21 repositorios
Tools for executing non-interactive, task-based container workloads that run to completion.
Explore 21 awesome GitHub repositories matching devops & infrastructure · Batch Workload Execution. Refine with filters or upvote what's useful.
Kubernetes is a distributed container orchestration platform that automates the deployment, scaling, and management of containerized applications across clusters of computing nodes. It functions as a declarative infrastructure controller, utilizing a control loop architecture that continuously monitors the current system state against user-defined configurations to ensure desired operational outcomes. The system relies on a centralized API-driven interface and a replicated key-value store to maintain a consistent source of truth for all cluster objects. The platform distinguishes itself throu
Supports non-interactive, task-based workloads by automatically managing container lifecycles until completion.
Prefect is a workflow orchestration platform designed to define, schedule, and monitor complex data pipelines as Python code. It functions as a container-native engine that wraps individual tasks in isolated environments, ensuring consistent dependencies and resource allocation across diverse infrastructure. By utilizing a state-machine-based orchestration model, the system tracks execution progress through discrete transitions and persistent event logs to maintain reliable and observable task processing. The platform distinguishes itself through a decoupled worker-API architecture, which sep
Runs workflow tasks as isolated jobs within managed serverless container environments.
Claude Quickstarts is a development framework and collection of reference implementations designed for building autonomous agents. It provides the foundational patterns necessary to orchestrate multi-agent workflows, enabling models to perform complex, multi-step tasks across software engineering, customer support, and computer-use domains. The platform distinguishes itself through specialized capabilities for desktop and browser automation, allowing agents to interact with graphical interfaces by capturing visual context and executing precise mouse and keyboard inputs. It includes robust inf
Chains multiple predictable actions into a single request to reduce latency and costs for workflows.
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.
Processes data tasks on individual compute nodes by connecting to a central scheduler and reporting completion status.
The AWS Cloud Development Kit is an infrastructure-as-code framework that enables developers to define and provision cloud resources using familiar programming languages. By utilizing construct-based synthesis, it translates high-level, object-oriented code into declarative templates, allowing for the automated management of complex cloud environments through a centralized, code-driven control plane. The framework distinguishes itself through its ability to model infrastructure as a dependency-aware resource graph, ensuring that components are provisioned and updated in the correct order. It
Executes large-scale computational tasks by automatically provisioning and managing infrastructure.
Boto3 is the AWS SDK for Python, providing a programmatic interface for managing and automating AWS cloud infrastructure and services. It serves as a cloud management API client and resource manager for provisioning, configuring, and scaling virtual servers, databases, and storage. The library enables the implementation of infrastructure-as-code through declarative templates and scripts, allowing for the deployment of identical resource stacks across multiple accounts and geographic regions. It also provides a framework for coordinating distributed workflows, serverless functions, and contain
Provides tools for executing and optimizing the distribution of large-scale non-interactive batch workloads.
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
Groups multiple speculative actions into single requests to optimize latency and reduce the number of model calls.
TechEmpower FrameworkBenchmarks is an open-source project that provides a standardized, reproducible suite for measuring and comparing the performance of web frameworks across many languages. At its core, it defines a set of common server-side tasks—such as JSON serialization, database queries, and server-side template rendering—and executes them uniformly against hundreds of framework implementations to produce comparable throughput and latency metrics. The project is built around a multi-language benchmark harness that automates the full test lifecycle, from dependency installation and serve
Executes a suite of predefined HTTP workloads across multiple framework implementations to measure throughput and latency.
This is a scikit-learn automated machine learning framework designed to optimize model selection and hyperparameters. It functions as an automated model selector and hyperparameter optimization tool for classification and regression tasks, utilizing an automated ensemble builder to combine high-performing models for increased predictive accuracy. The system features a distributed search engine that uses Dask for parallel machine learning optimization across CPU cores or clusters. It implements a budget-based evaluation strategy through successive halving to prioritize promising model configur
Runs predefined model training tasks in parallel across hardware to accelerate model discovery.
ToyDB is a distributed SQL database that provides a system for storing and querying data across multiple nodes. It focuses on maintaining strong consistency and fault tolerance through the implementation of a distributed consensus algorithm. The project distinguishes itself by supporting historical data versioning, enabling time-travel queries to retrieve the state of the database from a specific point in the past. It utilizes multi-version concurrency control to manage ACID transactions and ensure data integrity during concurrent operations. The system covers relational data modeling with t
Executes predefined read, write, and transactional benchmarks to measure overall cluster performance.
OpenWhisk is a serverless cloud platform designed for deploying and executing stateless functions in response to API calls or events. It serves as a complete serverless stack, providing an API gateway for functions, a function-as-a-service runtime manager, and an event-driven workflow engine. The platform distinguishes itself through a polyglot execution model that supports multiple language runtimes and allows for the creation of custom runtimes using Docker containers. It enables complex logic through function orchestration and composition, allowing multiple functions to be chained into seq
Links multiple functions together into sequential pipelines where the output of one serves as the input for the next.
sysbench is a database and system benchmark tool used to measure the throughput and latency of database systems and hardware components. It functions as a multi-threaded workload generator and hardware performance profiler designed to determine how systems perform under heavy load. The project serves as a scriptable benchmark engine, allowing for the definition of custom performance scenarios through scripts. It simulates real-world traffic patterns by generating random data based on mathematical probability distributions, such as Zipfian, Gaussian, or Pareto. Capabilities cover database per
Executes predefined computational tasks and database queries to isolate and measure the performance of specific hardware subsystems.
