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"""
Backends for embarrassingly parallel code.
"""
import gc
import os
import sys
import warnings
import threading
import functools
import contextlib
from abc import ABCMeta, abstractmethod
from .format_stack import format_exc
from .my_exceptions import WorkerInterrupt, TransportableException
from ._multiprocessing_helpers import mp
from ._compat import with_metaclass, PY27
if mp is not None:
from .disk import delete_folder
from .pool import MemmappingPool
from multiprocessing.pool import ThreadPool
from .executor import get_memmapping_executor
# Compat between concurrent.futures and multiprocessing TimeoutError
from multiprocessing import TimeoutError
from .externals.loky._base import TimeoutError as LokyTimeoutError
from .externals.loky import process_executor, cpu_count
class ParallelBackendBase(with_metaclass(ABCMeta)):
"""Helper abc which defines all methods a ParallelBackend must implement"""
supports_timeout = False
supports_inner_max_num_threads = False
nesting_level = None
def __init__(self, nesting_level=None, inner_max_num_threads=None):
self.nesting_level = nesting_level
self.inner_max_num_threads = inner_max_num_threads
MAX_NUM_THREADS_VARS = [
'OMP_NUM_THREADS', 'OPENBLAS_NUM_THREADS', 'MKL_NUM_THREADS',
'BLIS_NUM_THREADS', 'VECLIB_MAXIMUM_THREADS', 'NUMBA_NUM_THREADS',
'NUMEXPR_NUM_THREADS',
]
TBB_ENABLE_IPC_VAR = "ENABLE_IPC"
@abstractmethod
def effective_n_jobs(self, n_jobs):
"""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. 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.
"""
@abstractmethod
def apply_async(self, func, callback=None):
"""Schedule a func to be run"""
def configure(self, n_jobs=1, parallel=None, prefer=None, require=None,
**backend_args):
"""Reconfigure the backend and return the number of workers.
This makes it possible to reuse an existing backend instance for
successive independent calls to Parallel with different parameters.
"""
self.parallel = parallel
return self.effective_n_jobs(n_jobs)
def start_call(self):
"""Call-back method called at the beginning of a Parallel call"""
def stop_call(self):
"""Call-back method called at the end of a Parallel call"""
def terminate(self):
"""Shutdown the workers and free the shared memory."""
def compute_batch_size(self):
"""Determine the optimal batch size"""
return 1
def batch_completed(self, batch_size, duration):
"""Callback indicate how long it took to run a batch"""
def get_exceptions(self):
"""List of exception types to be captured."""
return []
def abort_everything(self, ensure_ready=True):
"""Abort any running tasks
This is called when an exception has been raised when executing a tasks
and all the remaining tasks will be ignored and can therefore be
aborted to spare computation resources.
If ensure_ready is True, the backend should be left in an operating
state as future tasks might be re-submitted via that same backend
instance.
If ensure_ready is False, the implementer of this method can decide
to leave the backend in a closed / terminated state as no new task
are expected to be submitted to this backend.
Setting ensure_ready to False is an optimization that can be leveraged
when aborting tasks via killing processes from a local process pool
managed by the backend it-self: if we expect no new tasks, there is no
point in re-creating new workers.
"""
# Does nothing by default: to be overridden in subclasses when
# canceling tasks is possible.
pass
def get_nested_backend(self):
"""Backend instance to be used by nested Parallel calls.
By default a thread-based backend is used for the first level of
nesting. Beyond, switch to sequential backend to avoid spawning too
many threads on the host.
"""
nesting_level = getattr(self, 'nesting_level', 0) + 1
if nesting_level > 1:
return SequentialBackend(nesting_level=nesting_level), None
else:
return ThreadingBackend(nesting_level=nesting_level), None
@contextlib.contextmanager
def retrieval_context(self):
"""Context manager to manage an execution context.
Calls to Parallel.retrieve will be made inside this context.
