Python Concurrent-processes Resources

GP-VAE This repository provides datasets and code for preprocessing, training and testing models for the paper: Diverse Text Generation via Variationa

Towards Return Parity in Markov Decision Processes Code for the AISTATS 2022 pap

MOT-Tracking-by-Detection-Pipeline Tracking-by-Detection形式のMOT(Multi Object Trac

This speeds up PyCharm's package index processes and avoids CPU & memory overloading

Event Queue Dialect Event queue (Equeue) dialect is an MLIR Dialect that models concurrent devices in terms of control and structure. Motivation The m

On the adaptation of recurrent neural networks for system identification This repository contains the Python code to reproduce the results of the pape

A Python package for simulating Active Inference agents in Markov Decision Process environments. Please see our companion preprint on arxiv for an ove

Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras which will then be used to generate residuals

Designed a greedy algorithm based on Markov sequential decision-making process in MATLAB/Python to optimize using Gurobi solver, the wheel size, gear shifting sequence by modeling drivetrain constraints to achieve maximum laps in a race with a 2-hour time window.

Concurrent Sync A concurrent sync tool which works similar to rsync. It supports syncing given sources with multiple targets concurrently. Requirement

Media Fixer Have you ever had a film or TV show that your TV wasn't able to play its audio? Well this program is for you. Media Fixer is a program whi

ExportTelegramUsers A Python script that exports users from one Telegram group to another using one or more concurrent user bots. Make sure to set all

AutoParaSelenium A library to make parallel selenium tests that automatically download and setup webdrivers Usage Installation pip install autoparasel

Spatio-Temporal Variational GPs This repository is the official implementation of the methods in the publication: O. Hamelijnck, W.J. Wilkinson, N.A.

Distributed Grid Descent: an algorithm for hyperparameter tuning guided by Bayesian inference, designed to run on multiple processes and potentially many machines with no central point of control.

🌀 Pykka Pykka makes it easier to build concurrent applications. Pykka is a Python implementation of the actor model. The actor model introduces some

Pyautogui Utilizei a biblioteca pyautogui para automatizar alguns processos no c

Futured provides a consistent interface for concurrent functional programming in Python. It wraps any callable to return a concurrent.futures.Future,

rosny is a lightweight library for building concurrent systems. Installation Tested on: Linux Python = 3.6 From pip: pip install rosny From source: p

jumpdiff jumpdiff is a python library with non-parametric Nadaraya─Watson estimators to extract the parameters of jump-diffusion processes. With jumpd

Run python scripts and pass data between multiple python and node processes using this npm module. process-communication has a event based architecture for interacting with python data and errors inside nodejs.

About Python's multithreading and GIL

pyeventbus pyeventbus is a publish/subscribe event bus for Python 2.7. simplifies the communication between python classes decouples event senders and

FGrequests: Fastest Asynchronous Group Requests Installation Install using pip: pip install fgrequests Documentation Pretty easy to use. import fgrequ

NPPDecay This is our implementation for the paper Self-Adaptable Point Processes with Nonparametric Time Decays, by Zhimeng Pan, Zheng Wang, Jeff M. P

ConnectMP - Taking Multi-Process Data Sharing to the moon 🚀 Contribute · Community · Documentation 🎫 Introduction : 🍤 ConnectMP is the easiest and

brewhouse-management A desktop application developed in Python with PyQt5 to predict demand and help monitor and schedule brewing processes for Barnab

The Neural Process Family This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CN

Bender 🤖 A Python package for faster, safer, and simpler ML processes. Why use bender? Bender will make your machine learning processes, faster, safe

Log4j2 Vulnerability Local Scanner (CVE-2021-45046) Log4j 漏洞本地检测脚本，扫描主机上所有java进程，检测是否引入了有漏洞的log4j-core jar包，是否可能遭到远程代码执行攻击（CVE-2021-45046）。上传扫描报告到指定的服

This is a backport of the concurrent.futures standard library module to Python 2. It does not work on Python 3 due to Python 2 syntax being used in th

unsync Unsynchronize asyncio by using an ambient event loop, or executing in separate threads or processes. Quick Overview Functions marked with the @

Flyte Flyte is a workflow automation platform for complex, mission-critical data, and ML processes at scale Home Page · Quick Start · Documentation ·

Deep Latent Force Models This repository contains a PyTorch implementation of the deep latent force model (DLFM), presented in the paper, Compositiona

Doctoral dissertation of Zheng Zhao This thesis is mainly concerned with state-space methods for a class of deep Gaussian process (DGP) regression pro

Pypeln Pypeln (pronounced as "pypeline") is a simple yet powerful Python library for creating concurrent data pipelines. Main Features Simple: Pypeln

A synchronous, single-threaded interface for starting processes on Linux

AALpy An Active Automata Learning Library AALpy is a light-weight active automata learning library written in pure Python. You can start learning auto

DGPNet - Dense Gaussian Processes for Few-Shot Segmentation Welcome to the public repository for DGPNet. The paper is available at arxiv: https://arxi

counterfactual-tpp This is a repository containing code and real data for the paper Counterfactual Temporal Point Processes. Pre-requisites This code

counterfactual-tpp This is a repository containing code and real data for the paper Counterfactual Temporal Point Processes. Pre-requisites This code

Prior-RObust Bayesian Optimization (PROBO) Introduction, TOC This repository contains Prior-RObust Bayesian Optimization (PROBO) as introduced in our

Sync-stream This project is designed for providing the synchoronization of the stdout / stderr among different threads, processes, devices or hosts.

