Embarrassingly parallel for loops

Common usage

Joblib provides a simple helper class to write parallel for loops using multiprocessing. The core idea is to write the code to be executed as a generator expression, and convert it to parallel computing:

>>> from math import sqrt
>>> [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]

can be spread over 2 CPUs using the following:

>>> from math import sqrt
>>> from joblib import Parallel, delayed
>>> Parallel(n_jobs=2)(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]

Thread-based parallelism vs process-based parallelism

By default joblib.Parallel uses the 'loky' backend module to start separate Python worker processes to execute tasks concurrently on separate CPUs. This is a reasonable default for generic Python programs but can induce a significant overhead as the input and output data need to be serialized in a queue for communication with the worker processes.

When you know that the function you are calling is based on a compiled extension that releases the Python Global Interpreter Lock (GIL) during most of its computation then it is more efficient to use threads instead of Python processes as concurrent workers. For instance this is the case if you write the CPU intensive part of your code inside a with nogil block of a Cython function.

To hint that your code can efficiently use threads, just pass prefer="threads" as parameter of the joblib.Parallel constructor. In this case joblib will automatically use the "threading" backend instead of the default "loky" backend:

>>> Parallel(n_jobs=2, prefer="threads")(
...     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]

It is also possible to manually select a specific backend implementation with the help of a context manager:

>>> from joblib import parallel_backend
>>> with parallel_backend('threading', n_jobs=2):
...    Parallel()(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]

The latter is especially useful when calling a library that uses joblib.Parallel internally without exposing backend selection as part of its public API.

Note that the prefer="threads" option was introduced in joblib 0.12. In prior versions, the same effect could be achieved by hardcoding a specific backend implementation such as backend="threading" in the call to joblib.Parallel but this is now considered a bad pattern (when done in a library) as it does not make it possible to override that choice with the parallel_backend context manager.

Shared-memory semantics

The default backend of joblib will run each function call in isolated Python processes, therefore they cannot mutate a common Python object defined in the main program.

However if the parallel function really needs to rely on the shared memory semantics of threads, it should be made explicit with require='sharedmem', for instance:

>>> shared_set = set()
>>> def collect(x):
...    shared_set.add(x)
...
>>> Parallel(n_jobs=2, require='sharedmem')(
...     delayed(collect)(i) for i in range(5))
[None, None, None, None, None]
>>> sorted(shared_set)
[0, 1, 2, 3, 4]

Keep in mind that relying a on the shared-memory semantics is probably suboptimal from a performance point of view as concurrent access to a shared Python object will suffer from lock contention.

Reusing a pool of workers

Some algorithms require to make several consecutive calls to a parallel function interleaved with processing of the intermediate results. Calling joblib.Parallel several times in a loop is sub-optimal because it will create and destroy a pool of workers (threads or processes) several times which can cause a significant overhead.

For this case it is more efficient to use the context manager API of the joblib.Parallel class to re-use the same pool of workers for several calls to the joblib.Parallel object:

>>> with Parallel(n_jobs=2) as parallel:
...    accumulator = 0.
...    n_iter = 0
...    while accumulator < 1000:
...        results = parallel(delayed(sqrt)(accumulator + i ** 2)
...                           for i in range(5))
...        accumulator += sum(results)  # synchronization barrier
...        n_iter += 1
...
>>> (accumulator, n_iter)                            
(1136.596..., 14)

Working with numerical data in shared memory (memmapping)

By default the workers of the pool are real Python processes forked using the multiprocessing module of the Python standard library when n_jobs != 1. The arguments passed as input to the Parallel call are serialized and reallocated in the memory of each worker process.

This can be problematic for large arguments as they will be reallocated n_jobs times by the workers.

As this problem can often occur in scientific computing with numpy based datastructures, joblib.Parallel provides a special handling for large arrays to automatically dump them on the filesystem and pass a reference to the worker to open them as memory map on that file using the numpy.memmap subclass of numpy.ndarray. This makes it possible to share a segment of data between all the worker processes.

Note

The following only applies with the "loky"` and ``'multiprocessing' process-backends. If your code can release the GIL, then using a thread-based backend backend by passing prefer='threads' is even more efficient because it makes it possible to avoid the communication overhead of process-based parallelism.