By default, this does nothing. It may be useful for subclasses to
handle nested parallelism. In particular, it may be required to avoid
deadlocks if a backend manages a fixed number of workers, when those
workers may be asked to do nested Parallel calls. Without
'retrieval_context' this could lead to deadlock, as all the workers
managed by the backend may be "busy" waiting for the nested parallel
calls to finish, but the backend has no free workers to execute those
tasks.
"""
yield
def _prepare_worker_env(self, n_jobs):
"""Return environment variables limiting threadpools in external libs.
This function return a dict containing environment variables to pass
when creating a pool of process. These environment variables limit the
number of threads to `n_threads` for OpenMP, MKL, Accelerated and
OpenBLAS libraries in the child processes.
"""
explicit_n_threads = self.inner_max_num_threads
default_n_threads = str(max(cpu_count() // n_jobs, 1))
# Set the inner environment variables to self.inner_max_num_threads if
# it is given. Else, default to cpu_count // n_jobs unless the variable
# is already present in the parent process environment.
env = {}
for var in self.MAX_NUM_THREADS_VARS:
if explicit_n_threads is None:
var_value = os.environ.get(var, None)
if var_value is None:
var_value = default_n_threads
else:
var_value = str(explicit_n_threads)
env[var] = var_value
if self.TBB_ENABLE_IPC_VAR not in os.environ:
# To avoid over-subscription when using TBB, let the TBB schedulers
# use Inter Process Communication to coordinate:
env[self.TBB_ENABLE_IPC_VAR] = "1"
return env
@staticmethod
def in_main_thread():
return isinstance(threading.current_thread(), threading._MainThread)
class SequentialBackend(ParallelBackendBase):
"""A ParallelBackend which will execute all batches sequentially.
Does not use/create any threading objects, and hence has minimal
overhead. Used when n_jobs == 1.
"""
uses_threads = True
supports_sharedmem = True
def effective_n_jobs(self, n_jobs):
"""Determine the number of jobs which are going to run in parallel"""
if n_jobs == 0:
raise ValueError('n_jobs == 0 in Parallel has no meaning')
return 1
def apply_async(self, func, callback=None):
"""Schedule a func to be run"""
result = ImmediateResult(func)
if callback:
callback(result)
return result
def get_nested_backend(self):
# import is not top level to avoid cyclic import errors.
from .parallel import get_active_backend
# SequentialBackend should neither change the nesting level, the
# default backend or the number of jobs. Just return the current one.
return get_active_backend()
class PoolManagerMixin(object):
"""A helper class for managing pool of workers."""
_pool = None
def effective_n_jobs(self, n_jobs):
"""Determine the number of jobs which are going to run in parallel"""
if n_jobs == 0:
raise ValueError('n_jobs == 0 in Parallel has no meaning')
elif mp is None or n_jobs is None:
# multiprocessing is not available or disabled, fallback
# to sequential mode
return 1
elif n_jobs < 0:
n_jobs = max(cpu_count() + 1 + n_jobs, 1)
return n_jobs
def terminate(self):
"""Shutdown the process or thread pool"""
if self._pool is not None:
self._pool.close()
self._pool.terminate() # terminate does a join()
self._pool = None
def _get_pool(self):
"""Used by apply_async to make it possible to implement lazy init"""
return self._pool
def apply_async(self, func, callback=None):
"""Schedule a func to be run"""
return self._get_pool().apply_async(
SafeFunction(func), callback=callback)
def abort_everything(self, ensure_ready=True):
"""Shutdown the pool and restart a new one with the same parameters"""
self.terminate()
if ensure_ready:
self.configure(n_jobs=self.parallel.n_jobs, parallel=self.parallel,
**self.parallel._backend_args)
class AutoBatchingMixin(object):
"""A helper class for automagically batching jobs."""
