Multi Threading Python Packages

dbutils

Database connections for multi-threaded environments

sqlitedict

Persistent dict, backed by sqlite3 and pickle, multithread-safe.

small-gicp

Efficient and parallel algorithms for point cloud registration [C++, Python]

mttkinter

Legacy multi-threading support for Tkinter, Python 2.7 or earlier only

pypenred

Parallel Engine for Radiation Energy Deposition

pmmc

Mesh-based Monte Carlo (MMC)

s3m

sqlite3 wrapper for multithreaded python applications

jsonpyes

The tool which imports raw JSON to ElasticSearch in one line of commands

pool-workers

Pool_Workers is a small python package for managing pools, workers, and queues with ease.

sqlcell

SQLCell is a magic function for the Jupyter Notebook that executes raw, parallel, parameterized SQL queries with the ability to accept Python values as parameters and assign output data to Python variables while concurrently running Python code. And *much* more.

pymultithreading

A python module for creating multithreading processes easily, in a more Pythonic way.

tykit

'Tell You kit' is a toolkit to monitor your scripts' status easily, which haves rich and pretty output for the progress bar and console logs. The tykit may support more decent output in the future

distributed-compy

Distributed_compy is a distributed computing library that offers multi-threading, heterogeneous (CPU + mult-GPU), and multi-node support

mixnmatchttp

Modular HTTP/S server: mix 'n' match Auth, Caching, Proxy, and more

iter-executor

A Python library for executing iterables in parallel with flexible executors

parallel-iterator

A Python library for executing iterables in parallel with flexible executors

threadify

An enhancement to Python threads that adds cooperative pause, unpause, and kill capabilities.

python-multithreading

A python module for creating multithreading processes easily, in a more Pythonic way.

plmap

Multi Threading and Multi Processing made easy in Python2.7

log-everywhere

Logging package to enable writing the corresponding datetime and thread on which every logged event happenes to multiple, DIFFERENT files safely when running multiple threads (multi-threading with python standard library's `threading` package).