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""" Helpers for embarrassingly parallel code. """ # Author: Gael Varoquaux < gael dot varoquaux at normalesup dot org > # Copyright: 2010, Gael Varoquaux # License: BSD 3 clause from __future__ import division import os import sys from math import sqrt import functools import time import threading import itertools from uuid import uuid4 from numbers import Integral import warnings import queue from ._multiprocessing_helpers import mp from .logger import Logger, short_format_time from .disk import memstr_to_bytes from ._parallel_backends import (FallbackToBackend, MultiprocessingBackend, ThreadingBackend, SequentialBackend, LokyBackend) from .externals.cloudpickle import dumps, loads from .externals import loky # Make sure that those two classes are part of the public joblib.parallel API # so that 3rd party backend implementers can import them from here. from ._parallel_backends import AutoBatchingMixin # noqa from ._parallel_backends import ParallelBackendBase # noqa BACKENDS = { 'multiprocessing': MultiprocessingBackend, 'threading': ThreadingBackend, 'sequential': SequentialBackend, 'loky': LokyBackend, } # name of the backend used by default by Parallel outside of any context # managed by ``parallel_backend``. DEFAULT_BACKEND = 'loky' DEFAULT_N_JOBS = 1 DEFAULT_THREAD_BACKEND = 'threading' # Thread local value that can be overridden by the ``parallel_backend`` context # manager _backend = threading.local() VALID_BACKEND_HINTS = ('processes', 'threads', None) VALID_BACKEND_CONSTRAINTS = ('sharedmem', None) def _register_dask(): """ Register Dask Backend if called with parallel_backend("dask") """ try: from ._dask import DaskDistributedBackend register_parallel_backend('dask', DaskDistributedBackend) except ImportError as e: msg = ("To use the dask.distributed backend you must install both " "the `dask` and distributed modules.\n\n" "See https://dask.pydata.org/en/latest/install.html for more " "information.") raise ImportError(msg) from e EXTERNAL_BACKENDS = { 'dask': _register_dask, } def get_active_backend(prefer=None, require=None, verbose=0): """Return the active default backend""" if prefer not in VALID_BACKEND_HINTS: raise ValueError("prefer=%r is not a valid backend hint, " "expected one of %r" % (prefer, VALID_BACKEND_HINTS)) if require not in VALID_BACKEND_CONSTRAINTS: raise ValueError("require=%r is not a valid backend constraint, " "expected one of %r" % (require, VALID_BACKEND_CONSTRAINTS)) if prefer == 'processes' and require == 'sharedmem': raise ValueError("prefer == 'processes' and require == 'sharedmem'" " are inconsistent settings") backend_and_jobs = getattr(_backend, 'backend_and_jobs', None) if backend_and_jobs is not None: # Try to use the backend set by the user with the context manager. backend, n_jobs = backend_and_jobs nesting_level = backend.nesting_level supports_sharedmem = getattr(backend, 'supports_sharedmem', False) if require == 'sharedmem' and not supports_sharedmem: # This backend does not match the shared memory constraint: # fallback to the default thead-based backend. sharedmem_backend = BACKENDS[DEFAULT_THREAD_BACKEND]( nesting_level=nesting_level) if verbose >= 10: print("Using %s as joblib.Parallel backend instead of %s " "as the latter does not provide shared memory semantics." % (sharedmem_backend.__class__.__name__, backend.__class__.__name__)) return sharedmem_backend, DEFAULT_N_JOBS else: return backend_and_jobs # We are outside of the scope of any parallel_backend context manager, # create the default backend instance now. backend = BACKENDS[DEFAULT_BACKEND](nesting_level=0) supports_sharedmem = getattr(backend, 'supports_sharedmem', False) uses_threads = getattr(backend, 'uses_threads', False) if ((require == 'sharedmem' and not supports_sharedmem) or (prefer == 'threads' and not uses_threads)): # Make sure the selected default backend match the soft hints and # hard constraints: backend = BACKENDS[DEFAULT_THREAD_BACKEND](nesting_level=0) return backend, DEFAULT_N_JOBS class parallel_backend(object): """Change the default backend used by Parallel inside a with block. If ``backend`` is a string it must match a previously registered implementation using the ``register_parallel_backend`` function. By default the following backends are available: - 'loky': single-host, process-based parallelism (used by default), - 'threading': single-host, thread-based parallelism, - 'multiprocessing': legacy single-host, process-based parallelism. 