9 مستودعات
Techniques for executing tasks across multiple processing units or nodes.
Distinguishing note: Focuses on parallel execution and hardware utilization in a distributed context.
Explore 9 awesome GitHub repositories matching devops & infrastructure · Distributed Computing. Refine with filters or upvote what's useful.
This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex
Executes data transfers concurrently with computations to maximize bus bandwidth and reduce total execution time.
This project is a machine learning array framework and tensor computation library designed for high-performance numerical computing. It provides a comprehensive suite of tools for constructing and training neural networks, featuring an automatic differentiation engine that facilitates gradient-based optimization and complex mathematical modeling. The library distinguishes itself through a unified memory architecture that allows data to be shared across CPU and GPU devices without explicit copies, significantly reducing data movement overhead. Its execution model relies on a lazy evaluation en
Shares processing loads across multiple physical machines using communication backends.
Meshroom is a node-based photogrammetry software designed to transform collections of two-dimensional images into three-dimensional models and scene geometry. It provides a visual interface for constructing and managing modular data pipelines, allowing users to automate complex computer vision tasks such as feature extraction, depth map estimation, and mesh generation. The software distinguishes itself through a distributed computational framework that dispatches resource-intensive tasks across local hardware or remote render farms. By utilizing a directed acyclic graph execution model, it en
Executes processing pipelines across local or remote hardware while managing node locking and resource monitoring for parallel tasks.
Modin is a distributed dataframe library and parallel data processing engine designed to handle large datasets that exceed system memory. It functions as a distributed computing framework that parallelizes data manipulation tasks across multiple CPU cores or clusters to increase throughput and avoid memory errors. The project mirrors the Pandas API, allowing for the distribution of data workflows without changing core code logic. It utilizes a pluggable backend interface, which enables users to switch between different distributed execution engines to optimize performance based on available h
Manages the execution of data tasks across various backends to optimize performance based on hardware.
This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow. It provides a comprehensive guide for building, training, and deploying neural networks, combining theoretical fundamentals with practical implementation examples. The repository distinguishes itself by covering the end-to-end machine learning workflow, from low-level tensor mathematics and linear algebra to the creation of complex model architectures. It includes specific guidance on developing data pipelines for diverse data types, such as images, text, and time-series seque
Implements distributed computing strategies to parallelize workloads across CPUs, GPUs, and TPUs.
PowerInfer is a high-performance local large language model inference engine and sparse inference framework. It provides a runtime for executing models on consumer-grade hardware, utilizing a GPU acceleration backend to optimize tensor operations for graphics processors. The system distinguishes itself through a sparse inference framework that increases generation speed by skipping computations based on activation sparsity in model weights. It includes a GGUF model converter for transforming weights and metadata into a unified binary format, as well as an OpenAI API compatible server for inte
Splits the compute graph into segments and distributes them across multiple nodes to parallelize model execution.
Featuretools is a Python data science library and automated feature engineering framework designed to create predictive features from multiple related datasets. It automates the data preparation and transformation steps required for machine learning models through deep feature synthesis. The library enables the automatic generation of comprehensive feature tables by applying recursive transformations to relational data. It supports the transformation of unstructured text into structured numeric features and allows users to define custom primitives to extend the synthesis process with specific
Distributes the recursive feature synthesis process across multiple cores or clusters for efficient large-scale processing.
هذا المشروع هو إطار عمل للمحولات (transformers) قائم على JAX ومدرب لنماذج اللغة الكبيرة مصمم لبناء وتدريب النماذج الموزعة على مسرعات الأجهزة TPU. يوفر نظاماً للتدريب المسبق والضبط الدقيق للنماذج ذاتية الانحدار عن طريق تقسيم الأوزان والحسابات عبر شبكة من الأجهزة لتقليل حمل الذاكرة وزيادة سرعة المعالجة. يتضمن إطار العمل منسق حساب TPU لتوفير الموارد وأتمتة تثبيت التبعيات عبر العقد الموزعة البعيدة. كما يتميز بمحول أوزان النموذج القادر على تحويل وإعادة تقسيم نقاط التحقق (checkpoints) بين تكوينات الأجهزة المختلفة والدقة العددية. يغطي المشروع قدرات أوسع تشمل إدارة نقاط التحقق المقسمة للتخزين السحابي، وتحميل البيانات القائم على التدفق مع استعادة الحالة، وتوليد النصوص القائم على النواة لاستنتاج النموذج. كما يدعم تسريع الأجهزة المجمع بـ XLA لمجموعات TPU و GPU ويوفر أدوات لقياس الأداء مقابل مهام اللغة الموحدة.
Automates dependency installation and cluster initialization on remote nodes for distributed execution.
هذا المشروع عبارة عن إطار عمل للمواءمة ومجموعة من خطوط المعالجة لتدريب النماذج اللغوية باستخدام الضبط الدقيق الخاضع للإشراف (supervised fine-tuning) وتحسين التفضيلات. يوفر أدوات لتنفيذ التدريب الموزع واسع النطاق عبر وحدات معالجة رسومية (GPUs) وعقد حوسبة متعددة، إلى جانب نظام لقياس مدى فائدة النموذج وجودة الحوار من خلال معايير قياس أحادية ومتعددة الجولات. يتضمن إطار العمل أدوات متخصصة لتحسين التفضيلات المباشر (DPO) لتنقية سلوك النموذج باستخدام بيانات مقترنة دون الحاجة إلى نموذج مكافأة منفصل. كما يدعم المواءمة الدستورية للذكاء الاصطناعي وتدريب نماذج المكافآت لترتيب وتقييم الاستجابات بناءً على معايير التفضيل. يغطي المشروع قدرات أوسع لدمج وخلط مجموعات البيانات، والضبط الدقيق الفعال للمعلمات عبر التكيف منخفض الرتبة (LoRA)، وتحسين أخذ عينات الرفض. يدير دورة حياة التدريب من خلال وصفات تعتمد على الإعدادات، ويوفر أنظمة لبث مقاييس الأداء في الوقت الفعلي إلى لوحات تحكم خارجية.
Coordinates large-scale model alignment tasks across multiple GPUs and compute nodes.