Revisions of python-joblib
- update to 1.4.2: * Due to maintenance issues, 1.4.1 was not valid and we bumped the version to 1.4.2 * Fix a backward incompatible change in MemorizedFunc.call which needs to return the metadata. Also make sure that NotMemorizedFunc.call return an empty dict for metadata for consistency. https://github.com/joblib/joblib/pull/1576
- update to 1.4.0: * Allow caching co-routines with Memory.cache. * Try to cast n_jobs to int in parallel and raise an error if it fails. This means that n_jobs=2.3 will now result in effective_n_jobs=2 instead of failing. * Ensure that errors in the task generator given to Parallel's call are raised in the results consumming thread. * Adjust codebase to NumPy 2.0 by changing np.NaN to np.nan and importing byte_bounds from np.lib.array_utils. * The parameter return_as in joblib.Parallel can now be set to generator_unordered. In this case the results will be returned in the order of task completion rather than the order of submission. * dask backend now supports return_as=generator and return_as=generator_unordered. * Vendor cloudpickle 3.0.0 and end support for Python 3.7 which has reached end of life. - drop avoid-deprecated-ast.patch (upstream)
- Add patch avoid-deprecated-ast.patch: * Avoid deprecated ast classes. - Add patch also-filter-new-fork-warning.patch: * Filter DeprecationWarning due to calling fork() with multiprocessing. - Switch to pyproject macros.
- update to 1.3.2: * FIX treat n_jobs=None as if left to its default value * FIX Init logger parent class in Parallel * MNT remove unnecessary .bck file * MTN adjust test regex for Python 3.12 improved error message * DOC add public documentation for parallel_backend * FIX flake8 new E721: type comparison * Ensure native byte order for memmap. * Drop runtime dependency on `distutils` * Add environment variable to change default parallel backend * Fix memmapping_reducer when 'os' has no attribute 'statvfs' * Move the metadata into `pyproject.toml` * TST Close client in test_pickle_in_socket * Do not swallow PicklingError * FIX Avoid collisions when caching nested functions * FIX heisenfailure in doc/memory.rst * MAINT Explicit support for Python 3.11 * MNT Use faulthandler rather than custom autokill logic * BENCH add benchmark script for n_jobs=1 * TST Fix test_nested_parallel_warnings_parent_backend for Python nogil * TST Fix test_memmapping for Python nogil * MAINT Clean deprecations * ENH make temp resource cleanup safer * MAINT Simplify warning in `_persist_input` * MNT Use full flake8 rather than flake8_diff.sh * Update Dask backend * FIX upload to codecov * MTN vendor loky 3.4.0 * MTN skip thread_bomb mitigation test on PyPy for now
- Update to 1.2.0 (CVE-2022-21797, bsc#1204232) * Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported. #1327 * Make sure that joblib works even when multiprocessing is not available, for instance with Pyodide #1256 * Avoid unnecessary warnings when workers and main process delete the temporary memmap folder contents concurrently. #1263 * Vendor loky 3.1.0 with several fixes to more robustly forcibly terminate worker processes in case of a crash. #1269 * Fix memory alignment bug for pickles containing numpy arrays. This is especially important when loading the pickle with mmap_mode != None as the resulting numpy.memmap object would not be able to correct the misalignment without performing a memory copy. This bug would cause invalid computation and segmentation faults with native code that would directly access the underlying data buffer of a numpy array, for instance C/C++/Cython code compiled with older GCC versions or some old OpenBLAS written in platform specific assembly. #1254 * Vendor cloudpickle 2.2.0 which adds support for PyPy 3.8+. * Vendor loky 3.3.0 which fixes a bug with leaking processes in case of nested loky parallel calls and more reliability spawn the correct number of reusable workers. - Drop support-setuptools-62.patch
- Add patch support-setuptools-62.patch: * Support setuptools >= 62 by handling more than one warning in a test case.
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- 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
- 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.
- Switch to %pytest - Add patch to work well with new numpy: * numpy16.patch
- 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
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