# In seconds, should be big enough to hide multiprocessing dispatching
# overhead.
# This settings was found by running benchmarks/bench_auto_batching.py
# with various parameters on various platforms.
MIN_IDEAL_BATCH_DURATION = .2
# Should not be too high to avoid stragglers: long jobs running alone
# on a single worker while other workers have no work to process any more.
MAX_IDEAL_BATCH_DURATION = 2
# Batching counters default values
_DEFAULT_EFFECTIVE_BATCH_SIZE = 1
_DEFAULT_SMOOTHED_BATCH_DURATION = 0.0
def __init__(self, **kwargs):
self._effective_batch_size = self._DEFAULT_EFFECTIVE_BATCH_SIZE
self._smoothed_batch_duration = self._DEFAULT_SMOOTHED_BATCH_DURATION
super(AutoBatchingMixin, self).__init__(**kwargs)
def compute_batch_size(self):
"""Determine the optimal batch size"""
old_batch_size = self._effective_batch_size
batch_duration = self._smoothed_batch_duration
if (batch_duration > 0 and
batch_duration < self.MIN_IDEAL_BATCH_DURATION):
# The current batch size is too small: the duration of the
# processing of a batch of task is not large enough to hide
# the scheduling overhead.
ideal_batch_size = int(old_batch_size *
self.MIN_IDEAL_BATCH_DURATION /
batch_duration)
# Multiply by two to limit oscilations between min and max.
ideal_batch_size *= 2
# dont increase the batch size too fast to limit huge batch sizes
# potentially leading to starving worker
batch_size = min(2 * old_batch_size, ideal_batch_size)
batch_size = max(batch_size, 1)
self._effective_batch_size = batch_size
if self.parallel.verbose >= 10:
self.parallel._print(
"Batch computation too fast (%.4fs.) "
"Setting batch_size=%d.", (batch_duration, batch_size))
elif (batch_duration > self.MAX_IDEAL_BATCH_DURATION and
old_batch_size >= 2):
# The current batch size is too big. If we schedule overly long
# running batches some CPUs might wait with nothing left to do
# while a couple of CPUs a left processing a few long running
# batches. Better reduce the batch size a bit to limit the
# likelihood of scheduling such stragglers.
# decrease the batch size quickly to limit potential starving
ideal_batch_size = int(
old_batch_size * self.MIN_IDEAL_BATCH_DURATION / batch_duration
)
# Multiply by two to limit oscilations between min and max.
batch_size = max(2 * ideal_batch_size, 1)
self._effective_batch_size = batch_size
if self.parallel.verbose >= 10:
self.parallel._print(
"Batch computation too slow (%.4fs.) "
"Setting batch_size=%d.", (batch_duration, batch_size))
else:
# No batch size adjustment
batch_size = old_batch_size
if batch_size != old_batch_size:
# Reset estimation of the smoothed mean batch duration: this
# estimate is updated in the multiprocessing apply_async
# CallBack as long as the batch_size is constant. Therefore
# we need to reset the estimate whenever we re-tune the batch
# size.
self._smoothed_batch_duration = \
self._DEFAULT_SMOOTHED_BATCH_DURATION
return batch_size
def batch_completed(self, batch_size, duration):
"""Callback indicate how long it took to run a batch"""
if batch_size == self._effective_batch_size:
# Update the smoothed streaming estimate of the duration of a batch
# from dispatch to completion
old_duration = self._smoothed_batch_duration
if old_duration == self._DEFAULT_SMOOTHED_BATCH_DURATION:
# First record of duration for this batch size after the last
# reset.
new_duration = duration
else:
# Update the exponentially weighted average of the duration of
# batch for the current effective size.
new_duration = 0.8 * old_duration + 0.2 * duration
self._smoothed_batch_duration = new_duration
def reset_batch_stats(self):
"""Reset batch statistics to default values.
This avoids interferences with future jobs.
"""
self._effective_batch_size = self._DEFAULT_EFFECTIVE_BATCH_SIZE
self._smoothed_batch_duration = self._DEFAULT_SMOOTHED_BATCH_DURATION
class ThreadingBackend(PoolManagerMixin, ParallelBackendBase):
"""A ParallelBackend which will use a thread pool to execute batches in.
This is a low-overhead backend but it suffers from the Python Global
Interpreter Lock if the called function relies a lot on Python objects.
