Joblib version Azure CI status Documentation Status Codecov coverage

The homepage of joblib with user documentation is located on:

Getting the latest code

To get the latest code using git, simply type:

git clone

If you don’t have git installed, you can download a zip of the latest code:


You can use pip to install joblib:

pip install joblib

from any directory or:

python install

from the source directory.


  • Joblib has no mandatory dependencies besides Python (supported versions are 3.8+).

  • Joblib has an optional dependency on Numpy (at least version 1.6.1) for array manipulation.

  • Joblib includes its own vendored copy of loky for process management.

  • Joblib can efficiently dump and load numpy arrays but does not require numpy to be installed.

  • Joblib has an optional dependency on python-lz4 as a faster alternative to zlib and gzip for compressed serialization.

  • Joblib has an optional dependency on psutil to mitigate memory leaks in parallel worker processes.

  • Some examples require external dependencies such as pandas. See the instructions in the Building the docs section for details.

Workflow to contribute

To contribute to joblib, first create an account on github. Once this is done, fork the joblib repository to have your own repository, clone it using ‘git clone’ on the computers where you want to work. Make your changes in your clone, push them to your github account, test them on several computers, and when you are happy with them, send a pull request to the main repository.

Running the test suite

To run the test suite, you need the pytest (version >= 3) and coverage modules. Run the test suite using:

pytest joblib

from the root of the project.

Building the docs

To build the docs you need to have sphinx (>=1.4) and some dependencies installed:

pip install -U -r .readthedocs-requirements.txt

The docs can then be built with the following command:

make doc

The html docs are located in the doc/_build/html directory.

Making a source tarball

To create a source tarball, eg for packaging or distributing, run the following command:

python sdist

The tarball will be created in the dist directory. This command will compile the docs, and the resulting tarball can be installed with no extra dependencies than the Python standard library. You will need setuptool and sphinx.

Making a release and uploading it to PyPI

This command is only run by project manager, to make a release, and upload in to PyPI:

python sdist bdist_wheel
twine upload dist/*

Note that the documentation should automatically get updated at each git push. If that is not the case, try building th doc locally and resolve any doc build error (in particular when running the examples).

Updating the changelog

Changes are listed in the CHANGES.rst file. They must be manually updated but, the following git command may be used to generate the lines:

git log --abbrev-commit --date=short --no-merges --sparse

Latest changes

Release 1.4.2 – 2024/05/02

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 which needs to return the metadata. Also make sure that return an empty dict for metadata for consistency.

Release 1.4.0 – 2024/04/08

Release 1.3.2 – 2023/08/08

Release 1.3.1 – 2023/06/29

Release 1.3.0 – 2023/06/28

Release 1.2.0

  • Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported.

  • Make sure that joblib works even when multiprocessing is not available, for instance with Pyodide

  • Avoid unnecessary warnings when workers and main process delete the temporary memmap folder contents concurrently.

  • 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.

  • Vendor cloudpickle 2.2.0 which adds support for PyPy 3.8+.

  • Vendor loky 3.3.0 which fixes several bugs including:

    • robustly forcibly terminating worker processes in case of a crash (;

    • avoiding leaking worker processes in case of nested loky parallel calls;

    • reliability spawn the correct number of reusable workers.

Release 1.1.1

Release 1.1.0

Release 1.0.1

Release 1.0.0

  • Make joblib.hash and joblib.Memory caching system compatible with numpy >= 1.20.0. Also make it explicit in the documentation that users should now expect to have their joblib.Memory cache invalidated when either joblib or a third party library involved in the cached values definition is upgraded. In particular, users updating joblib to a release that includes this fix will see their previous cache invalidated if they contained reference to numpy objects.

  • Remove deprecated check_pickle argument in delayed.

Release 0.17.0

  • Fix a spurious invalidation of Memory.cache’d functions called with Parallel under Jupyter or IPython.

  • Bump vendored loky to 2.9.0 and cloudpickle to 1.6.0. In particular this fixes a problem to add compat for Python 3.9.

Release 0.16.0

Release 0.15.1

  • Make joblib work on Python 3 installation that do not ship with the lzma package in their standard library.

Release 0.15.0

  • 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.

  • 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.

