Skip to main content

Easy parallel processing in Python

Project description


Paraproc is a simple library that helps you easily parallelize your computation (over independent chunks of data) across multiple processes in Python, especially when you want to mix callings to external command line programs and hand brew Python functions together in your data processing pipeline.

Under the hood, it combines subprocess and multiprocessing, and uses a process pool to schedule the jobs. It also provides a numpy.ndarray interface to access shared-memory across multiple processes.

Paraproc supports both Python 2 and 3, with numpy as the only external dependency. It is contained in only one Python file, so it can be easily copied into your project. (The copyright and license notice must be retained.)

Code snippets that demonstrate the basic usage of the library can be found later in this documentation, and in the demo_*.py files.

Bugs can be reported to The code can also be found there.

Quick starts

Execute commands in parallel

You can run both Python codes and command line programs in parallel:

import os
import paraproc
def my_job():

pc = paraproc.PooledCaller()
for k in range(5):
for k in range(5):
    pc.check_call('echo $$', shell=True) # For linux/mac

The pc.check_call() method will return immediatedly. The actual execution of the queued commands are delayed until you call pc.wait().

Use shared-memory

You can load large data in shared-memory, and read or write them as a normal numpy array from multiple processes:

import numpy as np
import paraproc
def slow_operation(k, x):
    x[:100000,:] += 1 # Write access
    res = np.mean(x) # Read access
    print('#{0}: mean = {1}'.format(k, res))

a = paraproc.SharedMemoryArray.from_array(np.random.rand(1000000,500)) # About 4 GB
pc = paraproc.PooledCaller()
for k in range(pc.pool_size):
    pc.check_call(slow_operation, k, a)

The data in a is shared in memory across all children processes and never copied even with write accesses.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

paraproc-0.1.3.tar.gz (6.2 kB view hashes)

Uploaded source

Built Distribution

paraproc-0.1.3-py3-none-any.whl (6.4 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page