- Update to Release 0.13.1:
* Memory now accepts pathlib.Path objects as ``location``
parameter. Also, a warning is raised if the returned backend
is None while ``location`` is not None.
* Make ``Parallel`` raise an informative ``RuntimeError`` when
the active parallel backend has zero worker.
* Make the ``DaskDistributedBackend`` wait for workers before
trying to schedule work. This is useful in particular when
the workers are provisionned dynamically but provisionning is
not immediate (for instance using Kubernetes, Yarn or an HPC
job queue).
OBS-URL: https://build.opensuse.org/request/show/669903
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python/python-joblib?expand=0&rev=22
- update to Release 0.13.0
* Include loky 2.4.2 with default serialization with ``cloudpickle``.
This can be tweaked with the environment variable ``LOKY_PICKLER``.
* Fix nested backend in SequentialBackend to avoid changing the default
backend to Sequential. (#792)
* Fix nested_backend behavior to avoid setting the default number of
workers to -1 when the backend is not dask. (#784)
- Update to Release 0.12.5
* Include loky 2.3.1 with better error reporting when a worker is
abruptly terminated. Also fixes spurious debug output.
* Include cloudpickle 0.5.6. Fix a bug with the handling of global
variables by locally defined functions.
- Update to Release 0.12.4
* Include loky 2.3.0 with many bugfixes, notably w.r.t. when setting
non-default multiprocessing contexts. Also include improvement on
memory management of long running worker processes and fixed issues
when using the loky backend under PyPy.
* Raises a more explicit exception when a corrupted MemorizedResult is loaded.
* Loading a corrupted cached file with mmap mode enabled would
recompute the results and return them without memmory mapping.
- Update to Release 0.12.3
* Fix joblib import setting the global start_method for multiprocessing.
* Fix MemorizedResult not picklable (#747).
* Fix Memory, MemorizedFunc and MemorizedResult round-trip pickling +
unpickling (#746).
* Fixed a regression in Memory when positional arguments are called as
kwargs several times with different values (#751).
* Integration of loky 2.2.2 that fixes issues with the selection of the
default start method and improve the reporting when calling functions
with arguments that raise an exception when unpickling.
* Prevent MemorizedFunc.call_and_shelve from loading cached results to
RAM when not necessary. Results in big performance improvements
- Update to Release 0.12.2
* Integrate loky 2.2.0 to fix regression with unpicklable arguments and
functions reported by users (#723, #643).
* Loky 2.2.0 also provides a protection against memory leaks long running
applications when psutil is installed (reported as #721).
* Joblib now includes the code for the dask backend which has been updated
to properly handle nested parallelism and data scattering at the same
time (#722).
* Restored some private API attribute and arguments
(`MemorizedResult.argument_hash` and `BatchedCalls.__init__`'s
`pickle_cache`) for backward compat. (#716, #732).
* Fix a deprecation warning message (for `Memory`'s `cachedir`) (#720).
OBS-URL: https://build.opensuse.org/request/show/663427
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python/python-joblib?expand=0&rev=20
- 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
OBS-URL: https://build.opensuse.org/request/show/624255
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-joblib?expand=0&rev=2
