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tutorials - 机器学习相关教程

  •    Python

我是 周沫凡, 莫烦Python 只是谐音, 我喜欢制作, 分享所学的东西, 所以你能在这里找到很多有用的东西, 少走弯路. 你能在这里找到关于我的所有东西. 这些 tutorial 都是我用业余时间写出来, 录成视频, 如果你觉得它对你很有帮助, 请你也分享给需要学习的朋友们. 如果你看好我的经验分享, 也请考虑适当的 赞助打赏, 让我能继续分享更好的内容给大家.

fiber - Distributed Computing for AI Made Simple

  •    Python

This project is experimental and the APIs are not considered stable. Fiber is a Python distributed computing library for modern computer clusters.

react-native-multithreading - 🧵 Fast and easy multithreading for React Native using JSI

  •    C++

Since JSI is not officially released, installing react-native-multithreading requires you to edit a few native files. See the setup guide (SETUP.md) for more details. Requires react-native-reanimated 2.1.0 or higher.

billiard - Multiprocessing Pool Extensions

  •    Python

billiard is a fork of the Python 2.7 multiprocessing package. The multiprocessing package itself is a renamed and updated version of R Oudkerk's pyprocessing package. This standalone variant draws its fixes/improvements from python-trunk and provides additional bug fixes and improvements. Please report bugs related to multiprocessing at the Python bug tracker. Issues related to billiard should be reported at http://github.com/celery/billiard/issues.

curl-easy - cURL wrapper for PHP

  •    PHP

This is small but powerful and robust library which speeds the things up. If you are tired of using PHP cURL extension with its procedural interface, but you want also keep control about script execution - it's great choice for you! If you need high speed crawling in your project, you might be interested in stil/curl-easy extension - stil/curl-robot. In order to use cURL-PHP library you need to install the » libcurl package.

imgp - Multi-core batch image resizer and rotator

  •    Python

imgp is a command line image resizer and rotator for JPEG and PNG images. It can resize (or thumbnail) and rotate thousands of images in a go, at lightning speed, while saving significantly on storage. Powered by multiprocessing, an intelligent adaptive algorithm, recursive operations, shell completion scripts, EXIF preservation (and more), imgp is a very flexible utility with well-documented easy to use options.

aioprocessing - A Python 3.4+ library that integrates the multiprocessing module with asyncio

  •    Python

The aioprocessing objects can be used just like their multiprocessing equivalents - as they are in func above - but they can also be seamlessly used inside of asyncio coroutines, without ever blocking the event loop. In most cases, this library makes blocking calls to multiprocessing methods asynchronous by executing the call in a ThreadPoolExecutor, using asyncio.run_in_executor(). It does not re-implement multiprocessing using asynchronous I/O. This means there is extra overhead added when you use aioprocessing objects instead of multiprocessing objects, because each one is generally introducing a ThreadPoolExecutor containing at least one threading.Thread. It also means that all the normal risks you get when you mix threads with fork apply here, too (See http://bugs.python.org/issue6721 for more info).

parallel - Parallel Processing for Amp.

  •    PHP

True parallel processing using native threading and multiprocessing for parallelizing code, without blocking.This library is a component for Amp that provides native threading, multiprocessing, process synchronization, shared memory, and task workers. Like other Amp components, this library uses Coroutines built from Promises and Generators to make writing asynchronous code more like writing synchronous code.

process - An async process dispatcher for Amp.

  •    PHP

This package can be installed as a Composer dependency.amphp/process follows the semver semantic versioning specification like all other amphp packages.

schwimmbad - A common interface to processing pools.

  •    Python

schwimmbad provides a uniform interface to parallel processing pools and enables switching easily between local development (e.g., serial processing or with multiprocessing) and deployment on a cluster or supercomputer (via, e.g., MPI or JobLib). See the installation instructions in the documentation for more information.

multiprocess - Easy to make the common PHP/Python/js

  •    PHP

Easy to make the common PHP/Python/js...script change daemon and multi-process execution

snidel - Snidel makes parallel processing easy for all PHP developers.

  •    PHP

A multi-process container. It looks like multi-thread-ish. Not a few people, start their programming carrier with PHP, and go on. Parallel processing, they are not familiar with it, and may be a hurdle for them.

mputil - Utility functions for Python's multiprocessing standard library module

  •    Python

Mputil is (currently) a rather small package that provides functions for memory-efficient multi-processing, based Python's multiprocessing standard library. Mputil doesn't have a full-blown documentation, yet. However, you can find explanations and usage examples in the Jupyter Notebook that is references in the "Examples" section below.

Axeman - Axeman is a utility to retrieve certificates from Certificate Transparency Lists (CTLs)

  •    Python

Harvester of certificates, bearer of flannel. Axeman is a utility for downloading, parsing, and storing Certificate Transparency Lists using python3's concurrency and multi-processing. Its aim is to download and parse certificates relatively quickly and efficiently, storing them in CSVs on the local filesystem.

MultiRunner - This is a python package for multi-process running.

  •    Python

This is a python package for multi-process running.

pypette - Ridiculously simple flow controller for building complex pipelines

  •    Python

pypette (to be read as pipette) is a module which makes building pipelines ridiculously simple, allowing users to control the flow with minimal instructions. The basic unit of execution, say a python method or a callable.

aiomultiprocess - Take a modern Python codebase to the next level of performance.

  •    Python

Take a modern Python codebase to the next level of performance. On their own, AsyncIO and multiprocessing are useful, but limited: AsyncIO still can't exceed the speed of GIL, and multiprocessing only works on one task at a time. But together, they can fully realize their true potential.

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