Popular GitHub Repositories for Parallelization

A high-performance distributed training framework for Reinforcement Learning

Training and serving large-scale neural networks with auto parallelization.

Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.

Pax is a Jax-based machine learning framework for training large scale models. Pax allows for advanced and fully configurable experimentation and parallelization, and has demonstrated industry leading model flop utilization rates.

Microsoft Finance Time Series Forecasting Framework (FinnTS) is a forecasting package that utilizes cutting-edge time series forecasting and parallelization on the cloud to produce accurate forecasts for financial data.

TePDist (TEnsor Program DISTributed) is an HLO-level automatic distributed system for DL models.

Automatic optimization and parallelization for Scientific Machine Learning (SciML)

SuperLearner guide: fitting models, ensembling, prediction, hyperparameters, parallelization, timing, feature selection, etc.

Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training

Hypernetwork training considerations and implementation types in PyTorch. Includes classification and time-series examples alongside 1D GroupConv Parallelization.

WIP. Veloce is a low-code Ray-based parallelization library that makes machine learning computation novel, efficient, and heterogeneous.

SPPU BE COMP Codes of LP1 - HPC, AIR, and DA

??? Everything you need in order to get started building distributed machine learning with Apple's MLX

Flood Fill Network for 3d Electron Microscopy volume neuron segmentation. Adapt from google FFN-master add notes and custom variation and parallelization (for windows and our retina & LGN data)

A package for synthetic data generation for imputation using single and multiple imputation methods.

In this project, we focus on different ways to optimize a machine learning model parameters.

Machine learning triggering algorithm in Python (TensorFlow 2) for weak and noisy signal from CRES. This version of the algorithm utilizes LSTM units to learn from the chirping signature of CRES signal in Fourier space. MLFlow hyperparameter optimization with multi-GPU parallelization has been added.

并行与分布式编程示例 - （多线程、MPI、混合）

Kaggle competition to predict customer transactions was performed using Linear Regression, Neural Networks and Random Forests. Separate programs were written for each of the above models with and without parallelization and the time improvisation was noted.

Реалізація паралельних обчислень за допомогою MPI та OpenMP мовою Python