'loky' is recommended to run functions that manipulate Python objects. 'threading' is a low-overhead alternative that is most efficient for functions that release the Global Interpreter Lock: e.g. I/O-bound code or CPU-bound code in a few calls to native code that explicitly releases the GIL. In addition, if the `dask` and `distributed` Python packages are installed, it is possible to use the 'dask' backend for better scheduling of nested parallel calls without over-subscription and potentially distribute parallel calls over a networked cluster of several hosts. It is also possible to use the distributed 'ray' backend for distributing the workload to a cluster of nodes. To use the 'ray' joblib backend add the following lines: >> from ray.util.joblib import register_ray >> register_ray() >> with parallel_backend("ray"): .. print(Parallel()(delayed(neg)(i + 1) for i in range(5))) [-1, -2, -3, -4, -5] Alternatively the backend can be passed directly as an instance. By default all available workers will be used (``n_jobs=-1``) unless the caller passes an explicit value for the ``n_jobs`` parameter. This is an alternative to passing a ``backend='backend_name'`` argument to the ``Parallel`` class constructor. It is particularly useful when calling into library code that uses joblib internally but does not expose the backend argument in its own API. >>> from operator import neg >>> with parallel_backend('threading'): ... print(Parallel()(delayed(neg)(i + 1) for i in range(5))) ... [-1, -2, -3, -4, -5] Warning: this function is experimental and subject to change in a future version of joblib. Joblib also tries to limit the oversubscription by limiting the number of threads usable in some third-party library threadpools like OpenBLAS, MKL or OpenMP. The default limit in each worker is set to ``max(cpu_count() // effective_n_jobs, 1)`` but this limit can be overwritten with the ``inner_max_num_threads`` argument which will be used to set this limit in the child processes. .. versionadded:: 0.10 """ def __init__(self, backend, n_jobs=-1, inner_max_num_threads=None, **backend_params): if isinstance(backend, str): if backend not in BACKENDS and backend in EXTERNAL_BACKENDS: register = EXTERNAL_BACKENDS[backend] register() backend = BACKENDS[backend](**backend_params) if inner_max_num_threads is not None: msg = ("{} does not accept setting the inner_max_num_threads " "argument.".format(backend.__class__.__name__)) assert backend.supports_inner_max_num_threads, msg backend.inner_max_num_threads = inner_max_num_threads # If the nesting_level of the backend is not set previously, use the # nesting level from the previous active_backend to set it current_backend_and_jobs = getattr(_backend, 'backend_and_jobs', None) if backend.nesting_level is None: if current_backend_and_jobs is None: nesting_level = 0 else: nesting_level = current_backend_and_jobs[0].nesting_level backend.nesting_level = nesting_level # Save the backends info and set the active backend self.old_backend_and_jobs = current_backend_and_jobs self.new_backend_and_jobs = (backend, n_jobs) _backend.backend_and_jobs = (backend, n_jobs) def __enter__(self): return self.new_backend_and_jobs def __exit__(self, type, value, traceback): self.unregister() def unregister(self): if self.old_backend_and_jobs is None: if getattr(_backend, 'backend_and_jobs', None) is not None: del _backend.backend_and_jobs else: _backend.backend_and_jobs = self.old_backend_and_jobs # Under Linux or OS X the default start method of multiprocessing # can cause third party libraries to crash. Under Python 3.4+ it is possible # to set an environment variable to switch the default start method from # 'fork' to 'forkserver' or 'spawn' to avoid this issue albeit at the cost # of causing semantic changes and some additional pool instantiation overhead. DEFAULT_MP_CONTEXT = None if hasattr(mp, 'get_context'): method = os.environ.get('JOBLIB_START_METHOD', '').strip() or None if method is not None: DEFAULT_MP_CONTEXT = mp.get_context(method=method) class BatchedCalls(object): """Wrap a sequence of (func, args, kwargs) tuples as a single callable""" def __init__(self, iterator_slice, backend_and_jobs, reducer_callback=None, pickle_cache=None): self.items = list(iterator_slice) self._size = len(self.items) self._reducer_callback = reducer_callback if isinstance(backend_and_jobs, tuple): self._backend, self._n_jobs = backend_and_jobs else: # this is for backward