Dual Parameterization of Sparse Variational Gaussian Processes Documentation | Notebooks | API reference Introduction This repository is the official

Bayes-Newton Bayes-Newton is a library for approximate inference in Gaussian processes (GPs) in JAX (with objax), built and actively maintained by Wil

Machine Learning Portal Goal Application Workflow Process Design Live Project Goal A complete end-to-end machine learning portal that covers processes

Conditioning Sparse Variational Gaussian Processes for Online Decision-making This repository contains a PyTorch and GPyTorch implementation of the pa

Gorrabot is a Gitlab bot made to automate checks and processes in the Faraday development. Features Check that the CHANGELOG is modified By default, m

Modular Gaussian Processes for Transfer Learning 🧩 Introduction This repository contains the implementation of our paper Modular Gaussian Processes f

Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms applied on Continuous Control Tasks This is the master thesi

Parental Control Application LittleBrother Overview LittleBrother is a simple parental control application monitoring specific processes (read "games"

vmgp This is the repository of Vivek Myers and Nikhil Sardana for our CS 330 final project, Bayesian Meta-Learning Through Variational Gaussian Proces

QuakeLabeler Quake Labeler was born from the need for seismologists and developers who are not AI specialists to easily, quickly, and independently bu

future-map is a Python library to use together with the official concurrent.futures module.

BO-GP Bayesian optimization based on Gaussian processes (BO-GP) for CFD simulations. The BO-GP codes are developed using GPy and GPyOpt. The optimizer

Sparse Implicit Processes In this repository we include the two different versions of the SIP code developed for the article Sparse Implicit Processes

Markovflow Documentation | Tutorials | API reference | Slack What does Markovflow do? Markovflow is a Python library for time-series analysis via prob

Fast as FUCK nvim completion. SQLite, concurrent scheduler, hundreds of hours of optimization.

Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay This is the official implementation of our paper "Diversity-based Traje

This tool gives developers, researchers and companies the ability to analyze software packages of different programming languages that are being or will be used in their codes, providing information that allows them to know in advance if this library complies with processes. secure development, if currently supported, possible backdoors (malicious embedded code), typosquatting analysis, the history of versions and reported vulnerabilities (CVEs) of the package.

Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge: Official Pytorch implementation of ICLR 2018 paper Deep Learning for Phy

A lightweight (serverless) native python parallel processing framework based on simple decorators and call graphs, supporting both control flow and dataflow execution paradigms as well as de-centralized CPU & GPU scheduling.

Matern Gaussian Processes on Graphs This repo provides an extension for gpflow with Matérn kernels, inducing variables and trainable models implemente

Fast and Flexible Temporal Point Processes with Triangular Maps This repository includes a reference implementation of the algorithms described in "Fa

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Socorro Socorro is a Mozilla-centric ingestion pipeline and analysis tools for crash reports using the Breakpad libraries. Support This is a Mozilla-s

Website | Documentation (release) | Documentation (develop) | Glossary Table of Contents What does GPflow do? Installation Getting Started with GPflow

GPyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian

PyTorch Implementation of Differentiable SDE Solvers This library provides stochastic differential equation (SDE) solvers with GPU support and efficie

Neural ODE Processes Official code for the paper Neural ODE Processes (ICLR 2021). Abstract Neural Ordinary Differential Equations (NODEs) use a neura

NColony Infrastructure for running "colonies" of processes. Hacking $ tox Should DTRT -- if it passes, it means unit tests are passing, and 100% cover

SafeOpt - Safe Bayesian Optimization This code implements an adapted version of the safe, Bayesian optimization algorithm, SafeOpt [1], [2]. It also p

Bayesian Optimization Pure Python implementation of bayesian global optimization with gaussian processes. PyPI (pip): $ pip install bayesian-optimizat

GPyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian

Aboleth A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation [1] with stochastic gradient variational Bayes

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

pyrasite Tools for injecting arbitrary code into running Python processes. homepage: http://pyrasite.com documentation: http://pyrasite.rtfd.org downl

numpy-ml Ever wish you had an inefficient but somewhat legible collection of machine learning algorithms implemented exclusively in NumPy? No? Install

DISCLAIMER: This is not an official google project, this is just something I wrote while at Google. Pyringe What this is Pyringe is a python debugger

SCOOP (Scalable COncurrent Operations in Python) is a distributed task module allowing concurrent parallel programming on various environments, from h