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python-joblib
python-joblib.changes
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File python-joblib.changes of Package python-joblib
------------------------------------------------------------------- Fri Oct 30 08:23:38 UTC 2020 - pgajdos@suse.com - disable test_hash_numpy_noncontiguous, test_hashes_are_different_between_c_and_fortran_contiguous_arrays, test_hashes_stay_the_same_with_numpy_objects, test_non_contiguous_array_pickling [bsc#1177209] ------------------------------------------------------------------- Tue Oct 27 12:01:23 UTC 2020 - pgajdos@suse.com - disable test_nested_loop_error_in_grandchild_resource_tracker_silent [bsc#1177209] ------------------------------------------------------------------- Mon Oct 5 14:18:31 UTC 2020 - pgajdos@suse.com - disable yet another tests [bsc#1177209] ------------------------------------------------------------------- Tue Sep 8 08:26:41 UTC 2020 - Guillaume GARDET <guillaume.gardet@opensuse.org> - Disable tests failing often in OBS: * joblib-disable-unrelialble-tests.patch ------------------------------------------------------------------- Fri Aug 21 08:40:04 UTC 2020 - Michel Normand <normand@linux.vnet.ibm.com> - New disable_test_on_big_endian.patch as per upstream issue https://github.com/joblib/joblib/issues/279 ------------------------------------------------------------------- Sat Jul 18 09:12:26 UTC 2020 - Dirk Mueller <dmueller@suse.com> - update to 0.16.0 - Fix a problem in the constructors of of Parallel backends classes that inherit from the `AutoBatchingMixin` that prevented the dask backend to properly batch short tasks. https://github.com/joblib/joblib/pull/1062 - Fix a problem in the way the joblib dask backend batches calls that would badly interact with the dask callable pickling cache and lead to wrong results or errors. https://github.com/joblib/joblib/pull/1055 - Prevent a dask.distributed bug from surfacing in joblib's dask backend during nested Parallel calls (due to joblib's auto-scattering feature) https://github.com/joblib/joblib/pull/1061 - Workaround for a race condition after Parallel calls with the dask backend that would cause low level warnings from asyncio coroutines: https://github.com/joblib/joblib/pull/1078 ------------------------------------------------------------------- Tue Jun 2 16:24:43 UTC 2020 - Dirk Mueller <dmueller@suse.com> - update to 0.15.1: - Make joblib work on Python 3 installation that do not ship with the lzma package in their standard library. - Drop support for Python 2 and Python 3.5. All objects in ``joblib.my_exceptions`` and ``joblib.format_stack`` are now deprecated and will be removed in joblib 0.16. Note that no deprecation warning will be raised for these objects Python < 3.7. https://github.com/joblib/joblib/pull/1018 - Fix many bugs related to the temporary files and folder generated when automatically memory mapping large numpy arrays for efficient inter-process communication. In particular, this would cause `PermissionError` exceptions to be raised under Windows and large leaked files in `/dev/shm` under Linux in case of crash. https://github.com/joblib/joblib/pull/966 - Make the dask backend collect results as soon as they complete leading to a performance improvement: https://github.com/joblib/joblib/pull/1025 - Fix the number of jobs reported by ``effective_n_jobs`` when ``n_jobs=None`` called in a parallel backend context. https://github.com/joblib/joblib/pull/985 - Upgraded vendored cloupickle to 1.4.1 and loky to 2.8.0. This allows for Parallel calls of dynamically defined functions with type annotations in particular. ------------------------------------------------------------------- Thu Mar 5 13:33:14 UTC 2020 - pgajdos@suse.com - version update to 0.14.1 - Configure the loky workers' environment to mitigate oversubsription with nested multi-threaded code in the following case: - allow for a suitable number of threads for numba (``NUMBA_NUM_THREADS``); - enable Interprocess Communication for scheduler coordination when the nested code uses Threading Building Blocks (TBB) (``ENABLE_IPC=1``) https://github.com/joblib/joblib/pull/951 - Fix a regression where the loky backend was not reusing previously spawned workers. https://github.com/joblib/joblib/pull/968 - Revert https://github.com/joblib/joblib/pull/847 to avoid using `pkg_resources` that introduced a performance regression under Windows: https://github.com/joblib/joblib/issues/965 - Improved the load balancing between workers to avoid stranglers caused by an excessively large batch size when the task duration is varying significantly (because of the combined use of ``joblib.Parallel`` and ``joblib.Memory`` with a partially warmed cache for instance). https://github.com/joblib/joblib/pull/899 - Add official support for Python 3.8: fixed protocol number in `Hasher` and updated tests. - Fix a deadlock when using the dask backend (when scattering large numpy arrays). https://github.com/joblib/joblib/pull/914 - Warn users that they should never use `joblib.load` with files from untrusted sources. Fix security related API change introduced in numpy 1.6.3 that would prevent using joblib with recent numpy versions. https://github.com/joblib/joblib/pull/879 - Upgrade to cloudpickle 1.1.1 that add supports for the upcoming Python 3.8 release among other things. https://github.com/joblib/joblib/pull/878 - Fix semaphore availability checker to avoid spawning resource trackers on module import. https://github.com/joblib/joblib/pull/893 - Fix the oversubscription protection to only protect against nested `Parallel` calls. This allows `joblib` to be run in background threads. https://github.com/joblib/joblib/pull/934 - Fix `ValueError` (negative dimensions) when pickling large