compatibility purposes. Before 0.12.6, # nested backends were returned without n_jobs indications. self._backend, self._n_jobs = backend_and_jobs, None self._pickle_cache = pickle_cache if pickle_cache is not None else {} def __call__(self): # Set the default nested backend to self._backend but do not set the # change the default number of processes to -1 with parallel_backend(self._backend, n_jobs=self._n_jobs): return [func(*args, **kwargs) for func, args, kwargs in self.items] def __reduce__(self): if self._reducer_callback is not None: self._reducer_callback() # no need pickle the callback. return ( BatchedCalls, (self.items, (self._backend, self._n_jobs), None, self._pickle_cache) ) def __len__(self): return self._size ############################################################################### # CPU count that works also when multiprocessing has been disabled via # the JOBLIB_MULTIPROCESSING environment variable def cpu_count(only_physical_cores=False): """Return the number of CPUs. This delegates to loky.cpu_count that takes into account additional constraints such as Linux CFS scheduler quotas (typically set by container runtimes such as docker) and CPU affinity (for instance using the taskset command on Linux). If only_physical_cores is True, do not take hyperthreading / SMT logical cores into account. """ if mp is None: return 1 return loky.cpu_count(only_physical_cores=only_physical_cores) ############################################################################### # For verbosity def _verbosity_filter(index, verbose): """ Returns False for indices increasingly apart, the distance depending on the value of verbose. We use a lag increasing as the square of index """ if not verbose: return True elif verbose > 10: return False if index == 0: return False verbose = .5 * (11 - verbose) ** 2 scale = sqrt(index / verbose) next_scale = sqrt((index + 1) / verbose) return (int(next_scale) == int(scale)) ############################################################################### def delayed(function, check_pickle=None): """Decorator used to capture the arguments of a function.""" if check_pickle is not None: warnings.warn('check_pickle is deprecated in joblib 0.12 and will be' ' removed in 0.13', DeprecationWarning) # Try to pickle the input function, to catch the problems early when # using with multiprocessing: if check_pickle: dumps(function) def delayed_function(*args, **kwargs): return function, args, kwargs try: delayed_function = functools.wraps(function)(delayed_function) except AttributeError: " functools.wraps fails on some callable objects " return delayed_function ############################################################################### class BatchCompletionCallBack(object): """Callback used by joblib.Parallel's multiprocessing backend. This callable is executed by the parent process whenever a worker process has returned the results of a batch of tasks. It is used for progress reporting, to update estimate of the batch processing duration and to schedule the next batch of tasks to be processed. """ def __init__(self, dispatch_timestamp, batch_size, parallel): self.dispatch_timestamp = dispatch_timestamp self.batch_size = batch_size self.parallel = parallel def __call__(self, out): self.parallel.n_completed_tasks += self.batch_size this_batch_duration = time.time() - self.dispatch_timestamp self.parallel._backend.batch_completed(self.batch_size, this_batch_duration) self.parallel.print_progress() with self.parallel._lock: if self.parallel._original_iterator is not None: self.parallel.dispatch_next() ############################################################################### def register_parallel_backend(name, factory, make_default=False): """Register a new Parallel backend factory. The new backend can then be selected by passing its name as the backend argument to the Parallel class. Moreover, the default backend can be overwritten globally by setting make_default=True. The factory can be any callable that takes no argument and return an instance of ``ParallelBackendBase``. Warning: this function is experimental and subject to change in a future version of joblib. .. versionadded:: 0.10 """ BACKENDS[name] = factory if make_default: global DEFAULT_BACKEND DEFAULT_BACKEND = name def effective_n_jobs(n_jobs=-1): """Determine the number of jobs that can actually run in parallel n_jobs is the number of workers requested by the callers. Passing n_jobs=-1 means requesting