Scientific Python libraries such as numpy, scipy, pandas and scikit-learn often release the GIL in performance critical code paths. It is therefore advised to always measure the speed of thread-based parallelism and use it when the scalability is not limited by the GIL.

Automated array to memmap conversion

The automated array to memmap conversion is triggered by a configurable threshold on the size of the array:

>>> import numpy as np
>>> from joblib import Parallel, delayed
>>> def is_memmap(obj):
...     return isinstance(obj, np.memmap)

>>> Parallel(n_jobs=2, max_nbytes=1e6)(
...     delayed(is_memmap)(np.ones(int(i)))
...     for i in [1e2, 1e4, 1e6])
[False, False, True]

By default the data is dumped to the /dev/shm shared-memory partition if it exists and is writable (typically the case under Linux). Otherwise the operating system’s temporary folder is used. The location of the temporary data files can be customized by passing a temp_folder argument to the Parallel constructor.

Passing max_nbytes=None makes it possible to disable the automated array to memmap conversion.

Manual management of memmaped input data

For even finer tuning of the memory usage it is also possible to dump the array as a memmap directly from the parent process to free the memory before forking the worker processes. For instance let’s allocate a large array in the memory of the parent process:

>>> large_array = np.ones(int(1e6))

Dump it to a local file for memmapping:

>>> import tempfile
>>> import os
>>> from joblib import load, dump

>>> temp_folder = tempfile.mkdtemp()
>>> filename = os.path.join(temp_folder, 'joblib_test.mmap')
>>> if os.path.exists(filename): os.unlink(filename)
>>> _ = dump(large_array, filename)
>>> large_memmap = load(filename, mmap_mode='r+')

The large_memmap variable is pointing to a numpy.memmap instance:

>>> large_memmap.__class__.__name__, large_array.nbytes, large_array.shape
('memmap', 8000000, (1000000,))

>>> np.allclose(large_array, large_memmap)
True

The original array can be freed from the main process memory:

>>> del large_array
>>> import gc
>>> _ = gc.collect()

It is possible to slice large_memmap into a smaller memmap:

>>> small_memmap = large_memmap[2:5]
>>> small_memmap.__class__.__name__, small_memmap.nbytes, small_memmap.shape
('memmap', 24, (3,))

Finally a np.ndarray view backed on that same memory mapped file can be used:

>>> small_array = np.asarray(small_memmap)
>>> small_array.__class__.__name__, small_array.nbytes, small_array.shape
('ndarray', 24, (3,))

All those three datastructures point to the same memory buffer and this same buffer will also be reused directly by the worker processes of a Parallel call:

>>> Parallel(n_jobs=2, max_nbytes=None)(
...     delayed(is_memmap)(a)
...     for a in [large_memmap, small_memmap, small_array])
[True, True, True]

Note that here max_nbytes=None is used to disable the auto-dumping feature of Parallel. small_array is still in shared memory in the worker processes because it was already backed by shared memory in the parent process. The pickling machinery of Parallel multiprocessing queues are able to detect this situation and optimize it on the fly to limit the number of memory copies.

Writing parallel computation results in shared memory

If data are opened using the w+ or r+ mode in the main program, the worker will get r+ mode access. Thus the worker will be able to write its results directly to the original data, alleviating the need of the serialization to send back the results to the parent process.

Here is an example script on parallel processing with preallocated numpy.memmap datastructures NumPy memmap in joblib.Parallel.

Warning

Having concurrent workers write on overlapping shared memory data segments, for instance by using inplace operators and assignments on a numpy.memmap instance, can lead to data corruption as numpy does not offer atomic operations. The previous example does not risk that issue as each task is updating an exclusive segment of the shared result array.

Some C/C++ compilers offer lock-free atomic primitives such as add-and-fetch or compare-and-swap that could be exposed to Python via CFFI for instance. However providing numpy-aware atomic constructs is outside of the scope of the joblib project.

A final note: don’t forget to clean up any temporary folder when you are done with the computation:

>>> import shutil
>>> try:
...     shutil.rmtree(temp_folder)
... except OSError:
...     pass  # this can sometimes fail under Windows

Note that the 'loky' backend now used by default for process-based parallelism automatically tries to maintain and reuse a pool of workers by it-self even for calls without the context manager.