numpy arrays on Windows. https://github.com/joblib/joblib/pull/920 - Upgrade to loky 2.6.0 that add supports for the setting environment variables in child before loading any module. https://github.com/joblib/joblib/pull/940 - Fix the oversubscription protection for native libraries using threadpools (OpenBLAS, MKL, Blis and OpenMP runtimes). The maximal number of threads is can now be set in children using the ``inner_max_num_threads`` in ``parallel_backend``. It defaults to ``cpu_count() // n_jobs``. https://github.com/joblib/joblib/pull/940 - deleted patches - numpy16.patch (upstreamed) ------------------------------------------------------------------- Tue May 28 10:34:57 UTC 2019 - Tomáš Chvátal <tchvatal@suse.com> - Switch to %pytest - Add patch to work well with new numpy: * numpy16.patch ------------------------------------------------------------------- Tue Mar 26 14:45:24 UTC 2019 - Tomáš Chvátal <tchvatal@suse.com> - Update to 0.13.2: * Upgrade to cloudpickle 0.8.0 * Add a non-regression test related to joblib issues #836 and #833, reporting that cloudpickle versions between 0.5.4 and 0.7 introduced a bug where global variables changes in a parent process between two calls to joblib.Parallel would not be propagated into the workers ------------------------------------------------------------------- Thu Mar 7 15:57:20 UTC 2019 - John Vandenberg <jayvdb@gmail.com> - Remove no longer necessary pytest argument -k 'not test_no_blas_crash_or_freeze_with_subprocesses' ------------------------------------------------------------------- Tue Jan 29 16:41:57 CET 2019 - mcepl@suse.com - 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). ------------------------------------------------------------------- Mon Jan 7 19:39:00 UTC 2019 - Todd R <toddrme2178@gmail.com> - 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). ------------------------------------------------------------------- Thu Jan 3 07:07:28 UTC 2019 - Tomáš Chvátal <tchvatal@suse.com> - Disable blas test as it is very flaky outside of x86_64 ------------------------------------------------------------------- Fri Jul 27 06:35:25 UTC 2018 - jengelh@inai.de - Use noun phrase in summary. ------------------------------------------------------------------- Fri Jul 20 11:48:47 UTC 2018 - tchvatal@suse.com - Enable tests ------------------------------------------------------------------- Wed Jul 18 03:03:33 UTC 2018 - arun@gmx.de - 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 containers. * Make it possible to hint for thread-based parallelism with prefer='threads' or enforce shared-memory semantics with require='sharedmem'. * Rely on the built-in exception nesting system of Python 3 to preserve traceback information when an exception is raised on a remote worker process. This avoid verbose and redundant exception reports under Python 3. * Preserve exception type information when doing nested Parallel calls instead of mapping the exception to the generic JoblibException type. * Introduce the concept of 'store' and refactor the Memory internal storage implementation to make it accept extra store backends for caching results. backend and backend_options are the new options added to Memory to specify and configure a store backend. * Add the register_store_backend function to extend the store backend used by default with Memory. This default store backend is named 'local' and corresponds to the local filesystem. * The store backend API is experimental and thus is subject to change in the future without deprecation. * The cachedir parameter of Memory is now marked as deprecated, use location instead. * Add support for LZ4 compression if lz4 package is installed. * Add register_compressor function for extending available compressors. * Allow passing a string to compress parameter in dump funtion. This string should correspond to the compressor used (e.g. zlib, gzip, lz4, etc). The default compression level is used in this case. * Allow parallel_backend to be used globally instead of only as a context manager. Support lazy registration of external parallel backends ------------------------------------------------------------------- Thu Jan 11 22:12:57 UTC 2018 - jengelh@inai.de - Ensure neutrality of description. ------------------------------------------------------------------- Mon May 22 16:35:59 UTC 2017 - toddrme2178@gmail.com - Implement single-spec version. - Run tests. - Fix source URL. - Update to version 0.11. * For a full changelog please see: https://github.com/joblib/joblib/blob/0.11/CHANGES.rst ------------------------------------------------------------------- Sun May 24 13:14:03 UTC 2015 - toddrme2178@gmail.com - Disable non-functional documentation ------------------------------------------------------------------- Sun Dec 8 19:47:59 UTC 2013 - p.drouand@gmail.com - Update to version 0.7.1 + MISC: capture meaningless argument (n_jobs=0) in Parallel + ENH Handles tuples, sets and Python 3's dict_keys type the same as lists. in pre_dispatch + ENH: fix function caching for IPython ------------------------------------------------------------------- Thu Oct 24 11:07:22 UTC 2013 - speilicke@suse.com - Require python-setuptools instead of distribute (upstreams merged) ------------------------------------------------------------------- Sat Feb 9 15:54:15 UTC 2013 - p.drouand@gmail.com - Update to version 0.7.0d * No changelog available - Clean the specfile and remove .buildinfo file ------------------------------------------------------------------- Wed Oct 10 20:59:57 UTC 2012 - scorot@free.fr - Add Group field in doc package for SLE 11 ------------------------------------------------------------------- Wed Sep 5 17:10:35 UTC 2012 - toddrme2178@gmail.com - Initial version
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