Python Distributed-optimization Resources

OptNet: Differentiable Optimization as a Layer in Neural Networks This repository is by Brandon Amos and J. Zico Kolter and contains the PyTorch sourc

geoopt Manifold aware pytorch.optim. Unofficial implementation for “Riemannian Adaptive Optimization Methods” ICLR2019 and more. Installation Make sur

PPO Pytorch C++ This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment t

Pytorch-DPPO Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286 Using PPO with clip loss (from https

easyopt is a super simple yet super powerful optuna-based Hyperparameters Optimization Framework that requires no coding.

OptNet: Differentiable Optimization as a Layer in Neural Networks This repository is by Brandon Amos and J. Zico Kolter and contains the PyTorch sourc

Task-based End-to-end Model Learning in Stochastic Optimization This repository is by Priya L. Donti, Brandon Amos, and J. Zico Kolter and contains th

DeepPanoContext (DPC) [Project Page (with interactive results)][Paper] DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context G

Certifiable Outlier-Robust Geometric Perception About This repository holds the implementation for certifiably solving outlier-robust geometric percep

PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.

GNPy is an open-source, community-developed library for building route planning and optimization tools in real-world mesh optical networks

OptaPy is an AI constraint solver for Python to optimize the Vehicle Routing Problem, Employee Rostering, Maintenance Scheduling, Task Assignment, School Timetabling, Cloud Optimization, Conference Scheduling, Job Shop Scheduling, Bin Packing and many more planning problems.

GRBlogtools Extract information from Gurobi log files and generate pandas DataFrames or Excel worksheets for further processing. Also includes a wrapp

QQ浏览器2021AI算法大赛 - 自动超参数优化竞赛 ACM CIKM 2021 AnalyticCup 在信息流推荐业务场景中普遍存在模型或策略效果依赖于“超参数”的问题，而“超参数"的设定往往依赖人工经验调参，不仅效率低下维护成本高，而且难以实现更优效果。因此，本次赛题以超参数优化为主题，从真

Python implementation of the IPv8 layer provide authenticated communication with privacy

A python application that wraps around AlphaVantage, Quandl and IEX APIs, calculates financial statistics and optimizes portfolio allocations.

rl: An efficient framework for reinforcement learning Requirements Introduction PPO Test Requirements name version Python =3.7 numpy =1.19 torch =1

NerfingMVS Project Page | Paper | Video | Data NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo Yi Wei, Shaohui

[MM'21] Constrained Graphic Layout Generation via Latent Optimization This repository provides the official code for the paper "Constrained Graphic La

Sign-Agnostic Convolutional Occupancy Networks Paper | Supplementary | Video | Teaser Video | Project Page This repository contains the implementation

Differentiable Factor Graph Optimization for Learning Smoothers Overview Status Setup Datasets Training Evaluation Acknowledgements Overview Code rele

neural-combinatorial-rl-pytorch PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning. I have implemented the basic

Pytorch-DPPO Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286 Using PPO with clip loss (from https

Sign-Agnostic Convolutional Occupancy Networks Paper | Supplementary | Video | Teaser Video | Project Page This repository contains the implementation

pytorch-a2c-ppo-acktr Update (April 12th, 2021) PPO is great, but Soft Actor Critic can be better for many continuous control tasks. Please check out

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

DAN-Basd-on-Openmmlab DAN: Unfolding the Alternating Optimization for Blind Super Resolution We reproduce DAN via mmediting based on open-sourced code

Optimization as a Model for Few-Shot Learning This repo provides a Pytorch implementation for the Optimization as a Model for Few-Shot Learning paper.

Minimal PyTorch implementation of Generative Latent Optimization This is a reimplementation of the paper Piotr Bojanowski, Armand Joulin, David Lopez-

PyTorch implementation of TRPO Try my implementation of PPO (aka newer better variant of TRPO), unless you need to you TRPO for some specific reasons.