Custom backend API (experimental)

New in version 0.10.

Warning

The custom backend API is experimental and subject to change without going through a deprecation cycle.

User can provide their own implementation of a parallel processing backend in addition to the 'loky', 'threading', 'multiprocessing' backends provided by default. A backend is registered with the joblib.register_parallel_backend() function by passing a name and a backend factory.

The backend factory can be any callable that returns an instance of ParallelBackendBase. Please refer to the default backends source code as a reference if you want to implement your own custom backend.

Note that it is possible to register a backend class that has some mandatory constructor parameters such as the network address and connection credentials for a remote cluster computing service:

class MyCustomBackend(ParallelBackendBase):

    def __init__(self, endpoint, api_key):
       self.endpoint = endpoint
       self.api_key = api_key

    ...
    # Do something with self.endpoint and self.api_key somewhere in
    # one of the method of the class

register_parallel_backend('custom', MyCustomBackend)

The connection parameters can then be passed to the joblib.parallel_backend() context manager:

with parallel_backend('custom', endpoint='http://compute', api_key='42'):
    Parallel()(delayed(some_function)(i) for i in range(10))

Using the context manager can be helpful when using a third-party library that uses joblib.Parallel internally while not exposing the backend argument in its own API.

Old multiprocessing backend

Prior to version 0.12, joblib used the 'multiprocessing' backend as default backend instead of 'loky'.

This backend creates an instance of multiprocessing.Pool that forks the Python interpreter in multiple processes to execute each of the items of the list. The delayed function is a simple trick to be able to create a tuple (function, args, kwargs) with a function-call syntax.

Warning

Under Windows, the use of multiprocessing.Pool requires to protect the main loop of code to avoid recursive spawning of subprocesses when using joblib.Parallel. In other words, you should be writing code like this when using the 'multiprocessing' backend:

import ....

def function1(...):
    ...

def function2(...):
    ...

...
if __name__ == '__main__':
    # do stuff with imports and functions defined about
    ...

No code should run outside of the "if __name__ == '__main__'" blocks, only imports and definitions.

The 'loky' backend used by default in joblib 0.12 and later does not impose this anymore.

Bad interaction of multiprocessing and third-party libraries

Using the 'multiprocessing' backend can cause a crash when using third party libraries that manage their own native thread-pool if the library is first used in the main process and subsequently called again in a worker process (inside the joblib.Parallel call).

Joblib version 0.12 and later are no longer subject to this problem thanks to the use of loky as the new default backend for process-based parallelism.

Prior to Python 3.4 the 'multiprocessing' backend of joblib can only use the fork strategy to create worker processes under non-Windows systems. This can cause some third-party libraries to crash or freeze. Such libraries include Apple vecLib / Accelerate (used by NumPy under OSX), some old version of OpenBLAS (prior to 0.2.10) or the OpenMP runtime implementation from GCC which is used internally by third-party libraries such as XGBoost, spaCy, OpenCV…

The best way to avoid this problem is to use the 'loky' backend instead of the multiprocessing backend. Prior to joblib 0.12, it is also possible to get joblib.Parallel configured to use the 'forkserver' start method on Python 3.4 and later. The start method has to be configured by setting the JOBLIB_START_METHOD environment variable to 'forkserver' instead of the default 'fork' start method. However the user should be aware that using the 'forkserver' method prevents joblib.Parallel to call function interactively defined in a shell session.

You can read more on this topic in the multiprocessing documentation.

Under Windows the fork system call does not exist at all so this problem does not exist (but multiprocessing has more overhead).

Parallel reference documentation

class joblib.Parallel(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)

Helper class for readable parallel mapping.

Read more in the User Guide.

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’.

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)) 
[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)) 
#...
---------------------------------------------------------------------------
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()) 
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
joblib.delayed(function, check_pickle=None)

Decorator used to capture the arguments of a function.

joblib.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.

New in version 0.10.

joblib.parallel_backend(backend, n_jobs=-1, **backend_params)

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.

Alternatively 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.

New in version 0.10.