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Inzynierka/Lib/site-packages/joblib/parallel.py

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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 ._utils import eval_expr
# 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 = {
'threading': ThreadingBackend,
'sequential': SequentialBackend,
}
# name of the backend used by default by Parallel outside of any context
# managed by ``parallel_backend``.
# threading is the only backend that is always everywhere
DEFAULT_BACKEND = 'threading'
DEFAULT_N_JOBS = 1
MAYBE_AVAILABLE_BACKENDS = {'multiprocessing', 'loky'}
# if multiprocessing is available, so is loky, we set it as the default
# backend
if mp is not None:
BACKENDS['multiprocessing'] = MultiprocessingBackend
from .externals import loky
BACKENDS['loky'] = LokyBackend
DEFAULT_BACKEND = 'loky'
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 :func:`~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. Note that on some rare systems (such as pyiodine),
multiprocessing and loky may not be available, in which case joblib
defaults to threading.
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 # doctest: +SKIP
>>> register_ray() # doctest: +SKIP
>>> with parallel_backend("ray"): # doctest: +SKIP
... 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 :class:`~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:
if backend in EXTERNAL_BACKENDS:
register = EXTERNAL_BACKENDS[backend]
register()
elif backend in MAYBE_AVAILABLE_BACKENDS:
warnings.warn(
f"joblib backend '{backend}' is not available on "
f"your system, falling back to {DEFAULT_BACKEND}.",
UserWarning,
stacklevel=2)
BACKENDS[backend] = BACKENDS[DEFAULT_BACKEND]
else:
raise ValueError("Invalid backend: %s, expected one of %r"
% (backend, sorted(BACKENDS.keys())))
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):
"""Decorator used to capture the arguments of a 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 :class:`~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
:func:`~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. On some rare
systems (such as Pyiodide), the loky backend may not be
available.
- "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
:func:`~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
:class:`~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 :func:`~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 :func:`~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. Note that only basic
arithmetics 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, 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'}, 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.
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)
elif backend not in BACKENDS and backend in MAYBE_AVAILABLE_BACKENDS:
warnings.warn(
f"joblib backend '{backend}' is not available on "
f"your system, falling back to {DEFAULT_BACKEND}.",
UserWarning,
stacklevel=2)
BACKENDS[backend] = BACKENDS[DEFAULT_BACKEND]
backend = BACKENDS[DEFAULT_BACKEND](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_expr(
pre_dispatch.replace("n_jobs", str(n_jobs))
)
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)
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