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).
The actual thread pool is lazily initialized: the actual thread pool
construction is delayed to the first call to apply_async.
ThreadingBackend is used as the default backend for nested calls.
"""
supports_timeout = True
uses_threads = True
supports_sharedmem = True
def configure(self, n_jobs=1, parallel=None, **backend_args):
"""Build a process or thread pool and return the number of workers"""
n_jobs = self.effective_n_jobs(n_jobs)
if n_jobs == 1:
# Avoid unnecessary overhead and use sequential backend instead.
raise FallbackToBackend(
SequentialBackend(nesting_level=self.nesting_level))
self.parallel = parallel
self._n_jobs = n_jobs
return n_jobs
def _get_pool(self):
"""Lazily initialize the thread pool
The actual pool of worker threads is only initialized at the first
call to apply_async.
"""
if self._pool is None:
self._pool = ThreadPool(self._n_jobs)
return self._pool
class MultiprocessingBackend(PoolManagerMixin, AutoBatchingMixin,
ParallelBackendBase):
"""A ParallelBackend which will use a multiprocessing.Pool.
Will introduce some communication and memory overhead when exchanging
input and output data with the with the worker Python processes.
However, does not suffer from the Python Global Interpreter Lock.
"""
# Environment variables to protect against bad situations when nesting
JOBLIB_SPAWNED_PROCESS = "__JOBLIB_SPAWNED_PARALLEL__"
supports_timeout = True
def effective_n_jobs(self, n_jobs):
"""Determine the number of jobs which are going to run in parallel.
This also checks if we are attempting to create a nested parallel
loop.
"""
if mp is None:
return 1
if mp.current_process().daemon:
# Daemonic processes cannot have children
if n_jobs != 1:
warnings.warn(
'Multiprocessing-backed parallel loops cannot be nested,'
' setting n_jobs=1',
stacklevel=3)
return 1
if process_executor._CURRENT_DEPTH > 0:
# Mixing loky and multiprocessing in nested loop is not supported
if n_jobs != 1:
warnings.warn(
'Multiprocessing-backed parallel loops cannot be nested,'
' below loky, setting n_jobs=1',
stacklevel=3)
return 1
elif not (self.in_main_thread() or self.nesting_level == 0):
# Prevent posix fork inside in non-main posix threads
if n_jobs != 1:
warnings.warn(
'Multiprocessing-backed parallel loops cannot be nested'
' below threads, setting n_jobs=1',
stacklevel=3)
return 1
return super(MultiprocessingBackend, self).effective_n_jobs(n_jobs)
def configure(self, n_jobs=1, parallel=None, prefer=None, require=None,
**memmappingpool_args):
"""Build a process or thread pool and return the number of workers"""
n_jobs = self.effective_n_jobs(n_jobs)
if n_jobs == 1:
raise FallbackToBackend(
SequentialBackend(nesting_level=self.nesting_level))
already_forked = int(os.environ.get(self.JOBLIB_SPAWNED_PROCESS, 0))
if already_forked:
raise ImportError(
'[joblib] Attempting to do parallel computing '
'without protecting your import on a system that does '
'not support forking. To use parallel-computing in a '
'script, you must protect your main loop using "if '
"__name__ == '__main__'"
'". Please see the joblib documentation on Parallel '
'for more information')
# Set an environment variable to avoid infinite loops
os.environ[self.JOBLIB_SPAWNED_PROCESS] = '1'
# Make sure to free as much memory as possible before forking
gc.collect()
self._pool = MemmappingPool(n_jobs, **memmappingpool_args)
self.parallel = parallel
return n_jobs
def terminate(self):
"""Shutdown the process or thread pool"""
super(MultiprocessingBackend, self).terminate()
if self.JOBLIB_SPAWNED_PROCESS in os.environ:
del os.environ[self.JOBLIB_SPAWNED_PROCESS]
self.reset_batch_stats()
class LokyBackend(AutoBatchingMixin, ParallelBackendBase):
"""Managing pool of workers with loky instead of multiprocessing."""
