o choose the default backend if no specific backend was selected with the :func:`~parallel_config` context manager. The default process-based backend is 'loky' and the default thread-based backend is 'threading'. Ignored if the ``backend`` parameter is specified. require: 'sharedmem' or None, default=None Hard constraint to select the backend. If set to 'sharedmem', the selected backend will be single-host and thread-based even if the user asked for a non-thread based backend with :func:`~joblib.parallel_config`. verbose: int, default=0 The verbosity level: if non zero, progress messages are printed. Above 50, the output is sent to stdout. The frequency of the messages increases with the verbosity level. If it more than 10, all iterations are reported. timeout: float or None, default=None Timeout limit for each task to complete. If any task takes longer a TimeOutError will be raised. Only applied when n_jobs != 1 pre_dispatch: {'all', integer, or expression, as in '3*n_jobs'}, default='2*n_jobs' The number of batches (of tasks) to be pre-dispatched. Default is '2*n_jobs'. When batch_size="auto" this is reasonable default and the workers should never starve. Note that only basic arithmetic are allowed here and no modules can be used in this expression. batch_size: int or 'auto', default='auto' The number of atomic tasks to dispatch at once to each worker. When individual evaluations are very fast, dispatching calls to workers can be slower than sequential computation because of the overhead. Batching fast computations together can mitigate this. The ``'auto'`` strategy keeps track of the time it takes for a batch to complete, and dynamically adjusts the batch size to keep the time on the order of half a second, using a heuristic. The initial batch size is 1. ``batch_size="auto"`` with ``backend="threading"`` will dispatch batches of a single task at a time as the threading backend has very little overhead and using larger batch size has not proved to bring any gain in that case. temp_folder: str or None, default=None Folder to be used by the pool for memmapping large arrays for sharing memory with worker processes. If None, this will try in order: - a folder pointed by the JOBLIB_TEMP_FOLDER environment variable, - /dev/shm if the folder exists and is writable: this is a RAM disk filesystem available by default on modern Linux distributions, - the default system temporary folder that can be overridden with TMP, TMPDIR or TEMP environment variables, typically /tmp under Unix operating systems. Only active when ``backend="loky"`` or ``"multiprocessing"``. max_nbytes int, str, or None, optional, default='1M' Threshold on the size of arrays passed to the workers that triggers automated memory mapping in temp_folder. Can be an int in Bytes, or a human-readable string, e.g., '1M' for 1 megabyte. Use None to disable memmapping of large arrays. Only active when ``backend="loky"`` or ``"multiprocessing"``. mmap_mode: {None, 'r+', 'r', 'w+', 'c'}, default='r' Memmapping mode for numpy arrays passed to workers. None will disable memmapping, other modes defined in the numpy.memmap doc: https://numpy.org/doc/stable/reference/generated/numpy.memmap.html Also, see 'max_nbytes' parameter documentation for more details. backend_kwargs: dict, optional Additional parameters to pass to the backend `configure` method. Notes ----- This object uses workers to compute in parallel the application of a function to many different arguments. The main functionality it brings in addition to using the raw multiprocessing or concurrent.futures API are (see examples for details): * More readable code, in particular since it avoids constructing list of arguments. * Easier debugging: - informative tracebacks even when the error happens on the client side - using 'n_jobs=1' enables to turn off parallel computing for debugging without changing the codepath - early capture of pickling errors * An optional progress meter. * Interruption of multiprocesses jobs with 'Ctrl-C' * Flexible pickling control for the communication to and from the worker processes. * Ability to use shared memory efficiently with worker processes for large numpy-based datastructures. Note that the intended usage is to run one call at a time. Multiple calls to the same Parallel object will result in a ``RuntimeError`` Examples -------- A simple example: >>> from math import sqrt >>> from joblib import Parallel, delayed >>> Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in range(10)) [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0] Reshaping the output when the function has several return values: >>> from math import modf >>> from joblib import Parallel, delayed >>> r = Parallel(n_jobs=1)(delayed(modf)(i/2.) for i in range(10)) >>> res, i = zip(*r) >>> res (0.0, 0.5, 0.0, 0.5, 0.0, 0.5, 0.0, 0.5, 0.0, 0.5) >>> i (0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0) The progress meter: the higher the value of `verbose`, the more messages: >>> from time import sleep >>> from joblib import Parallel, delayed >>> r = Parallel(n_jobs=2, verbose=10)( ... delayed(sleep)(.2) for _ in range(10)) #doctest: +SKIP [Parallel(n_jobs=2)]: Done 1 tasks | elapsed: 0.6s [Parallel(n_jobs=2)]: Done 4 tasks | elapsed: 0.8s [Parallel(n_jobs=2)]: Done 10 out of 10 | elapsed: 1.4s finished Traceback example, note how the line of the error is indicated as well as the values of the parameter passed to the function that triggered the exception, even though the traceback happens in the child process: >>> from heapq import nlargest >>> from joblib import Parallel, delayed >>> Parallel(n_jobs=2)( ... delayed(nlargest)(2, n) for n in (range(4), 'abcde', 3)) ... # doctest: +SKIP ----------------------------------------------------------------------- Sub-process traceback: ----------------------------------------------------------------------- TypeError Mon Nov 12 11:37:46 2012 PID: 12934 Python 2.7.3: /usr/bin/python ........................................................................ /usr/lib/python2.7/heapq.pyc in nlargest(n=2, iterable=3, key=None) 419 if n >= size: 420 return sorted(iterable, key=key, reverse=True)[:n] 421 422 # When key is none, use simpler decoration 423 if key is None: --> 424 it = izip(iterable, count(0,-1)) # decorate 425 result = _nlargest(n, it) 426 return map(itemgetter(0), result) # undecorate 427 428 # General case, slowest method TypeError: izip argument #1 must support iteration _______________________________________________________________________ Using pre_dispatch in a producer/consumer situation, where the data is generated on the fly. Note how the producer is first called 3 times before the parallel loop is initiated, and then called to generate new data on the fly: >>> from math import sqrt >>> from joblib import Parallel, delayed >>> def producer(): ... for i in range(6): ... print('Produced %s' % i) ... yield i >>> out = Parallel(n_jobs=2, verbose=100, pre_dispatch='1.5*n_jobs')( ... delayed(sqrt)(i) for i in producer()) #doctest: +SKIP Produced 0 Produced 1 Produced 2 [Parallel(n_jobs=2)]: Done 1 jobs | elapsed: 0.0s Produced 3 [Parallel(n_jobs=2)]: Done 2 jobs | elapsed: 0.0s Produced 4 [Parallel(n_jobs=2)]: Done 3 jobs | elapsed: 0.0s Produced 5 [Parallel(n_jobs=2)]: Done 4 jobs | elapsed: 0.0s [Parallel(n_jobs=2)]: Done 6 out of 6 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 6 out of 6 | elapsed: 0.0s finished r+