12 repositorios
Systems that construct and modify execution paths at runtime based on logic and data.
Distinguishing note: Focuses on runtime graph generation for workflows, distinct from static build-time dependency graphs.
Explore 12 awesome GitHub repositories matching software engineering & architecture · Dynamic Task Graphs. Refine with filters or upvote what's useful.
Airflow is a workflow orchestration platform for authoring, scheduling, and monitoring complex data pipelines as code using Python. It employs a DAG-based task scheduler to manage execution timing and dependencies via directed acyclic graphs, utilizing a distributed task execution engine to run workloads across a cluster of worker nodes. The platform provides a data pipeline monitor for tracking the health and execution history of programmatic workflows. This includes a web interface for workflow progress visualization and health monitoring to identify and troubleshoot pipeline failures. The
Enables the construction and modification of execution paths at runtime using Python code to handle variable workloads.
Conductor is a durable workflow engine designed to orchestrate complex, long-running business processes and autonomous agent loops. It functions as a stateful execution platform that persists the entire history of a process, ensuring that workflows remain reliable and recoverable across infrastructure failures, system restarts, and transient network errors. By managing task lifecycles, worker polling, and state transitions, it provides a centralized coordination layer for distributed systems. The platform distinguishes itself through its specialized support for AI agent orchestration, allowin
Constructs and modifies execution paths at runtime based on previous outputs, enabling flexible branching and parallel processing.
Luigi is a Python framework designed for building and managing complex batch data pipelines. It functions as a workflow orchestration engine that organizes tasks into directed acyclic graphs, ensuring that jobs execute in the correct logical order based on their dependencies. By utilizing a centralized scheduler, the system coordinates task execution across distributed environments, tracks global workflow state, and prevents redundant processing by verifying the existence of output targets before triggering any work. The project distinguishes itself through a robust state-tracking mechanism t
Constructs and executes task graphs at runtime based on logic and data dependencies.
Dask es un framework de computación paralela y un programador de tareas distribuido diseñado para escalar flujos de trabajo de ciencia de datos en Python desde máquinas individuales hasta grandes clústeres. Funciona como un gestor de recursos de clúster que orquesta la lógica computacional representando las tareas y sus dependencias como grafos acíclicos dirigidos. Esta arquitectura permite al sistema automatizar la distribución de cargas de trabajo a través del hardware disponible mientras gestiona requisitos de ejecución complejos. El proyecto se distingue por un motor de evaluación perezosa que difiere las operaciones de datos hasta que se solicitan explícitamente, permitiendo la optimización global del grafo y una asignación eficiente de recursos. Incorpora el volcado de datos consciente de la memoria para evitar fallos del sistema al procesar conjuntos de datos que exceden la memoria disponible, y utiliza la fusión de grafos de tareas para combinar secuencias de operaciones en pasos de ejecución únicos, minimizando la sobrecarga de programación y la comunicación entre nodos. La plataforma proporciona una superficie de capacidades integral para el análisis de datos a gran escala, incluyendo soporte para aprendizaje automático distribuido, integración de computación de alto rendimiento y procesamiento de datos en paralelo. Ofrece herramientas extensas para la gestión del ciclo de vida del clúster, perfilado de rendimiento y monitoreo en tiempo real de la ejecución de tareas. Los usuarios pueden desplegar estos entornos en diversas infraestructuras, incluyendo hardware local, proveedores de nube, sistemas en contenedores y clústeres de computación de alto rendimiento.
Reduces computation overhead by fusing multiple operations into single steps to minimize inter-task communication.
This project is a framework for building AI coding agents that automate software development tasks using large language models. It includes a task lifecycle manager that tracks complex development goals through a persistent graph of dependent tasks and a system for multi-agent orchestration to delegate tasks to specialized sub-agents. The framework implements a Model Context Protocol client to discover and execute tools from external servers and provides a remote development bridge to synchronize local command line interfaces with remote containers or desktop environments. The system covers
Tracks complex development goals using a persistent graph of interdependent tasks and dependency states.
Cpp-taskflow is a C++ task-parallelism framework and task graph scheduler designed to manage and execute complex dependency graphs of parallel tasks across CPU and GPU hardware. It provides a parallel algorithm library for high-performance implementations of reductions, sorts, pipelines, and iterations. The framework distinguishes itself through its ability to offload heavy computational workloads from a task graph to graphics processors for acceleration. It also includes a task profiling tool and a performance analysis interface for visualizing task execution flow and dependency structures t
Supports the dynamic generation of nested sub-graphs to handle recursive parallel workloads within a task flow.
Taskflow is a C++ task-parallel framework designed to build high-performance parallel workflows and complex dependency graphs. It provides a programming model that organizes computational work into directed acyclic graphs, enabling developers to manage concurrency, resource scheduling, and task dependencies across multi-core CPUs and GPU accelerators. The framework distinguishes itself through its ability to orchestrate heterogeneous systems, allowing for the integration of hardware-accelerated kernels and memory operations into unified execution pipelines. It supports dynamic runtime subflow
Enables recursive parallelism by spawning sub-graphs dynamically during execution.
UFO is a multi-device task orchestrator and LLM agent orchestration framework designed to decompose natural language requests into executable task graphs. It functions as a cross-platform UI automation tool capable of performing interactions on Windows and mobile devices while routing tasks to distributed agents based on their hardware and software capabilities. The system is distinguished by its RAG-enhanced agent architecture, which integrates external documentation and previous execution traces to improve decision-making. It employs a hybrid UI detection approach that combines computer vis
Decomposes natural language goals into a dynamic graph of interdependent subtasks that can be modified during runtime.
This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene
Increases inference efficiency by fusing network layers and eliminating redundant operations in the graph.
Open Source Javascript Gantt
Models task dependencies as a directed graph to automatically update dates when links change.
This is a documentation and sample project for learning Unreal Engine's Gameplay Ability System (GAS), focused on building networked gameplay abilities that synchronize correctly in a multiplayer environment. The project provides a step-by-step guide to implementing GAS, covering the core concepts of defining gameplay mechanics through a data-driven attribute system that separates ability logic from character state management. The documentation explores key architectural patterns for multiplayer ability design, including a predicted-action network model that uses client-side prediction and se
Implements a task graph system for orchestrating complex ability sequences with asynchronous gameplay tasks.
Butterfly es una librería de visualización de grafos interactiva y motor de diagramación basado en SVG utilizado para construir diseñadores de flujos basados en nodos y editores de flujos visuales. Proporciona un framework para renderizar nodos, bordes y grupos interconectados para modelar arquitecturas técnicas, procesos de negocio y flujos de trabajo basados en datos. El proyecto se distingue por su capacidad para integrar componentes de UI personalizados como nodos de grafo y su soporte especializado para la visualización de linaje de datos. Permite el mapeo de relaciones entre campos de datos, tablas y entidades de negocio para rastrear la procedencia y monitorear el estado del flujo de trabajo. El sistema cubre un amplio rango de capacidades, incluyendo algoritmos de diseño de nodos automatizados, anidamiento de grupos jerárquicos y validación de lógica de conexión. Incluye utilidades de navegación como un minimapa, controles de zoom y paneo, y selección de marquesina, junto con herramientas para el recorrido de topología de grafos y exportación de imágenes en formatos vectoriales y rasterizados. La librería está implementada en JavaScript.
Supports adding, removing, and reorganizing nodes and edges at runtime to modify the graph structure.