  • Make the dask backend collect results as soon as they complete leading to a performance improvement:

  • Fix the number of jobs reported by effective_n_jobs when n_jobs=None called in a parallel backend context.

  • 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.

Release 0.14.1

Release 0.14.0

Release 0.13.2

Pierre Glaser

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

Release 0.13.1

Pierre Glaser

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.

Olivier Grisel

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).

Release 0.13.0

Thomas Moreau

Include loky 2.4.2 with default serialization with cloudpickle. This can be tweaked with the environment variable LOKY_PICKLER.

Thomas Moreau

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)

Release 0.12.5

Thomas Moreau, Olivier Grisel

Include loky 2.3.1 with better error reporting when a worker is abruptly terminated. Also fixes spurious debug output.

Pierre Glaser

Include cloudpickle 0.5.6. Fix a bug with the handling of global variables by locally defined functions.

Release 0.12.4

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.

Maxime Weyl

Raises a more explicit exception when a corrupted MemorizedResult is loaded.

Maxime Weyl

Loading a corrupted cached file with mmap mode enabled would recompute the results and return them without memory mapping.

Release 0.12.3

Thomas Moreau

Fix joblib import setting the global start_method for multiprocessing.

Alexandre Abadie

Fix MemorizedResult not picklable (#747).

Loïc Estève

Fix Memory, MemorizedFunc and MemorizedResult round-trip pickling + unpickling (#746).

James Collins

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.

Maxime Weyl

Prevent MemorizedFunc.call_and_shelve from loading cached results to RAM when not necessary. Results in big performance improvements

Release 0.12.2

Olivier Grisel

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).

Release 0.12.1

Thomas Moreau

Make sure that any exception triggered when serializing jobs in the queue will be wrapped as a PicklingError as in past versions of joblib.

Noam Hershtig

Fix kwonlydefaults key error in filter_args (#715)

Release 0.12

Thomas Moreau

Implement the 'loky' backend with @ogrisel. This backend relies on a robust implementation of concurrent.futures.ProcessPoolExecutor with spawned processes that can be reused across the Parallel calls. This fixes the bad integration with third paty libraries relying on thread pools, described in

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.

Elizabeth Sander

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.

Olivier Grisel

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.

Alexandre Abadie

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 function. This string should correspond to the compressor used (e.g. zlib, gzip, lz4, etc). The default compression level is used in this case.

Matthew Rocklin

Allow parallel_backend to be used globally instead of only as a context manager. Support lazy registration of external parallel backends

Release 0.11

Alexandre Abadie

Remove support for python 2.6

Alexandre Abadie

Remove deprecated format_signature, format_call and load_output functions from Memory API.

Loïc Estève

Add initial implementation of LRU cache cleaning. You can specify the size limit of a Memory object via the bytes_limit parameter and then need to clean explicitly the cache via the Memory.reduce_size method.

Olivier Grisel

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.

Karan Desai

pytest is used to run the tests instead of nosetests. python test or python nosetests do not work anymore, run pytest joblib instead.

Loïc Estève

An instance of joblib.ParallelBackendBase can be passed into the parallel argument in joblib.Parallel.

Loïc Estève

Fix handling of memmap objects with offsets greater than mmap.ALLOCATIONGRANULARITY in joblib.Parallel. See for more details.

Loïc Estève

Fix performance regression in joblib.Parallel with n_jobs=1. See for more details.

Loïc Estève

Fix race condition when a function cached with joblib.Memory.cache was used inside a joblib.Parallel. See for more details.

Release 0.10.3

Loïc Estève

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 for more details.

Release 0.10.2

Loïc Estève

FIX a bug in stack formatting when the error happens in a compiled extension. See for more details.

Vincent Latrouite

FIX a bug in the constructor of BinaryZlibFile that would throw an exception when passing unicode filename (Python 2 only). See for more details.

Olivier Grisel

Expose joblib.parallel.ParallelBackendBase and joblib.parallel.AutoBatchingMixin in the public API to make them officially re-usable by backend implementers.

Release 0.10.0

Alexandre Abadie

ENH: joblib.dump/load now accept file-like objects besides filenames. for more details.

Niels Zeilemaker and Olivier Grisel

Refactored joblib.Parallel to enable the registration of custom computational backends. Note the API to register custom backends is considered experimental and subject to change without deprecation.

