Awesome GitHub RepositoriesDistributed Computing

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

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

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

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

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

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

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

यह प्रोजेक्ट एक JAX-आधारित ट्रांसफार्मर फ्रेमवर्क और लार्ज लैंग्वेज मॉडल ट्रेनर है जिसे TPU हार्डवेयर एक्सेलेरेटर्स पर वितरित मॉडल्स बनाने और प्रशिक्षित करने के लिए डिज़ाइन किया गया है। यह मेमोरी ओवरहेड को कम करने और प्रोसेसिंग गति बढ़ाने के लिए डिवाइसेस के मेश पर वेट्स और कंप्यूटेशन्स को विभाजित करके ऑटोरिग्र्रेसिव मॉडल्स को प्रीट्रेन और फाइन-ट्यून करने के लिए एक सिस्टम प्रदान करता है। फ्रेमवर्क में संसाधनों को प्रोविज़न करने और रिमोट वितरित नोड्स पर डिपेंडेंसी इंस्टॉलेशन को स्वचालित करने के लिए एक TPU कंप्यूट ऑर्केस्ट्रेटर शामिल है। इसमें एक मॉडल वेट कन्वर्टर भी है जो विभिन्न हार्डवेयर कॉन्फ़िगरेशन और न्यूमेरिकल प्रिसिजन के बीच चेकपॉइंट्स को ट्रांसफॉर्म और रिशार्ड करने में सक्षम है। प्रोजेक्ट क्लाउड स्टोरेज के लिए शार्ड चेकपॉइंट मैनेजमेंट, स्टेट रिस्टोरेशन के साथ स्ट्रीम-आधारित डेटा लोडिंग, और मॉडल इन्फरेंस के लिए न्यूक्लियस-आधारित टेक्स्ट जनरेशन सहित व्यापक क्षमताओं को कवर करता है। यह TPU और GPU क्लस्टर्स के लिए XLA-कंपाइल्ड हार्डवेयर एक्सेलेरेशन का समर्थन करता है और मानकीकृत भाषा कार्यों के खिलाफ प्रदर्शन बेंचमार्किंग के लिए उपकरण प्रदान करता है।

This project is an alignment framework and suite of pipelines for training language models using supervised fine-tuning and preference optimization. It provides tools for executing large-scale distributed training across multiple GPUs and compute nodes, alongside a system for measuring model helpfulness and dialogue quality through single-turn and multi-turn benchmarks. The framework includes specialized tools for direct preference optimization to refine model behavior using paired data without a separate reward model. It also supports constitutional AI alignment and the training of reward mo

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