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
Memory now accepts pathlib.Path objects as
locationparameter. Also, a warning is raised if the returned backend is None while
locationis not None.
Parallelraise an informative
RuntimeErrorwhen the active parallel backend has zero worker.
DaskDistributedBackendwait 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).
Include loky 2.4.2 with default serialization with
cloudpickle. This can be tweaked with the environment variable
Fix nested backend in SequentialBackend to avoid changing the default backend to Sequential. (#792)
Thomas Moreau, Olivier Grisel
Fix nested_backend behavior to avoid setting the default number of workers to -1 when the backend is not dask. (#784)
Thomas Moreau, Olivier Grisel
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.
Thomas Moreau, Pierre Glaser, Olivier Grisel
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.
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).
Thomas Moreau and Olivier Grisel
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
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).
Alexandre Abadie and Olivier Grisel
Restored some private API attribute and arguments (MemorizedResult.argument_hash and BatchedCalls.__init__'s pickle_cache) for backward compat. (#716, #732).
Joris Van den Bossche
Fix a deprecation warning message (for Memory's cachedir) (#720).
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)
'loky'backend with @ogrisel. This backend relies on a robust implementation of
concurrent.futures.ProcessPoolExecutorwith spawned processes that can be reused accross the
Parallelcalls. 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.
PermissionError (errno 13)under Windows when run in combination with
The automatic array memory mapping feature of
Paralleldoes no longer use
/dev/shmif it is too small (less than 2 GB). In particular in docker containers
/dev/shmis 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
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
Introduce the concept of 'store' and refactor the
Memoryinternal storage implementation to make it accept extra store backends for caching results.
backend_optionsare the new options added to
Memoryto specify and configure a store backend.
register_store_backendfunction 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.
Memoryis now marked as deprecated, use
Add support for LZ4 compression if
lz4package is installed.
register_compressorfunction for extending available compressors.
Allow passing a string to
dumpfuntion. This string should correspond to the compressor used (e.g. zlib, gzip, lz4, etc). The default compression level is used in this case.
parallel_backendto be used globally instead of only as a context manager. Support lazy registration of external parallel backends
Remove support for python 2.6
Remove deprecated format_signature, format_call and load_output functions from Memory API.
Add initial implementation of LRU cache cleaning. You can specify the size limit of a
Memoryobject via the
bytes_limitparameter and then need to clean explicitly the cache via the
Make the multiprocessing backend work even when the name of the main thread is not the Python default. Thanks to Roman Yurchak for the suggestion.
pytest is used to run the tests instead of nosetests.
python setup.py testor
python setup.py nosetestsdo not work anymore, run
An instance of
joblib.ParallelBackendBasecan be passed into the
Fix handling of memmap objects with offsets greater than mmap.ALLOCATIONGRANULARITY in
joblib.Parrallel. See https://github.com/joblib/joblib/issues/451 for more details.
Fix performance regression in
joblib.Parallelwith n_jobs=1. See https://github.com/joblib/joblib/issues/483 for more details.
Fix race condition when a function cached with
joblib.Memory.cachewas used inside a
joblib.Parallel. See https://github.com/joblib/joblib/issues/490 for more details.
Fix tests when multiprocessing is disabled via the JOBLIB_MULTIPROCESSING environment variable.
Remove warnings in nested Parallel objects when the inner Parallel has n_jobs=1. See https://github.com/joblib/joblib/pull/406 for more details.
FIX a bug in stack formatting when the error happens in a compiled extension. See https://github.com/joblib/joblib/pull/382 for more details.
FIX a bug in the constructor of BinaryZlibFile that would throw an exception when passing unicode filename (Python 2 only). See https://github.com/joblib/joblib/pull/384 for more details.
Expose :class:`joblib.parallel.ParallelBackendBase` and :class:`joblib.parallel.AutoBatchingMixin` in the public API to make them officially re-usable by backend implementers.
ENH: joblib.dump/load now accept file-like objects besides filenames. https://github.com/joblib/joblib/pull/351 for more details.
Niels Zeilemaker and Olivier Grisel
Refactored joblib.Parallel to enable the registration of custom computational backends. https://github.com/joblib/joblib/pull/306 Note the API to register custom backends is considered experimental and subject to change without deprecation.