Deep Continuous Clustering Introduction This is a Pytorch implementation of the DCC algorithms presented in the following paper (paper): Sohil Atul Sh

Evidential Deep Learning for Guided Molecular Property Prediction and Discovery Ava Soleimany*, Alexander Amini*, Samuel Goldman*, Daniela Rus, Sangee

Licensing The majority of neural-scs is licensed under the CC BY-NC 4.0 License, however, portions of the project are available under separate license

Riggable 3D Face Reconstruction via In-Network Optimization Source code for CVPR 2021 paper "Riggable 3D Face Reconstruction via In-Network Optimizati

This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment to test the algorithm

Pyomo is a Python-based open-source software package that supports a diverse set of optimization capabilities for formulating and analyzing optimization models. Pyomo can be used to define symbolic problems, create concrete problem instances, and solve these instances with standard solvers.

Bonsai is a wrapper for the XGBoost and Catboost model training pipelines that leverages Bayesian optimization for computationally efficient hyperparameter tuning.

Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness This repository contains the code used for the exper

JAXopt Installation | Examples | References Hardware accelerated (GPU/TPU), batchable and differentiable optimizers in JAX. Installation JAXopt can be

​ DS2.ai is an integrated AI operation solution that supports all stages from custom AI development to deployment. It is an AI-specialized platform service that collects data, builds a training dataset through data labeling, and enables automatic development of artificial intelligence and easy deployment and operation.

HyperPose is a library for building high-performance custom pose estimation applications.

XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

ProSelfLC: CVPR 2021 ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks For any specific discussion or potential fu

CLIPPER: A Graph-Theoretic Framework for Robust Data Association Data association is a fundamental problem in robotics and autonomy. CLIPPER provides

Implemented fully documented Particle Swarm Optimization (PSO) algorithm in Python which includes a basic model along with few advanced features such as updating inertia weight, cognitive, social learning coefficients and maximum velocity of the particle.

WAGMA-SGD is a decentralized asynchronous SGD based on wait-avoiding group model averaging. The synchronization is relaxed by making the collectives externally-triggerable, namely, a collective can be initiated without requiring that all the processes enter it. It partially reduces the data within non-overlapping groups of process, improving the parallel scalability.

Bagua is a flexible and performant distributed training algorithm development framework.

MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees. MooGBT optimizes for multiple objectives by defining constraints on sub-objective(s) along with a primary objective. The constraints are defined as upper bounds on sub-objective loss function. MooGBT uses a Augmented Lagrangian(AL) based constrained optimization framework with Gradient Boosted Trees, to optimize for multiple objectives.

Dpex——用户无感知分布式数据预处理组件 一、前言 随着GPU与CPU的算力差距越来越大以及模型训练时的预处理Pipeline变得越来越复杂，CPU部分的数据预处理已经逐渐成为了模型训练的瓶颈所在，这导致单机的GPU配置的提升并不能带来期望的线性加速。预处理性能瓶颈的本质在于每个GPU能够使用的C

This repository contains FEDOT - an open-source framework for automated modeling and machine learning (AutoML). It can build custom modeling pipelines for different real-world processes in an automated way using an evolutionary approach. FEDOT supports classification (binary and multiclass), regression, clustering, and time series prediction tasks.

Q-Programming Summer of Qode This repository contains all the code and materials distributed in the Q-Programming Summer of Qode. If you want to creat

CoSA is a scheduler for spatial DNN accelerators that generate high-performance schedules in one shot using mixed integer programming

AdamBNN This is the pytorch implementation of our paper "How Do Adam and Training Strategies Help BNNs Optimization?", published in ICML 2021. In this

Secure Distributed Training at Scale This repository contains the implementation of experiments from the paper "Secure Distributed Training at Scale"

DistML is a Ray extension library to support large-scale distributed ML training on heterogeneous multi-node multi-GPU clusters

Toy implementations of some popular ML optimizers using Python/JAX

MALib: A parallel framework for population-based multi-agent reinforcement learning MALib is a parallel framework of population-based learning nested

OrbitalDump A simple multi-threaded distributed SSH brute-forcing tool written in Python. How it Works When the script is executed without the --proxi

Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments Paper: arXiv (ICRA 2021) Video : https://youtu.be/CC

dask-sql is a distributed SQL query engine in Python. It allows you to query and transform your data using a mixture of common SQL operations and Python code and also scale up the calculation easily if you need it.

