Awesome GitHub RepositoriesDistributed Training Accelerators

PyTorch Lightning is a deep learning research framework that provides a structured environment for organizing machine learning code. It functions as a unified trainer orchestrator, centralizing the execution flow by managing the interaction between hardware resources, data loaders, and model components. By decoupling model architecture from training logic, the framework enables researchers to maintain clean, modular codebases that remain portable across different environments. The framework distinguishes itself through a hardware-agnostic abstraction layer that scales deep learning workloads

Swift is a toolkit for the full-parameter and parameter-efficient fine-tuning of large language and multimodal models. It functions as a multimodal model trainer for text, image, video, and audio data, and includes specialized tools for model compression and reinforcement learning from human feedback. The framework provides an alignment toolkit for optimizing model behavior using preference learning algorithms and reinforcement learning. It integrates parameter-efficient fine-tuning methods to adapt models with minimal memory and compute requirements, alongside utilities for reducing hardware

OpenPi is a vision-language-action robot control framework designed to generate physical control actions for robotic systems. It functions as a distributed robot model trainer, a model format converter, and a robot action streaming server. The framework provides tools for transforming model checkpoints between different framework formats to ensure interoperability across various development environments. It also includes a server that uses websocket connections to stream model-generated control actions from remote inference servers to physical robot hardware in real-time. The system supports

DeepEP is a distributed model accelerator and expert-parallel communication library designed to optimize the training and inference of large-scale neural networks. It provides specialized GPU communication kernels and a remote GPU memory interface to facilitate high-throughput data exchange between hardware nodes. The system utilizes dynamic kernel generation to compile optimized GPU kernels during execution, removing the need for separate installation compilation steps. It implements virtual-lane traffic isolation to prevent interference between different data streams and employs routing met

Accelerate is a PyTorch distributed training library that abstracts the boilerplate required to run models across multiple GPUs, TPUs, and CPUs. It functions as a deep learning model scaler and distributed hardware orchestrator, allowing the same training script to run on different hardware backends without modifying the core logic. The project provides a distributed training command line interface for configuring compute environments and launching jobs across single or multi-node clusters. It includes a mixed precision training framework to implement FP16 and BF16 precision, reducing memory

This repository is the official documentation for TensorFlow, a machine learning framework. It provides comprehensive guides, tutorials, and API references for building, training, and deploying machine learning models. The documentation covers the full lifecycle of machine learning projects, from constructing data pipelines and building neural networks with high-level APIs to customizing training loops and deploying trained models in production, on edge devices, or in browsers. The documentation includes step-by-step tutorials for a range of tasks, including reinforcement learning, ranking mo

PyText is an extensible PyTorch-based framework for building, training, and deploying custom natural language processing models, including text classifiers, sequence taggers, and intent-slot predictors. It provides a modular toolkit that allows developers to assemble these models using pluggable registries for model architectures, data formats, and tensorizers, all configurable through YAML files without requiring code changes. The framework distinguishes itself through its comprehensive support for the full NLP model lifecycle, from training to production inference. It includes pre-built neu

Baichuan-7B is an open-source 7 billion parameter bilingual Transformer model designed for text generation and few-shot learning across Chinese and English. It is built on a large Transformer architecture trained on a bilingual corpus, enabling it to produce coherent text in both languages from a single model. The model incorporates several optimization techniques that distinguish it from standard large language models. It uses rotary position embeddings that can extrapolate to longer sequences than seen during training, allowing context extension beyond the original 4096-token training lengt

MuseTalk is a deep learning lip synchronization system designed to align video facial movements with audio tracks for high-fidelity video dubbing. It functions as an engine that matches facial expressions to audio input in real-time, enabling the modification of a speaker's lip movements to match new audio sources across different languages. The project features a distributed GPU training pipeline and a multi-stage processing workflow for refining the visual accuracy of synthetic speech. It distinguishes itself through the use of region-specific face masking and mouth openness control, which

xtuner es un motor de entrenamiento integral para modelos de lenguaje grandes, que ofrece un toolkit para pre-entrenamiento, ajuste fino supervisado (fine-tuning) y la optimización de modelos multimodales de visión-lenguaje. Sirve como un acelerador de entrenamiento distribuido y un framework especializado para escalar modelos de Mezcla de Expertos (MoE) y alinear el comportamiento del modelo mediante aprendizaje por refuerzo a partir de retroalimentación humana (RLHF). El proyecto se distingue por optimizaciones avanzadas de memoria y cómputo, como el paralelismo de secuencia para ventanas de contexto ultra largas y el paralelismo de pipeline entrelazado para reducir el tiempo de inactividad de la GPU. Proporciona una suite dedicada para la optimización de preferencias, implementando técnicas como Group Relative Policy Optimization y Direct Preference Optimization para refinar las políticas del modelo y los sistemas de recompensa. Las áreas de capacidad cubren el entrenamiento distribuido de modelos a través de múltiples nodos, la preparación de datasets multimodales y la gestión del ajuste fino basado en adaptadores. El motor también incluye herramientas para la evaluación de modelos, fusión de pesos y exportación de parámetros entrenados a motores de inferencia. El entrenamiento se gestiona mediante archivos de configuración estandarizados y lanzadores distribuidos para asegurar resultados consistentes a través de clusters de computación.

TensorFlowOnSpark is a distributed framework for running TensorFlow machine learning workloads and model training across Apache Spark clusters. It functions as a cluster computing orchestrator that manages worker processes and resource allocation to scale deep learning tasks across multiple computing nodes. The platform enables distributed deep learning training and large-scale model inference, allowing users to execute tasks across a cluster of servers to handle datasets that exceed the memory of a single machine. It integrates deep learning workloads with Spark data processing to create end

PocketFlow is an integrated toolkit for deep learning model compression, distributed training, and mobile format optimization. It provides a system for reducing the size and complexity of neural networks to improve inference efficiency, featuring a dedicated engine for knowledge distillation and a mobile model optimizer. The framework differentiates itself through an automated hyperparameter tuning system that uses reinforcement learning and statistical models to determine optimal compression ratios and layer-wise bit allocation. It also includes a distributed training system that utilizes mu