supports_timeout = True
supports_inner_max_num_threads = True
def configure(self, n_jobs=1, parallel=None, prefer=None, require=None,
idle_worker_timeout=300, **memmappingexecutor_args):
"""Build a process executor and return the number of workers"""
n_jobs = self.effective_n_jobs(n_jobs)
if n_jobs == 1:
raise FallbackToBackend(
SequentialBackend(nesting_level=self.nesting_level))
self._workers = get_memmapping_executor(
n_jobs, timeout=idle_worker_timeout,
env=self._prepare_worker_env(n_jobs=n_jobs),
**memmappingexecutor_args)
self.parallel = parallel
return n_jobs
def effective_n_jobs(self, n_jobs):
"""Determine the number of jobs which are going to run in parallel"""
if n_jobs == 0:
raise ValueError('n_jobs == 0 in Parallel has no meaning')
elif mp is None or n_jobs is None:
# multiprocessing is not available or disabled, fallback
# to sequential mode
return 1
elif mp.current_process().daemon:
# Daemonic processes cannot have children
if n_jobs != 1:
warnings.warn(
'Loky-backed parallel loops cannot be called in a'
' multiprocessing, setting n_jobs=1',
stacklevel=3)
return 1
elif not (self.in_main_thread() or self.nesting_level == 0):
# Prevent posix fork inside in non-main posix threads
if n_jobs != 1:
warnings.warn(
'Loky-backed parallel loops cannot be nested below '
'threads, setting n_jobs=1',
stacklevel=3)
return 1
elif n_jobs < 0:
n_jobs = max(cpu_count() + 1 + n_jobs, 1)
return n_jobs
def apply_async(self, func, callback=None):
"""Schedule a func to be run"""
future = self._workers.submit(SafeFunction(func))
future.get = functools.partial(self.wrap_future_result, future)
if callback is not None:
future.add_done_callback(callback)
return future
@staticmethod
def wrap_future_result(future, timeout=None):
"""Wrapper for Future.result to implement the same behaviour as
AsyncResults.get from multiprocessing."""
try:
return future.result(timeout=timeout)
except LokyTimeoutError:
raise TimeoutError()
def terminate(self):
if self._workers is not None:
# Terminate does not shutdown the workers as we want to reuse them
# in latter calls but we free as much memory as we can by deleting
# the shared memory
delete_folder(self._workers._temp_folder)
self._workers = None
self.reset_batch_stats()
def abort_everything(self, ensure_ready=True):
"""Shutdown the workers and restart a new one with the same parameters
"""
self._workers.shutdown(kill_workers=True)
delete_folder(self._workers._temp_folder)
self._workers = None
if ensure_ready:
self.configure(n_jobs=self.parallel.n_jobs, parallel=self.parallel)
class ImmediateResult(object):
def __init__(self, batch):
# Don't delay the application, to avoid keeping the input
# arguments in memory
self.results = batch()
def get(self):
return self.results
class SafeFunction(object):
"""Wrapper that handles the serialization of exception tracebacks.
If an exception is triggered when calling the inner function, a copy of
the full traceback is captured to make it possible to serialize
it so that it can be rendered in a different Python process.
"""
def __init__(self, func):
self.func = func
def __call__(self, *args, **kwargs):
try:
return self.func(*args, **kwargs)
except KeyboardInterrupt:
# We capture the KeyboardInterrupt and reraise it as
# something different, as multiprocessing does not
# interrupt processing for a KeyboardInterrupt
raise WorkerInterrupt()
except BaseException:
if PY27:
# Capture the traceback of the worker to make it part of
# the final exception message.
e_type, e_value, e_tb = sys.exc_info()
text = format_exc(e_type, e_value, e_tb, context=10,
tb_offset=1)
raise TransportableException(text, e_type)
else:
# Rely on Python 3 built-in Remote Traceback reporting
raise
class FallbackToBackend(Exception):
"""Raised when configuration should fallback to another backend"""
def __init__(self, backend):
self.backend = backend
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