all available workers for instance matching the number of CPU cores on the worker host(s). This method should return a guesstimate of the number of workers that can actually perform work concurrently with the currently enabled default backend. The primary use case is to make it possible for the caller to know in how many chunks to slice the work. In general working on larger data chunks is more efficient (less scheduling overhead and better use of CPU cache prefetching heuristics) as long as all the workers have enough work to do. Warning: this function is experimental and subject to change in a future version of joblib. .. versionadded:: 0.10 """ backend, backend_n_jobs = get_active_backend() if n_jobs is None: n_jobs = backend_n_jobs return backend.effective_n_jobs(n_jobs=n_jobs) ############################################################################### class Parallel(Logger): ''' Helper class for readable parallel mapping. Read more in the :ref:`User Guide <parallel>`. Parameters ----------- n_jobs: int, default: None The maximum number of concurrently running jobs, such as the number of Python worker processes when backend="multiprocessing" or the size of the thread-pool when backend="threading". If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. None is a marker for 'unset' that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a parallel_backend context manager that sets another value for n_jobs. backend: str, ParallelBackendBase instance or None, default: 'loky' Specify the parallelization backend implementation. Supported backends are: - "loky" used by default, can induce some communication and memory overhead when exchanging input and output data with the worker Python processes. - "multiprocessing" previous process-based backend based on `multiprocessing.Pool`. Less robust than `loky`. - "threading" is a very low-overhead backend but it suffers from the Python Global Interpreter Lock if the called function relies a lot on Python objects. "threading" is mostly useful when the execution bottleneck is a compiled extension that explicitly releases the GIL (for instance a Cython loop wrapped in a "with nogil" block or an expensive call to a library such as NumPy). - finally, you can register backends by calling register_parallel_backend. This will allow you to implement a backend of your liking. It is not recommended to hard-code the backend name in a call to Parallel in a library. Instead it is recommended to set soft hints (prefer) or hard constraints (require) so as to make it possible for library users to change the backend from the outside using the parallel_backend context manager. prefer: str in {'processes', 'threads'} or None, default: None Soft hint to choose the default backend if no specific backend was selected with the parallel_backend 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 parallel_backend. verbose: int, optional 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, optional 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'} 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. 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, optional 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, 1M by default 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'} Memmapping mode for numpy arrays passed to workers. See 'max_nbytes' parameter documentation for more details. 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. 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 ''' def __init__(self, n_jobs=None, backend=None, verbose=0, timeout=None, pre_dispatch='2 * n_jobs', batch_size='auto', temp_folder=None, max_nbytes='1M', mmap_mode='r', prefer=None, require=None): active_backend, context_n_jobs = get_active_backend( prefer=prefer, require=require, verbose=verbose) nesting_level = active_backend.nesting_level if backend is None and n_jobs is None: # If we are under a parallel_backend context manager, look up # the default number of jobs and use that instead: n_jobs = context_n_jobs if n_jobs is None: # No specific context override and no specific value request: # default to 1. n_jobs = 1 self.n_jobs = n_jobs self.verbose = verbose self.timeout = timeout self.pre_dispatch = pre_dispatch self._ready_batches = queue.Queue() self._id = uuid4().hex self._reducer_callback = None if isinstance(max_nbytes, str): max_nbytes = memstr_to_bytes(max_nbytes) self._backend_args = dict( max_nbytes=max_nbytes, mmap_mode=mmap_mode, temp_folder=temp_folder, prefer=prefer, require=require, verbose=max(0, self.verbose - 50), ) if DEFAULT_MP_CONTEXT is not None: self._backend_args['context'] = DEFAULT_MP_CONTEXT elif hasattr(mp, "get_context"): self._backend_args['context'] = mp.get_context() if backend is None: backend = active_backend elif isinstance(backend, ParallelBackendBase): # Use provided backend as is, with the current nesting_level if it # is not set yet. if backend.nesting_level is None: backend.nesting_level = nesting_level elif hasattr(backend, 'Pool') and hasattr(backend, 'Lock'): # Make it possible to pass a custom multiprocessing context as # backend to change the start method to forkserver or spawn or # preload modules on the forkserver helper process. self._backend_args['context'] = backend backend = MultiprocessingBackend(nesting_level=nesting_level) else: try: backend_factory = BACKENDS[backend] except KeyError as e: raise ValueError("Invalid backend: %s, expected one of %r" % (backend, sorted(BACKENDS.keys()))) from e backend = backend_factory(nesting_level=nesting_level) if (require == 'sharedmem' and not getattr(backend, 'supports_sharedmem', False)): raise ValueError("Backend %s does not support shared memory" % backend) if (batch_size == 'auto' or isinstance(batch_size, Integral) and batch_size > 0): self.batch_size = batch_size else: raise ValueError( "batch_size must be 'auto' or a positive integer, got: %r" % batch_size) self._backend = backend self._output = None self._jobs = list() self._managed_backend = False # This lock is used coordinate the main thread of this process with # the async callback thread of our the pool. self._lock = threading.RLock() def __enter__(self): self._managed_backend = True self._initialize_backend() return self def __exit__(self, exc_type, exc_value, traceback): self._terminate_backend() self._managed_backend = False def _initialize_backend(self): """Build a process or thread pool and return the number of workers""" try: n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self, **self._backend_args) if self.timeout is not None and not self._backend.supports_timeout: warnings.warn( 'The backend class {!r} does not support timeout. ' "You have set 'timeout={}' in Parallel but " "the 'timeout' parameter will not be used.".format( self._backend.__class__.__name__, self.timeout)) except FallbackToBackend as e: # Recursively initialize the backend in case of requested fallback. self._backend = e.backend n_jobs = self._initialize_backend() return n_jobs def _effective_n_jobs(self): if self._backend: return self._backend.effective_n_jobs(self.n_jobs) return 1 def _terminate_backend(self): if self._backend is not None: self._backend.terminate() def _dispatch(self, batch): """Queue the batch for computing, with or without multiprocessing WARNING: this method is not thread-safe: it should be only called indirectly via dispatch_one_batch. """ # If job.get() catches an exception, it closes the queue: if self._aborting: return self.n_dispatched_tasks += len(batch) self.n_dispatched_batches += 1 dispatch_timestamp = time.time() cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self) with self._lock: job_idx = len(self._jobs) job = self._backend.apply_async(batch, callback=cb) # A job can complete so quickly than its callback is # called before we get here, causing self._jobs to # grow. To ensure correct results ordering, .insert is # used (rather than .append) in the following line self._jobs.insert(job_idx, job) def dispatch_next(self): """Dispatch more data for parallel processing This method is meant to be called concurrently by the multiprocessing callback. We rely on the thread-safety of dispatch_one_batch to protect against concurrent consumption of the unprotected iterator. """ if not self.dispatch_one_batch(self._original_iterator): self._iterating = False self._original_iterator = None def dispatch_one_batch(self, iterator): """Prefetch the tasks for the next batch and dispatch them. The effective size of the batch is computed here. If there are no more jobs to dispatch, return False, else return True. The iterator consumption and dispatching is protected by the same lock so calling this function should be thread safe. """ if self.batch_size == 'auto': batch_size = self._backend.compute_batch_size() else: # Fixed batch size strategy batch_size = self.batch_size with self._lock: # to ensure an even distribution of the workolad between workers, # we look ahead in the original iterators more