Spherical Gaussian Optimization This is code to fit per-pixel environment map with spherical Gaussian lobes, using LBFGS optimization. This code has b

VOGUE is a StyleGAN interpolation optimization algorithm for photo-realistic try-on. Top: shirt try-on automatically synthesized by our method in two different examples.

Factor graphs + nonlinear optimization in JAX

AngularGrad Optimizer This repository contains the oficial implementation for AngularGrad: A New Optimization Technique for Angular Convergence of Con

LibAUC An end-to-end machine learning library for AUC optimization. Why LibAUC? Deep AUC Maximization (DAM) is a paradigm for learning a deep neural n

Hybrid solving process for combinatorial optimization problems Combinatorial optimization has found applications in numerous fields, from aerospace to

Bayesian optimization in JAX

MALib A general-purpose multi-agent training framework. Installation step1: build environment conda create -n malib python==3.7 -y conda activate mali

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

GenSen Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning Sandeep Subramanian, Adam Trischler, Yoshua B

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

Version: 5.1.0b1 (singularity) Web: https://docs.celeryproject.org/en/stable/index.html Download: https://pypi.org/project/celery/ Source: https://git

A multiprocessing distributed task queue for Django Features Multiprocessing worker pool Asynchronous tasks Scheduled, cron and repeated tasks Signed

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks A Research-oriented Federated Learning Library and Benchmark Platform

DC3: A learning method for optimization with hard constraints This repository is by Priya L. Donti, David Rolnick, and J. Zico Kolter and contains the

MADGRAD Optimization Algorithm For Tensorflow This package implements the MadGrad Algorithm proposed in Adaptivity without Compromise: A Momentumized,

End-to-End Optimization of Scene Layout Code release for: End-to-End Optimization of Scene Layout CVPR 2020 (Oral) Project site, Bibtex For help conta

OCTIS : Optimizing and Comparing Topic Models is Simple! OCTIS (Optimizing and Comparing Topic models Is Simple) aims at training, analyzing and compa

Parallelized symbolic regression built on Julia, and interfaced by Python. Uses regularized evolution, simulated annealing, and gradient-free optimization.

PyTorch Code for SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation Viraj Prabhu, Shivam Khare, Deeks

CS 7301: Spring 2021 Course on Advanced Topics in Optimization in Machine Learning

Exact Pareto Optimal solutions for preference based Multi-Objective Optimization

DABO: Data Augmentation with Bilevel Optimization [Paper] The goal is to automatically learn an efficient data augmentation regime for image classific

AutoOED is an optimal experiment design platform powered with automated machine learning to accelerate the discovery of optimal solutions. Our platform solves multi-objective optimization problems and automatically guides the design of experiment to be evaluated.

This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.

This repository provides a script and recipe to run the highly optimized transformer-based encoder and decoder component, and it is tested and maintained by NVIDIA.

SimplePose Code and pre-trained models for our paper, “Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation”, a

The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. I

scikit-opt Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,A

torch-optimizer torch-optimizer -- collection of optimizers for PyTorch compatible with optim module. Simple example import torch_optimizer as optim

Introduction This repository holds NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pytorch. Some of the code her

🎛 lazycluster Distributed machine learning made simple. Use your preferred distributed ML framework like a lazy engineer. Getting Started • Highlight

DEAP DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data stru

sk-dist: Distributed scikit-learn meta-estimators in PySpark What is it? sk-dist is a Python package for machine learning built on top of scikit-learn

Hivemind: decentralized deep learning in PyTorch Hivemind is a PyTorch library to train large neural networks across the Internet. Its intended usage

Project Home Blog Documents Paper Media Coverage Join Fiber users email list [email protected] Fiber Distributed Computing for AI Made Simp

BytePS BytePS is a high performance and general distributed training framework. It supports TensorFlow, Keras, PyTorch, and MXNet, and can run on eith

Apache SINGA Distributed deep learning system http://singa.apache.org Quick Start Installation Examples Issues JIRA tickets Code Analysis: Mailing Lis

A unified Data Analytics and AI platform for distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray What is Analytics Zoo? Analytics Zo

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. 10x Larger Models 10x Faster Trainin