Alexandre Abadie

Joblib pickle format change: joblib.dump always create a single pickle file and joblib.dump/ 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. for more details.

Antony Lee

ENH: joblib.dump/load now accept pathlib.Path objects as filenames. for more details.

Olivier Grisel

Workaround for “WindowsError: [Error 5] Access is denied” when trying to terminate a multiprocessing pool under Windows:

Release 0.9.4

Olivier Grisel

FIX a race condition that could cause a joblib.Parallel to hang when collecting the result of a job that triggers an exception.

Olivier Grisel

FIX a bug that caused joblib.Parallel to wrongly reuse previously memmapped arrays instead of creating new temporary files. for more details.

Loïc Estève

FIX for raising non inheritable exceptions in a Parallel call. See for more details.

Alexandre Abadie

FIX joblib.hash error with mixed types sets and dicts containing mixed types keys when using Python 3. see

Loïc Estève

FIX joblib.dump/load for big numpy arrays with dtype=object. See for more details.

Loïc Estève

FIX joblib.Parallel hanging when used with an exhausted iterator. See for more details.

Release 0.9.3

Olivier Grisel

Revert back to the fork start method (instead of forkserver) as the latter was found to cause crashes in interactive Python sessions.

Release 0.9.2

Loïc Estève

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.

Loïc Estève

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 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.

Arthur Mensch

Fixed a bug with joblib.hash that used to return unstable values for strings and numpy.dtype instances depending on interning states.

Olivier Grisel

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 reinitialized when a fork system call happens.

Olivier Grisel

New context manager based API (with block) 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.

Olivier Grisel

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.

Release 0.8.4

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.

Release 0.8.3

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.

Release 0.8.2

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

Release 0.8.1

2014-05-29 Gael Varoquaux

BUG: fix crash with high verbosity

Release 0.8.0

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

New Memory.call_and_shelve API to handle memoized results by reference instead of by value.

Release 0.8.0a3

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.

Release 0.8.0a2

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

Release 0.8.0a

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 <> 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

Release 0.7.1

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

Release 0.7.0

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

Release 0.6.5

2012-09-15 Yannick Schwartz

BUG: make sure that sets and dictionaries give reproducible hashes

2012-07-18 Marek Rudnicki

BUG: make sure that object-dtype numpy array hash correctly

2012-07-12 GaelVaroquaux

BUG: Bad default n_jobs for Parallel

Release 0.6.4

2012-05-07 Vlad Niculae

ENH: controlled randomness in tests and doctest fix

2012-02-21 GaelVaroquaux

ENH: add verbosity in memory

2012-02-21 GaelVaroquaux

BUG: non-reproducible hashing: order of kwargs

The ordering of a dictionary is random. As a result the function hashing was not reproducible. Pretty hard to test

Release 0.6.3

2012-02-14 GaelVaroquaux

BUG: fix joblib Memory pickling

2012-02-11 GaelVaroquaux

BUG: fix hasher with Python 3

2012-02-09 GaelVaroquaux

API: filter_args: *args, **kwargs -> args, kwargs

Release 0.6.2

2012-02-06 Gael Varoquaux

BUG: make sure Memory pickles even if cachedir=None

Release 0.6.1

Bugfix release because of a merge error in release 0.6.0

Release 0.6.0

Beta 3

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

Beta 2

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

Release 0.5.7

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

Release 0.5.6

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

Release 0.5.5

2011-19-10 Fabian Pedregosa

ENH: Make joblib installable under Python 3.X

Release 0.5.4

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.

Release 0.5.3

2011-06-25 Gael varoquaux

API: All the useful symbols in the __init__

Release 0.5.2

2011-06-25 Gael varoquaux

ENH: Add cpu_count

2011-06-06 Gael varoquaux

ENH: Make sure memory hash in a reproducible way

Release 0.5.1

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

Release 0.5.0

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.

GaelVaroquaux 2011-02-24

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

Release 0.4.6

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)

Release 0.4.5

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.

Release 0.4.4

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 for extra setuptools args.

Gael varoquaux 2010-07-29

MISC: Silence tests (and hopefully Yaroslav :P)

Release 0.4.3

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.

Release 0.4.2

Gael Varoquaux 2010-07-16

BUG: Make sure that joblib still works with Python2.5. => release 0.4.2

Release 0.4.1