Joblib pickle format change: joblib.dump always create a single pickle file and joblib.dump/joblib.save never do any memory copy when writing/reading pickle files. Reading pickle files generated with joblib versions prior to 0.10 will be supported for a limited amount of time, we advise to regenerate them from scratch when convenient. joblib.dump and joblib.load also support pickle files compressed using various strategies: zlib, gzip, bz2, lzma and xz. Note that lzma and xz are only available with python >= 3.3. https://github.com/joblib/joblib/pull/260 for more details.
ENH: joblib.dump/load now accept pathlib.Path objects as filenames. https://github.com/joblib/joblib/pull/316 for more details.
Workaround for "WindowsError: [Error 5] Access is denied" when trying to terminate a multiprocessing pool under Windows: https://github.com/joblib/joblib/issues/354
FIX a race condition that could cause a joblib.Parallel to hang when collecting the result of a job that triggers an exception. https://github.com/joblib/joblib/pull/296
FIX a bug that caused joblib.Parallel to wrongly reuse previously memmapped arrays instead of creating new temporary files. https://github.com/joblib/joblib/pull/294 for more details.
FIX for raising non inheritable exceptions in a Parallel call. See https://github.com/joblib/joblib/issues/269 for more details.
FIX joblib.hash error with mixed types sets and dicts containing mixed types keys when using Python 3. see https://github.com/joblib/joblib/issues/254
FIX joblib.dump/load for big numpy arrays with dtype=object. See https://github.com/joblib/joblib/issues/220 for more details.
FIX joblib.Parallel hanging when used with an exhausted iterator. See https://github.com/joblib/joblib/issues/292 for more details.
Revert back to the
forkstart method (instead of
forkserver) as the latter was found to cause crashes in interactive Python sessions.
Joblib hashing now uses the default pickle protocol (2 for Python 2 and 3 for Python 3). This makes it very unlikely to get the same hash for a given object under Python 2 and Python 3.
In particular, for Python 3 users, this means that the output of joblib.hash changes when switching from joblib 0.8.4 to 0.9.2 . We strive to ensure that the output of joblib.hash does not change needlessly in future versions of joblib but this is not officially guaranteed.
Joblib pickles generated with Python 2 can not be loaded with Python 3 and the same applies for joblib pickles generated with Python 3 and loaded with Python 2.
During the beta period 0.9.0b2 to 0.9.0b4, we experimented with a joblib serialization that aimed to make pickles serialized with Python 3 loadable under Python 2. Unfortunately this serialization strategy proved to be too fragile as far as the long-term maintenance was concerned (For example see https://github.com/joblib/joblib/pull/243). That means that joblib pickles generated with joblib 0.9.0bN can not be loaded under joblib 0.9.2. Joblib beta testers, who are the only ones likely to be affected by this, are advised to delete their joblib cache when they upgrade from 0.9.0bN to 0.9.2.
Fixed a bug with
joblib.hashthat used to return unstable values for strings and numpy.dtype instances depending on interning states.
Make joblib use the 'forkserver' start method by default under Python 3.4+ to avoid causing crash with 3rd party libraries (such as Apple vecLib / Accelerate or the GCC OpenMP runtime) that use an internal thread pool that is not not reinitialized when a
forksystem call happens.
New context manager based API (
withblock) to re-use the same pool of workers across consecutive parallel calls.
Vlad Niculae and Olivier Grisel
Automated batching of fast tasks into longer running jobs to hide multiprocessing dispatching overhead when possible.
FIX make it possible to call
joblib.load(filename, mmap_mode='r')on pickled objects that include a mix of arrays of both memory memmapable dtypes and object dtype.
2014-11-20 Olivier Grisel
OPTIM use the C-optimized pickler under Python 3
This makes it possible to efficiently process parallel jobs that deal with numerous Python objects such as large dictionaries.
2014-08-19 Olivier Grisel
FIX disable memmapping for object arrays
2014-08-07 Lars Buitinck
MAINT NumPy 1.10-safe version comparisons
2014-07-11 Olivier Grisel
FIX #146: Heisen test failure caused by thread-unsafe Python lists
This fix uses a queue.Queue datastructure in the failing test. This datastructure is thread-safe thanks to an internal Lock. This Lock instance not picklable hence cause the picklability check of delayed to check fail.
When using the threading backend, picklability is no longer required, hence this PRs give the user the ability to disable it on a case by case basis.
