Awesome GitHub RepositoriesComputational Graph Visualizers

Llama.cpp is an inference engine designed for the local execution of text-based and multimodal language models on consumer hardware. It provides a core environment for running models that process both text and image inputs, utilizing hardware-accelerated backends to optimize performance across diverse CPU and GPU architectures. The project distinguishes itself by offering a lightweight HTTP server that adheres to standard API specifications, enabling chat completion, embeddings, and reranking services. It includes a suite of tools for model quantization and conversion, which reduces memory us

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

This project is a deep learning framework designed for constructing, training, and deploying neural networks across diverse hardware environments. It functions as a high-performance tensor computation library that provides both imperative and symbolic programming interfaces, allowing developers to balance flexible, step-by-step model building with the efficiency of compiled computation graphs. The framework distinguishes itself through a hybrid execution engine that integrates declarative graph compilation with imperative runtime logic. It supports scalable, distributed training across multip

ONNX is an open-source standard for machine learning interoperability that provides a unified format for representing neural network models. By defining a common set of operators and a standardized file structure, it enables models to be shared, exported, and executed consistently across different training frameworks and software ecosystems. The project functions as an intermediate representation layer that decouples model development from deployment. It utilizes a language-neutral binary serialization format to store model structures and weights, ensuring that computational graphs remain por

OpenVINO is an AI inference engine and model serving platform designed to execute optimized deep learning models across CPUs, GPUs, and NPUs through a unified API. It includes a model optimization toolkit for converting, quantizing, and compressing models from various frameworks, alongside a specialized generative AI runtime for large language models. The project distinguishes itself through a plugin-based hardware acceleration layer that maps neural network operations to vendor-specific drivers. It features advanced execution mechanisms such as continuous batching, speculative decoding, and

TensorBoard is a visualization toolkit for tracking and analyzing machine learning model training progress and performance using TensorFlow event logs. It provides a monitoring dashboard for plotting scalar metrics, tensor distributions, and training curves, and includes specialized tools for visualizing neural network computational graphs and projecting high-dimensional embeddings. The project enables side-by-side comparison of multiple training runs to analyze the impact of hyperparameters on model outcomes. It also features a high-dimensional embedding projector and a graph visualizer for

MMdnn est un convertisseur et migrateur de modèles de deep learning conçu pour traduire les architectures et les poids de réseaux de neurones entre différents frameworks tels que TensorFlow, PyTorch et Keras. Il utilise une représentation intermédiaire standardisée pour découpler les structures de réseau et les poids des implémentations spécifiques aux frameworks, permettant ainsi la transformation de modèles pré-entraînés dans différents environnements. Le projet se distingue par la génération de code de reconstruction Python natif à partir de ses représentations intermédiaires, permettant aux modèles d'être reconstruits et affinés dans les environnements cibles. Il inclut également des outils spécialisés pour le déploiement de modèles mobiles, transformant les modèles de deep learning en formats compatibles avec les mobiles comme CoreML et TensorFlow Lite. Le système offre une suite plus large de fonctionnalités, notamment la visualisation de l'architecture des réseaux de neurones pour inspecter les structures de graphes et les métadonnées, ainsi que l'exécution d'inférence de modèles pour valider que les modèles convertis conservent leur comportement et leur précision d'origine. Des utilitaires supplémentaires gèrent la récupération de poids pré-entraînés depuis des dépôts distants et l'assemblage de points de contrôle de modèles déployables.