than batch_size # tasks - However, we keep consuming only one batch at each # dispatch_one_batch call. The extra tasks are stored in a local # queue, _ready_batches, that is looked-up prior to re-consuming # tasks from the origal iterator. try: tasks = self._ready_batches.get(block=False) except queue.Empty: # slice the iterator n_jobs * batchsize items at a time. If the # slice returns less than that, then the current batchsize puts # too much weight on a subset of workers, while other may end # up starving. So in this case, re-scale the batch size # accordingly to distribute evenly the last items between all # workers. n_jobs = self._cached_effective_n_jobs big_batch_size = batch_size * n_jobs islice = list(itertools.islice(iterator, big_batch_size)) if len(islice) == 0: return False elif (iterator is self._original_iterator and len(islice) < big_batch_size): # We reached the end of the original iterator (unless # iterator is the ``pre_dispatch``-long initial slice of # the original iterator) -- decrease the batch size to # account for potential variance in the batches running # time. final_batch_size = max(1, len(islice) // (10 * n_jobs)) else: final_batch_size = max(1, len(islice) // n_jobs) # enqueue n_jobs batches in a local queue for i in range(0, len(islice), final_batch_size): tasks = BatchedCalls(islice[i:i + final_batch_size], self._backend.get_nested_backend(), self._reducer_callback, self._pickle_cache) self._ready_batches.put(tasks) # finally, get one task. tasks = self._ready_batches.get(block=False) if len(tasks) == 0: # No more tasks available in the iterator: tell caller to stop. return False else: self._dispatch(tasks) return True def _print(self, msg, msg_args): """Display the message on stout or stderr depending on verbosity""" # XXX: Not using the logger framework: need to # learn to use logger better. if not self.verbose: return if self.verbose < 50: writer = sys.stderr.write else: writer = sys.stdout.write msg = msg % msg_args writer('[%s]: %s\n' % (self, msg)) def print_progress(self): """Display the process of the parallel execution only a fraction of time, controlled by self.verbose. """ if not self.verbose: return elapsed_time = time.time() - self._start_time # Original job iterator becomes None once it has been fully # consumed : at this point we know the total number of jobs and we are # able to display an estimation of the remaining time based on already # completed jobs. Otherwise, we simply display the number of completed # tasks. if self._original_iterator is not None: if _verbosity_filter(self.n_dispatched_batches, self.verbose): return self._print('Done %3i tasks | elapsed: %s', (self.n_completed_tasks, short_format_time(elapsed_time), )) else: index = self.n_completed_tasks # We are finished dispatching total_tasks = self.n_dispatched_tasks # We always display the first loop if not index == 0: # Display depending on the number of remaining items # A message as soon as we finish dispatching, cursor is 0 cursor = (total_tasks - index + 1 - self._pre_dispatch_amount) frequency = (total_tasks // self.verbose) + 1 is_last_item = (index + 1 == total_tasks) if (is_last_item or cursor % frequency): return remaining_time = (elapsed_time / index) * \ (self.n_dispatched_tasks - index * 1.0) # only display status if remaining time is greater or equal to 0 self._print('Done %3i out of %3i | elapsed: %s remaining: %s', (index, total_tasks, short_format_time(elapsed_time), short_format_time(remaining_time), )) def retrieve(self): self._output = list() while self._iterating or len(self._jobs) > 0: if len(self._jobs) == 0: # Wait for an async callback to dispatch new jobs time.sleep(0.01) continue # We need to be careful: the job list can be filling up as # we empty it and Python list are not thread-safe by default hence # the use of the lock with self._lock: job = self._jobs.pop(0) try: if getattr(self._backend, 'supports_timeout', False): self._output.extend(job.get(timeout=self.timeout)) else: self._output.extend(job.get()) except BaseException as exception: # Note: we catch any BaseException instead of just Exception # instances to also include KeyboardInterrupt. # Stop dispatching any new job in the async callback thread self._aborting = True # If the backend allows it, cancel or kill remaining running # tasks without waiting for the results as we will raise # the exception we got back to the caller instead of returning # any result. backend = self._backend if (backend is