2014-06-30 Olivier Grisel
BUG: use mmap_mode='r' by default in Parallel and MemmappingPool
The former default of mmap_mode='c' (copy-on-write) caused problematic use of the paging file under Windows.
2014-06-27 Olivier Grisel
BUG: fix usage of the /dev/shm folder under Linux
2014-05-29 Gael Varoquaux
BUG: fix crash with high verbosity
2014-05-14 Olivier Grisel
Fix a bug in exception reporting under Python 3
2014-05-10 Olivier Grisel
Fixed a potential segfault when passing non-contiguous memmap instances.
2014-04-22 Gael Varoquaux
ENH: Make memory robust to modification of source files while the interpreter is running. Should lead to less spurious cache flushes and recomputations.
2014-02-24 Philippe Gervais
Memory.call_and_shelveAPI to handle memoized results by reference instead of by value.
2014-01-10 Olivier Grisel & Gael Varoquaux
FIX #105: Race condition in task iterable consumption when pre_dispatch != 'all' that could cause crash with error messages "Pools seems closed" and "ValueError: generator already executing".
2014-01-12 Olivier Grisel
FIX #72: joblib cannot persist "output_dir" keyword argument.
2013-12-23 Olivier Grisel
ENH: set default value of Parallel's max_nbytes to 100MB
Motivation: avoid introducing disk latency on medium sized parallel workload where memory usage is not an issue.
FIX: properly handle the JOBLIB_MULTIPROCESSING env variable
FIX: timeout test failures under windows
2013-12-19 Olivier Grisel
FIX: support the new Python 3.4 multiprocessing API
2013-12-05 Olivier Grisel
ENH: make Memory respect mmap_mode at first call too
ENH: add a threading based backend to Parallel
This is low overhead alternative backend to the default multiprocessing backend that is suitable when calling compiled extensions that release the GIL.
Author: Dan Stahlke <firstname.lastname@example.org> Date: 2013-11-08
FIX: use safe_repr to print arg vals in trace
This fixes a problem in which extremely long (and slow) stack traces would be produced when function parameters are large numpy arrays.
2013-09-10 Olivier Grisel
ENH: limit memory copy with Parallel by leveraging numpy.memmap when possible
2013-07-25 Gael Varoquaux
MISC: capture meaningless argument (n_jobs=0) in Parallel
2013-07-09 Lars Buitinck
ENH Handles tuples, sets and Python 3's dict_keys type the same as lists. in pre_dispatch
2013-05-23 Martin Luessi
ENH: fix function caching for IPython
This release drops support for Python 2.5 in favor of support for Python 3.0
2013-02-13 Gael Varoquaux
BUG: fix nasty hash collisions
2012-11-19 Gael Varoquaux
ENH: Parallel: Turn of pre-dispatch for already expanded lists
Gael Varoquaux 2012-11-19
ENH: detect recursive sub-process spawning, as when people do not protect the __main__ in scripts under Windows, and raise a useful error.
Gael Varoquaux 2012-11-16
ENH: Full python 3 support
2012-09-15 Yannick Schwartz
BUG: make sure that sets and dictionnaries give reproducible hashes
2012-07-18 Marek Rudnicki
BUG: make sure that object-dtype numpy array hash correctly
BUG: Bad default n_jobs for Parallel
2012-05-07 Vlad Niculae
ENH: controlled randomness in tests and doctest fix
ENH: add verbosity in memory
BUG: non-reproducible hashing: order of kwargs
The ordering of a dictionnary is random. As a result the function hashing was not reproducible. Pretty hard to test
BUG: fix joblib Memory pickling
BUG: fix hasher with Python 3
API: filter_args: *args, **kwargs -> args, kwargs
2012-02-06 Gael Varoquaux
BUG: make sure Memory pickles even if cachedir=None
Bugfix release because of a merge error in release 0.6.0
2012-01-11 Gael Varoquaux
BUG: ensure compatibility with old numpy
DOC: update installation instructions
BUG: file semantic to work under Windows
2012-01-10 Yaroslav Halchenko
BUG: a fix toward 2.5 compatibility
2012-01-07 Gael Varoquaux
ENH: hash: bugware to be able to hash objects defined interactively in IPython
2012-01-07 Gael Varoquaux
ENH: Parallel: warn and not fail for nested loops
ENH: Parallel: n_jobs=-2 now uses all CPUs but one
2012-01-01 Juan Manuel Caicedo Carvajal and Gael Varoquaux
ENH: add verbosity levels in Parallel
2011-12-28 Gael varoquaux
API: zipped -> compress
2011-12-26 Gael varoquaux
ENH: Add a zipped option to Memory
API: Memory no longer accepts save_npy
2011-12-22 Kenneth C. Arnold and Gael varoquaux
BUG: fix numpy_pickle for array subclasses
2011-12-21 Gael varoquaux
ENH: add zip-based pickling
2011-12-19 Fabian Pedregosa
Py3k: compatibility fixes. This makes run fine the tests test_disk and test_parallel
2011-12-11 Lars Buitinck
ENH: Replace os.path.exists before makedirs with exception check New disk.mkdirp will fail with other errnos than EEXIST.
