contextshare 0.1.1

Simple interface for numpy arrays in multiprocessing contexts.

contextshare

Simple context manager to share numpy arrays in python multiprocessing. Installation on Python >= 3.8:

pip install contextshare

Example

The following code example is complete and compares a serial() implementation with the parallel() version thereof implemented with contextshare.

#!/usr/bin/env python
import contextshare as shared
import numpy as np


def serial(sigmas):
    alphas = []
    for sigma in sigmas:
        K = np.exp(-0.5 * D / (sigma * sigma))
        alphas.append(np.linalg.solve(K, b))
    return alphas


def parallel(sigmas):
    with shared.SharedMemory({"D": D, "b": b}, nworkers=11) as sm:

        @sm.register
        def one_sigma(sigma):
            K = np.exp(-0.5 * D / (sigma * sigma))
            return np.linalg.solve(K, b)

        for sigma in sigmas:
            one_sigma(sigma)

        return sm.evaluate(progress=True)


if __name__ == "__main__":
    N = 2000
    D = np.random.random((N, N))
    b = np.random.random(N)

    sigmas = 2.0 ** np.arange(1, 12)

    s = serial(sigmas)
    p = parallel(sigmas)

Documentation

The context manager shared.SharedMemory takes two arguments: the dictionary of numpy arrays to make available on all parallel workers and the number of workers to create.

# makes variables D and b available under the same name on 11 workers
with shared.SharedMemory({"D": D, "b": b}, nworkers=11) as sm:

The workers are spawned upon entering the context and are stopped upon exiting. Shared memory references are cleaned up automatically. Note that only numpy arrays are supported for sharing. Other arguments should be placed in the arguments of the function below. The function to call (i.e. the body of the serial for loop) needs to be placed in a function and either decorated or called explicitly:

# decorator
@sm.register
def one_sigma(sigma):
    pass

# or, equivalently, an explicit call
sm.register(one_sigma)        

Calling this decorated function returns immediately and enqueues a function call. Calling

sm.evaluate(progress=True)

starts the calculations and returns the results in order. With progress=True a progress bar is shown, default is to be silent.

Credits

This package was created with Cookiecutter using the audreyr/cookiecutter-pypackage` template.

======= History

0.1.0 (2021-02-23)

• First release on PyPI.