not None and hasattr(backend, 'abort_everything')): # If the backend is managed externally we need to make sure # to leave it in a working state to allow for future jobs # scheduling. ensure_ready = self._managed_backend backend.abort_everything(ensure_ready=ensure_ready) raise def __call__(self, iterable): if self._jobs: raise ValueError('This Parallel instance is already running') # A flag used to abort the dispatching of jobs in case an # exception is found self._aborting = False if not self._managed_backend: n_jobs = self._initialize_backend() else: n_jobs = self._effective_n_jobs() if isinstance(self._backend, LokyBackend): # For the loky backend, we add a callback executed when reducing # BatchCalls, that makes the loky executor use a temporary folder # specific to this Parallel object when pickling temporary memmaps. # This callback is necessary to ensure that several Parallel # objects using the same resuable executor don't use the same # temporary resources. def _batched_calls_reducer_callback(): # Relevant implementation detail: the following lines, called # when reducing BatchedCalls, are called in a thread-safe # situation, meaning that the context of the temporary folder # manager will not be changed in between the callback execution # and the end of the BatchedCalls pickling. The reason is that # pickling (the only place where set_current_context is used) # is done from a single thread (the queue_feeder_thread). self._backend._workers._temp_folder_manager.set_current_context( # noqa self._id ) self._reducer_callback = _batched_calls_reducer_callback # self._effective_n_jobs should be called in the Parallel.__call__ # thread only -- store its value in an attribute for further queries. self._cached_effective_n_jobs = n_jobs backend_name = self._backend.__class__.__name__ if n_jobs == 0: raise RuntimeError("%s has no active worker." % backend_name) self._print("Using backend %s with %d concurrent workers.", (backend_name, n_jobs)) if hasattr(self._backend, 'start_call'): self._backend.start_call() iterator = iter(iterable) pre_dispatch = self.pre_dispatch if pre_dispatch == 'all' or n_jobs == 1: # prevent further dispatch via multiprocessing callback thread self._original_iterator = None self._pre_dispatch_amount = 0 else: self._original_iterator = iterator if hasattr(pre_dispatch, 'endswith'): pre_dispatch = eval(pre_dispatch) self._pre_dispatch_amount = pre_dispatch = int(pre_dispatch) # The main thread will consume the first pre_dispatch items and # the remaining items will later be lazily dispatched by async # callbacks upon task completions. # TODO: this iterator should be batch_size * n_jobs iterator = itertools.islice(iterator, self._pre_dispatch_amount) self._start_time = time.time() self.n_dispatched_batches = 0 self.n_dispatched_tasks = 0 self.n_completed_tasks = 0 # Use a caching dict for callables that are pickled with cloudpickle to # improve performances. This cache is used only in the case of # functions that are defined in the __main__ module, functions that are # defined locally (inside another function) and lambda expressions. self._pickle_cache = dict() try: # Only set self._iterating to True if at least a batch # was dispatched. In particular this covers the edge # case of Parallel used with an exhausted iterator. If # self._original_iterator is None, then this means either # that pre_dispatch == "all", n_jobs == 1 or that the first batch # was very quick and its callback already dispatched all the # remaining jobs. self._iterating = False if self.dispatch_one_batch(iterator): self._iterating = self._original_iterator is not None while self.dispatch_one_batch(iterator): pass if pre_dispatch == "all" or n_jobs == 1: # The iterable was consumed all at once by the above for loop. # No need to wait for async callbacks to trigger to # consumption. self._iterating = False with self._backend.retrieval_context(): self.retrieve() # Make sure that we get a last message telling us we are done elapsed_time = time.time() - self._start_time self._print('Done %3i out of %3i | elapsed: %s finished', (len(self._output), len(self._output), short_format_time(elapsed_time))) finally: if hasattr(self._backend, 'stop_call'): self._backend.stop_call() if not self._managed_backend: self._terminate_backend() self._jobs = list() self._pickle_cache = None output = self._output self._output = None return output def __repr__(self): return '%s(n_jobs=%s)' % (self.__class__.__name__, self.n_jobs)