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 git://github.com/joblib/joblib.git
If you don’t have git installed, you can download a zip or tarball of the latest code: http://github.com/joblib/joblib/archives/master
You can use pip to install joblib:
pip install joblib
from any directory or:
python setup.py install
from the source directory.
Joblib has no mandatory dependencies besides Python (supported versions are 3.7+).
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:
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:
The html docs are located in the
Making a source tarball¶
To create a source tarball, eg for packaging or distributing, run the following command:
python setup.py 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 setup.py 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
Release 1.3.2 – 2023/08/08¶
Fix a regression in
joblib.Parallelintroduced in 1.3.0 where explicitly setting
n_jobs=Nonewas not interpreted as “unset”. https://github.com/joblib/joblib/pull/1475
Fix a regression in
joblib.Parallelintroduced in 1.3.0 where
joblib.Parallellogging methods exposed from inheritance to
joblib.Loggerdidn’t work because of missing logger initialization. https://github.com/joblib/joblib/pull/1494
Various maintenance updates to the doc, the ci and the test. https://github.com/joblib/joblib/pull/1480, https://github.com/joblib/joblib/pull/1481, https://github.com/joblib/joblib/pull/1476, https://github.com/joblib/joblib/pull/1492
Release 1.3.1 – 2023/06/29¶
Fix compatibility with python 3.7 by vendor loky 3.4.1 which is compatible with this version. https://github.com/joblib/joblib/pull/1472
Release 1.3.0 – 2023/06/28¶
Ensure native byte order for memmap arrays in
Add ability to change default Parallel backend in tests by setting the
JOBLIB_TESTS_DEFAULT_PARALLEL_BACKENDenvironment variable. https://github.com/joblib/joblib/pull/1356
Fix temporary folder creation in joblib.Parallel on Linux subsystems on Windows which do have /dev/shm but don’t have the os.statvfs function https://github.com/joblib/joblib/issues/1353
Drop runtime dependency on
distutilsis going away in Python 3.12 and is deprecated from Python 3.10 onwards. This import was kept around to avoid breaking scikit-learn, however it’s now been long enough since scikit-learn deployed a fixed (verion 1.1 was released in May 2022) that it should be safe to remove this. https://github.com/joblib/joblib/pull/1361
A warning is raised when a pickling error occurs during caching operations. In version 1.5, this warning will be turned into an error. For all other errors, a new warning has been introduced:
Avoid (module, name) collisions when caching nested functions. This fix changes the module name of nested functions, invalidating caches from previous versions of Joblib. https://github.com/joblib/joblib/pull/1374
Improve the behavior of
n_jobs=1, with simplified tracebacks and more efficient running time. https://github.com/joblib/joblib/pull/1393
parallel_configcontext manager to allow for more fine-grained control over the backend configuration. It should be used in place of the
parallel_backendcontext manager. In particular, it has the advantage of not requiring to set a specific backend in the context manager. https://github.com/joblib/joblib/pull/1392, https://github.com/joblib/joblib/pull/1457
joblib.Memory.reduce_size()to make it easy to limit the number of items and remove items that have not been accessed for a long time in the cache. https://github.com/joblib/joblib/pull/1200
Memoryas this is not automatically enforced, the limit can be directly passed to
joblib.Memory.reduce_size()which needs to be called to actually enforce the limit. https://github.com/joblib/joblib/pull/1447
loky3.4.0 which includes various fixes. https://github.com/joblib/joblib/pull/1422
Various updates to the documentation and to benchmarking tools. https://github.com/joblib/joblib/pull/1343, https://github.com/joblib/joblib/pull/1348, https://github.com/joblib/joblib/pull/1411, https://github.com/joblib/joblib/pull/1451, https://github.com/joblib/joblib/pull/1427, https://github.com/joblib/joblib/pull/1400
Fix a security issue where
eval(pre_dispatch)could potentially run arbitrary code. Now only basic numerics are supported. https://github.com/joblib/joblib/pull/1327
Make sure that joblib works even when multiprocessing is not available, for instance with Pyodide https://github.com/joblib/joblib/pull/1256
Avoid unnecessary warnings when workers and main process delete the temporary memmap folder contents concurrently. https://github.com/joblib/joblib/pull/1263
Fix memory alignment bug for pickles containing numpy arrays. This is especially important when loading the pickle with
mmap_mode != Noneas the resulting
numpy.memmapobject 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. https://github.com/joblib/joblib/pull/1254
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 (https://github.com/joblib/joblib/pull/1269);
avoiding leaking worker processes in case of nested loky parallel calls;
reliability spawn the correct number of reusable workers.
Fix a security issue where
eval(pre_dispatch)could potentially run arbitrary code. Now only basic numerics are supported. https://github.com/joblib/joblib/pull/1327
Fix byte order inconsistency issue during deserialization using joblib.load in cross-endian environment: the numpy arrays are now always loaded to use the system byte order, independently of the byte order of the system that serialized the pickle. https://github.com/joblib/joblib/pull/1181
Fix joblib.Memory bug with the
ignoreparameter when the cached function is a decorated function. https://github.com/joblib/joblib/pull/1165
Fix joblib.Memory to properly handle caching for functions defined interactively in a IPython session or in Jupyter notebook cell. https://github.com/joblib/joblib/pull/1214
Update vendored loky (from version 2.9 to 3.0) and cloudpickle (from version 1.6 to 2.0) https://github.com/joblib/joblib/pull/1218
Add check_call_in_cache method to check cache without calling function. https://github.com/joblib/joblib/pull/820
dask: avoid redundant scattering of large arguments to make a more efficient use of the network resources and avoid crashing dask with “OSError: [Errno 55] No buffer space available” or “ConnectionResetError: [Errno 104] connection reset by peer”. https://github.com/joblib/joblib/pull/1133
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. https://github.com/joblib/joblib/pull/1136
Remove deprecated check_pickle argument in delayed. https://github.com/joblib/joblib/pull/903
Fix a spurious invalidation of Memory.cache’d functions called with Parallel under Jupyter or IPython. https://github.com/joblib/joblib/pull/1093
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.
Fix a problem in the constructors 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
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.format_stackare 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
n_jobs=Nonecalled 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.
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 (
enable Interprocess Communication for scheduler coordination when the nested code uses Threading Building Blocks (TBB) (
Fix a regression where the loky backend was not reusing previously spawned workers. https://github.com/joblib/joblib/pull/968
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.Memorywith 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
parallel_backend. It defaults to
cpu_count() // n_jobs. https://github.com/joblib/joblib/pull/940
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 memory 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 across the
Parallelcalls. This fixes the bad integration 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
dumpfunction. 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.Parallel. 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.
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 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 dictionaries 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 dictionary 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 useful 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 hopefully 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