9 dépôts
Management of version identifiers to maintain compatibility between serialized data and evolving classes.
Distinct from Version Control: Focuses on data serialization versioning rather than source code version control
Explore 9 awesome GitHub repositories matching devops & infrastructure · Serialization Versioning. Refine with filters or upvote what's useful.
This project is a comprehensive technical interview preparation resource and computer science interview guide. It serves as an educational reference for developers to study core software engineering fundamentals and common coding patterns required for employment screenings. The repository provides detailed guides and references covering data structures and algorithms, networking and security, operating systems, and web development. It specifically focuses on the implementation and complexity analysis of sorting, searching, and graph algorithms. The material encompasses a wide breadth of comp
Covers the use of version identifiers to prevent exceptions during object deserialization.
Badger is an embeddable key-value store written in Go that provides persistent data storage for byte keys and values. It is a persistent database that utilizes a tiered LSM tree storage model to optimize disk storage and retrieval efficiency. The system features an ACID transaction engine that ensures data integrity through serializable snapshot isolation and multi-version concurrency control. It also provides an encrypted key-value store with data-at-rest encryption and a managed encrypted key registry to secure stored information. The engine covers a broad set of capabilities including hig
Tracks disk format versions to ensure compatibility and manage necessary data transformations.
node-uuid is a JavaScript library for generating and validating universally unique identifiers that comply with the RFC 4122 standard. It provides a utility for creating random, timestamp-based, or namespace-based identifiers within a Node.js environment. The library includes tools for detecting the specific standard version of a provided identifier and transforming identifiers between different versions. It also provides a command line utility for generating identifiers directly from the terminal. The project covers binary manipulation, including parsing strings into byte arrays and stringi
Detects the version of a unique identifier and transforms it into a different standard version.
OpenTTD is an open-source game engine and transport simulation game. It provides an isometric sandbox environment for building and managing complex logistics and transport networks. The project functions as a multiplayer simulation sandbox where users can build infrastructure cooperatively or competitively in a shared virtual world. The platform is designed as a moddable simulation system that supports external assets, graphics, and gameplay modifications. It includes mechanisms for downloading and integrating add-on content and utilizes a plugin-based system to extend game mechanics beyond t
Uses serialization versioning to ensure game state files remain readable across different software versions.
Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis
Manages multiple versions of serialized classes to ensure compatibility between clients and cluster members.
Flashlight est une bibliothèque de machine learning et de tenseurs autonome en C++ utilisée pour construire et entraîner des réseaux de neurones. Elle fonctionne comme un framework complet de réseaux de neurones et un moteur de différenciation automatique, fournissant les outils pour construire des graphes de calcul et calculer les gradients via la rétropropagation. Le projet sert de framework d'entraînement distribué, utilisant des opérations all-reduce pour synchroniser les gradients et les paramètres sur plusieurs nœuds de calcul et appareils. Il se distingue par une intégration profonde de la manipulation de tenseurs haute performance, l'interopérabilité native de la mémoire des appareils et un système pour synchroniser les poids entre les workers distribués afin d'accélérer l'entraînement de modèles à grande échelle. Le framework couvre un large éventail de capacités de deep learning, incluant la composition modulaire de couches pour concevoir des architectures complexes comme des blocs résiduels et des cellules récurrentes. Il fournit des utilitaires étendus de gestion de données pour l'ingestion et le préchargement, ainsi que des systèmes de sérialisation pour persister les états de modèle. De plus, il inclut une suite d'outils de surveillance et d'observabilité pour suivre les métriques d'entraînement et mesurer les erreurs de séquence. La bibliothèque est implémentée en C++.
Maintains backward compatibility for loading model states from binary files using class and member versioning.
Flashlight est une bibliothèque de machine learning en C++ et un framework de deep learning conçu pour construire et entraîner des réseaux de neurones. Il fonctionne comme une bibliothèque de manipulation de tenseurs et un moteur de différenciation automatique qui suit les opérations pour calculer les gradients via la rétropropagation pour l'optimisation des modèles. Le projet se distingue par son rôle de framework d'entraînement distribué, utilisant la synchronisation de gradient all-reduce et des environnements distribués pour mettre à l'échelle les charges de travail de machine learning sur plusieurs nœuds et appareils. Il dispose d'une interface mémoire agnostique au backend et d'une gestion basée sur RAII pour découpler les opérations sur tenseurs du matériel physique. Le framework couvre une large surface de capacités, incluant la construction d'architectures de réseaux de neurones avec des couches convolutionnelles, linéaires et récurrentes. Il fournit des utilitaires étendus pour l'algèbre tensorielle, la gestion et le batching de jeux de données, la sérialisation binaire versionnée pour les états de modèle, et des outils de surveillance pour suivre les métriques d'entraînement et l'utilisation de la mémoire.
Persists model states and tensors to disk using version numbers to ensure backward compatibility.
Hyperscan is a high-performance regular expression matching library that scans large volumes of data against thousands of patterns simultaneously. It accepts PCRE-compatible regular expressions and supports multi-pattern matching in a single pass, approximate matching within a configurable edit distance, and streaming mode for processing data that arrives in blocks. The library is designed for throughput-oriented scanning across block, streaming, and vectored inputs. What distinguishes Hyperscan is its hybrid automata engine, which combines deterministic and nondeterministic finite automata t
Serializes compiled pattern databases with version and platform checks for storage or transfer.
Julius is a cross-platform game engine and simulation tool designed for the reimplementation of legacy games. It executes original game logic using the original assets while applying modern resolution and interface updates. The engine features a localization framework for integrating community translations of text, audio, and video, and an integrated scenario editor for configuring gameplay rules and resource requirements within map data files. The project covers a broad capability surface including save game management with automated backups, input mapping for diverse keyboard layouts and t
Manages game state files to ensure compatibility and data preservation across different versions of the software.