2011-12-10 Bala Subrahmanyam Varanasi
MISC: pep8 compliant
2011-19-10 Fabian Pedregosa
ENH: Make joblib installable under Python 3.X
2011-09-29 Jon Olav Vik
BUG: Make mangling path to filename work on Windows
2011-09-25 Olivier Grisel
FIX: doctest heisenfailure on execution time
2011-08-24 Ralf Gommers
STY: PEP8 cleanup.
2011-06-25 Gael varoquaux
API: All the usefull symbols in the __init__
2011-06-25 Gael varoquaux
ENH: Add cpu_count
2011-06-06 Gael varoquaux
ENH: Make sure memory hash in a reproducible way
2011-04-12 Gael varoquaux
TEST: Better testing of parallel and pre_dispatch
Yaroslav Halchenko 2011-04-12
DOC: quick pass over docs -- trailing spaces/spelling
Yaroslav Halchenko 2011-04-11
ENH: JOBLIB_MULTIPROCESSING env var to disable multiprocessing from the environment
Alexandre Gramfort 2011-04-08
ENH : adding log message to know how long it takes to load from disk the cache
2011-04-01 Gael varoquaux
BUG: pickling MemoizeFunc does not store timestamp
2011-03-31 Nicolas Pinto
TEST: expose hashing bug with cached method
2011-03-26...2011-03-27 Pietro Berkes
BUG: fix error management in rm_subdirs BUG: fix for race condition during tests in mem.clear()
Gael varoquaux 2011-03-22...2011-03-26
TEST: Improve test coverage and robustness
Gael varoquaux 2011-03-19
BUG: hashing functions with only *var **kwargs
Gael varoquaux 2011-02-01... 2011-03-22
BUG: Many fixes to capture interprocess race condition when mem.cache is used by several processes on the same cache.
Fabian Pedregosa 2011-02-28
First work on Py3K compatibility
Gael varoquaux 2011-02-27
ENH: pre_dispatch in parallel: lazy generation of jobs in parallel for to avoid drowning memory.
ENH: Add the option of overloading the arguments of the mother 'Memory' object in the cache method that is doing the decoration.
Gael varoquaux 2010-11-21
ENH: Add a verbosity level for more verbosity
Gael varoquaux 2010-11-15
ENH: Deal with interruption in parallel
Gael varoquaux 2010-11-13
BUG: Exceptions raised by Parallel when n_job=1 are no longer captured.
Gael varoquaux 2010-11-13
BUG: Capture wrong arguments properly (better error message)
Pietro Berkes 2010-09-04
BUG: Fix Windows peculiarities with path separators and file names BUG: Fix more windows locking bugs
Gael varoquaux 2010-09-03
ENH: Make sure that exceptions raised in Parallel also inherit from the original exception class ENH: Add a shadow set of exceptions
Fabian Pedregosa 2010-09-01
ENH: Clean up the code for parallel. Thanks to Fabian Pedregosa for the patch.
Gael varoquaux 2010-08-23
BUG: Fix Parallel on computers with only one CPU, for n_jobs=-1.
Gael varoquaux 2010-08-02
BUG: Fix setup.py for extra setuptools args.
Gael varoquaux 2010-07-29
MISC: Silence tests (and hopefuly Yaroslav :P)
Gael Varoquaux 2010-07-22
BUG: Fix hashing for function with a side effect modifying their input argument. Thanks to Pietro Berkes for reporting the bug and proving the patch.
Gael Varoquaux 2010-07-16
BUG: Make sure that joblib still works with Python2.5. => release 0.4.2