Revisions of python-joblib
Dominique Leuenberger (dimstar_suse)
accepted
request 662604
from
Tomáš Chvátal (scarabeus_iv)
(revision 4)
- Disable blas test as it is very flaky outside of x86_64
Dominique Leuenberger (dimstar_suse)
accepted
request 625713
from
Tomáš Chvátal (scarabeus_iv)
(revision 3)
Dominique Leuenberger (dimstar_suse)
accepted
request 624255
from
Tomáš Chvátal (scarabeus_iv)
(revision 2)
- 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
Dominique Leuenberger (dimstar_suse)
accepted
request 574766
from
Ondřej Súkup (mimi_vx)
(revision 1)
python joblib
Displaying revisions 21 - 26 of 26