6 مستودعات
Modules that enable concurrent execution of AI tasks to improve throughput.
Distinguishing note: Focuses on concurrency, distinct from sequential module execution.
Explore 6 awesome GitHub repositories matching artificial intelligence & ml · Parallel Execution Modules. Refine with filters or upvote what's useful.
DSPy is a declarative programming framework designed for building complex language model applications. It treats model interactions as modular, composable programs, allowing developers to define task logic through typed class schemas rather than relying on manually written prompts. By organizing workflows into hierarchical, reusable Python objects, the framework enables the construction of sophisticated AI systems that manage state and execution flow independently. The framework distinguishes itself through an automated optimization engine that iteratively refines prompt instructions and few-
Enables concurrent execution of AI tasks to improve throughput and efficiency.
Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr
Forks execution state to generate multiple model responses concurrently for efficient path exploration.
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
Enables concurrent execution of independent operations to improve throughput and resource utilization.
Gunicorn is a production-grade WSGI HTTP server designed for deploying Python web applications. It functions as a process manager that utilizes a pre-fork worker model, where a master process initializes the application and spawns multiple child processes to handle incoming requests in parallel. This architecture ensures high performance and stability by isolating application execution within persistent worker processes. The server distinguishes itself through its flexible concurrency models and robust process lifecycle management. It supports interchangeable worker types, including synchrono
Initializes the application in a master process and forks multiple child processes to handle incoming requests in parallel.
Sequel is a relational database toolkit for Ruby that provides object-relational mapping, a fluent SQL query builder, and schema migration capabilities. It maps database tables to Ruby classes with support for associations, validations, lifecycle hooks, and eager loading, offering a comprehensive ORM layer for building data-centric applications. Sequel distinguishes itself through a plugin-based extension architecture that allows composable customization of models, databases, and datasets without relying on deep inheritance hierarchies. It includes a thread-safe connection pool with support f
Disconnects all active database connections before forking to prevent shared connection issues.
هذه مكتبة عميل Ruby ومحرك بروتوكول يُستخدم لدمج تطبيقات Ruby مع قواعد بيانات Redis. تعمل كطبقة اتصال تدير إصدارات البروتوكول وتوفر واجهة متسقة لتنفيذ عمليات قاعدة البيانات. تدعم المكتبة مجموعة متنوعة من طوبولوجيا النشر، بما في ذلك النسخ المستقلة، وRedis Sentinel لاكتشاف السيد عالي التوافر وتجاوز الفشل، وRedis Cluster مع توجيه الطلبات المدرك للفتحات واكتشاف العقد. كما توفر تقسيم جانب العميل (sharding) باستخدام التجزئة المتسقة لتوزيع البيانات عبر خوادم مستقلة. تغطي القدرات الواسعة إدارة المعاملات الذرية وبرمجة Lua للتحويلات من جانب الخادم، بالإضافة إلى دعم أنواع البيانات المتخصصة مثل الإحداثيات الجغرافية والتدفقات. يتم تحسين الأداء من خلال تجميع الأوامر (pipelining) وتحليلات التحليل الأصلية، بينما يتم التعامل مع الأمان عبر تشفير SSL/TLS ومصادقة الشهادة المتبادلة. يتضمن العميل أدوات لتنسيق طوبولوجيا قاعدة البيانات المستقلة والعنقودية باستخدام حاويات لاختبار التكامل المؤتمت.
Detects process forks to trigger reconnection and prevent shared socket conflicts between parent and child processes.