Rate-limit-semaphore - Semaphore implementation with rate limit restriction for async-style (any core)

Last update: Jun 21, 2022

Related tags

Overview

Rate Limit Semaphore

Rate limit semaphore for async-style (any core)

There are two implementations of rate limit semaphore. Live demo shows how FixedNewPreviousDelaySemaphore and FixedNewFirstDelaySemaphore work

Overview

import datetime
import ralisem

# Or another implementation
sem = ralisem.FixedNewPreviousDelaySemaphore(
    access_times=3, per=datetime.timedelta(seconds=1)
)
async with sem:
    ...

Difference:

FixedNewPreviousDelaySemaphore: Sures the last and a new access have a fixed required delay (per / access_times)
FixedNewFirstDelaySemaphore: Sures first and last in series (serias is access_times) have a fixed delay (per)

Methods

All of these implementations are inherited from one base TimeRateLimitSemaphoreBase. Check out full methods here

Installation

Via PyPI:

python -m pip install ralisem

Or via GitHub

python -m pip install https://github.com/deknowny/rate-limit-semaphore/archive/main.zip

Contributing

Check out Contributing section

You might also like...

A fast poisson image editing implementation that can utilize multi-core CPU or GPU to handle a high-resolution image input.

Poisson Image Editing - A Parallel Implementation Jiayi Weng (jiayiwen), Zixu Chen (zixuc) Poisson Image Editing is a technique that can fuse two imag

110 Dec 27, 2022

PyTorch implementation of Advantage async actor-critic Algorithms (A3C) in PyTorch

Advantage async actor-critic Algorithms (A3C) in PyTorch @inproceedings{mnih2016asynchronous, title={Asynchronous methods for deep reinforcement lea

111 Dec 8, 2022

Implementation of "Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner"

Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner This repository is the official implementation of Meta-rPPG: Remote Heart Ra

137 Dec 13, 2022

PyTorch implementation of the paper Deep Networks from the Principle of Rate Reduction

Deep Networks from the Principle of Rate Reduction This repository is the official PyTorch implementation of the paper Deep Networks from the Principl

459 Dec 27, 2022

PyTorch implementation of some learning rate schedulers for deep learning researcher.

pytorch-lr-scheduler PyTorch implementation of some learning rate schedulers for deep learning researcher. Usage WarmupReduceLROnPlateauScheduler Visu

59 Dec 8, 2022

Official NumPy Implementation of Deep Networks from the Principle of Rate Reduction (2021)

Deep Networks from the Principle of Rate Reduction This repository is the official NumPy implementation of the paper Deep Networks from the Principle

49 Dec 16, 2022

A Pytorch Implementation of a continuously rate adjustable learned image compression framework.

GainedVAE A Pytorch Implementation of a continuously rate adjustable learned image compression framework, Gained Variational Autoencoder(GainedVAE). N

39 Dec 24, 2022

Pytorch implementation of Learning Rate Dropout.

Learning-Rate-Dropout Pytorch implementation of Learning Rate Dropout. Paper Link: https://arxiv.org/pdf/1912.00144.pdf Train ResNet-34 for Cifar10: r

42 Nov 25, 2022

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayesian-Torch is designed to be flexible and seamless in extending a deterministic deep neural network architecture to corresponding Bayesian form by simply replacing the deterministic layers with Bayesian layers.

210 Jan 4, 2023

Releases(v0.1.0)

v0.1.0(Feb 15, 2022)

A few words…

Если вы, как и я, уже устали писать что-то под API, имеющее rate limit на запросы, и постоянно делать костыли, чтобы этот rate limit не превысить, то эта библиотека именно для вас

ralisem предоставляет две имплементации семафора с ограничением по частоте исполнения вместо ограничения по количеству исполняемых одновременно задач, что предоставляет стандартный asyncio.Semaphore. Одна из них ожидает равное количество времени между каждыми исполняемыми задачами, другая проверяет, чтобы 1 и последняя задача в серии имели заданный промежуток ожидания (прикрепил две лайв демки). Сделано на anyio, поэтому будет работать как на asyncio, так и на trio

Да, будет заюзано в квике для ограничения частоты обращений к API (собственно, это и послужило поводом)

Если кто подскажет, как на такое писать юнит тесты — буду рад (думал как-то через моки текущего времени, но жутко лень). Сейчас работает на честном слове

Доки здесь не нужно, чисто пара слов в ридми. На PyPI залита
Source code(tar.gz)
Source code(zip)

Rate-limit-semaphore - Semaphore implementation with rate limit restriction for async-style (any core)

Related tags

Overview

Rate Limit Semaphore

Overview

Methods

Installation

Contributing

You might also like...

A fast poisson image editing implementation that can utilize multi-core CPU or GPU to handle a high-resolution image input.

PyTorch implementation of Advantage async actor-critic Algorithms (A3C) in PyTorch

Implementation of "Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner"

PyTorch implementation of the paper Deep Networks from the Principle of Rate Reduction

PyTorch implementation of some learning rate schedulers for deep learning researcher.

Official NumPy Implementation of Deep Networks from the Principle of Rate Reduction (2021)

A Pytorch Implementation of a continuously rate adjustable learned image compression framework.

Pytorch implementation of Learning Rate Dropout.

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks

Releases(v0.1.0)

v0.1.0(Feb 15, 2022)

A few words…

Owner

Yan Kurbatov

rastrainer is a QGIS plugin to training remote sensing semantic segmentation model based on PaddlePaddle.

PyTorch code for 'Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning'

Code for "Searching for Efficient Multi-Stage Vision Transformers"

Fast Learning of MNL Model From General Partial Rankings with Application to Network Formation Modeling

"Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices", official implementation

SeqTR: A Simple yet Universal Network for Visual Grounding

Omniverse sample scripts - A guide for developing with Python scripts on NVIDIA Ominverse

The official implementation of "Rethink Dilated Convolution for Real-time Semantic Segmentation"

IndoNLI: A Natural Language Inference Dataset for Indonesian

Voila - Voilà turns Jupyter notebooks into standalone web applications

🔀 Visual Room Rearrangement

Code for NeurIPS2021 submission "A Surrogate Objective Framework for Prediction+Programming with Soft Constraints"

StyleGAN2-ada for practice

Official implementation of CVPR2020 paper "Deep Generative Model for Robust Imbalance Classification"

Deep Learning with PyTorch made easy 🚀 !

A graphical Semi-automatic annotation tool based on labelImg and Yolov5

A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view Stereo

This repo is duplication of jwyang/faster-rcnn.pytorch

Cleaned up code for DSTC 10: SIMMC 2.0 track: subtask 2: multimodal coreference resolution

Human-Pose-and-Motion History