicklingError as in past versions of joblib. * Fix kwonlydefaults key error in filter_args (#715) - changes from version 0.12: * Implement the 'loky' backend with @ogrisel. This backend relies on a robust implementation of concurrent.futures.ProcessPoolExecutor with spawned processes that can be reused accross the Parallel calls. This fixes the bad interation with third paty libraries relying on thread pools, described in https://pythonhosted.org/joblib/parallel.html#bad-interaction-of-multiprocessing-and-third-party-libraries * Limit the number of threads used in worker processes by C-libraries that relies on threadpools. This functionality works for MKL, OpenBLAS, OpenMP and Accelerated. * Prevent numpy arrays with the same shape and data from hashing to the same memmap, to prevent jobs with preallocated arrays from writing over each other. * Reduce overhead of automatic memmap by removing the need to hash the array. * Make Memory.cache robust to PermissionError (errno 13) under Windows when run in combination with Parallel. * The automatic array memory mapping feature of Parallel does no longer use /dev/shm if it is too small (less than 2 GB). In particular in docker containers /dev/shm is only 64 MB by default which would cause frequent failures when running joblib in Docker
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