StackStorm is an event-driven automation platform that watches for events from external systems and triggers workflows, actions, and remediation across infrastructure tools. At its core, it provides a workflow orchestration engine that chains multiple actions and conditional logic into reusable, multi-step workflows for complex automation tasks, alongside a rules engine that applies matching criteria to triggers and maps trigger payload data to action inputs for automated responses. The platform distinguishes itself through a ChatOps integration framework that enables executing commands and r
Assembles multiple individual tasks into a reusable, ordered pipeline that runs as a single automated workflow.
ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning pipelines and agentic workflows. It provides a unified framework that manages the entire lifecycle of machine learning assets, from data processing and model training to the deployment of persistent inference services. By decoupling pipeline logic from underlying compute and storage, the platform enables teams to transition workflows seamlessly from local development environments to production-grade cloud infrastructure. The platform distinguishes itself through a service-oriented
Configures the server workload manager to support snapshot-backed deployment execution and automated pipeline re-runs from checkpoints.
Este proyecto proporciona hojas de ruta estratégicas y guías que detallan la evolución y los patrones de despliegue de servicios gestionados de orquestación de contenedores y seguridad. Sirve como un documento de seguimiento público para las próximas características y prioridades de desarrollo para EKS, ECS, ECR y Fargate. El recurso incluye una guía de orquestación de contenedores en la nube y una estrategia para Kubernetes y ECS, describiendo el desarrollo de Kubernetes gestionado y servicios de orquestación propietarios para infraestructura en la nube. También proporciona un plan de seguridad y monitorización centrado en el escaneo de actividad maliciosa y el seguimiento de la salud de las cargas de trabajo. El material cubre una amplia gama de capacidades de infraestructura, incluyendo aprovisionamiento de recursos, escalado automático de cómputo y tareas, y gestión de imágenes de contenedor. Aborda la gestión de redes y tráfico mediante balanceo de carga y optimización de densidad de pods, así como la observabilidad mediante enrutamiento de logs y seguimiento de rendimiento.
Outlines the execution of non-interactive batch jobs and stateless applications across managed infrastructure.
Este proyecto es el sitio web oficial de documentación de Kubernetes, que sirve como un recurso técnico integral para gestionar aplicaciones contenedorizadas. Funciona como un portal de documentación técnica de código abierto que proporciona guías, tutoriales y materiales de referencia para software de sistemas distribuidos. El sitio está construido utilizando un generador de sitios estáticos con una arquitectura de plantillas basada en componentes para mantener patrones de diseño consistentes. Cuenta con un generador de documentación OpenAPI que analiza especificaciones técnicas para construir y actualizar automáticamente páginas de referencia de API estructuradas. Para apoyar a una audiencia global, emplea enrutamiento de contenido consciente de la internacionalización para gestionar versiones localizadas de los manuales. El flujo de trabajo de desarrollo incluye un servidor de recarga en caliente (hot-reloading) para previsualizar cambios en el sitio y renderizado de idiomas específico para acelerar los tiempos de compilación. El proyecto cubre una amplia gama de dominios técnicos, incluyendo orquestación de clústeres, configuración de red y gestión de recursos.
Executes non-interactive background jobs by ensuring failed containers are replaced until completion.
Baserow es una base de datos relacional no-code y constructor de aplicaciones que permite a los usuarios crear tablas de datos estructurados y herramientas de negocio a través de una interfaz visual. Funciona como un backend de datos de API REST headless y un espacio de trabajo de datos autohospedado, proporcionando una plataforma para gestionar bases de datos colaborativas mientras se mantiene el control total sobre la residencia de los datos. La plataforma integra modelos de lenguaje grandes para servir como una plataforma de datos potenciada por LLM, capaz de generar estructuras de bases de datos, contenido de registros y flujos de trabajo técnicos a partir de lenguaje natural. También actúa como un servidor de Protocolo de Contexto de Modelo (MCP), permitiendo que agentes de IA remotos interactúen con registros de bases de datos estructurados programáticamente. Más allá de sus capacidades principales de base de datos, el proyecto proporciona herramientas para construir portales externos de marca, aplicaciones de negocio internas y paneles interactivos. Incluye un motor de automatización basado en eventos para la automatización de procesos de negocio y admite una amplia gama de integraciones de API, incluyendo webhooks, transmisión de eventos WebSocket y sincronización de datos de terceros. El software está diseñado para el alojamiento en infraestructura privada y despliegue contenedorizado para garantizar la soberanía y seguridad de los datos.
Chains complex actions and notifications based on database events to automate repetitive tasks
OnmyojiAutoScript is an ADB-based Android game automation tool that utilizes image recognition to automate daily routines and resource farming. It functions as a computer vision bot and emulator controller, coordinating simultaneous task execution across multiple user profiles and ports. The system features a game resource optimizer that uses efficiency models to intelligently distribute in-game items and assets across characters. To bypass automated detection, it employs an anti-detection input simulator that mimics human interaction patterns through variable click and swipe behaviors. The
Triggers automation steps based on the detection of visual cues instead of fixed time delays.
P4wnP1_aloa es un framework de seguridad física diseñado para transformar una Raspberry Pi en un dispositivo dedicado para red teaming y pruebas de penetración. Funciona como una herramienta de emulación de gadgets USB, una utilidad de suplantación de redes inalámbricas y un controlador de automatización GPIO. El sistema permite la emulación de periféricos USB compuestos, como teclados, ratones y dispositivos de almacenamiento, sin requerir un reinicio. Además, proporciona capacidades para transmitir balizas de puntos de acceso falsos y respuestas suplantadas para emular diversos entornos de red inalámbrica. El framework incluye una interfaz de gestión remota accesible a través de una aplicación web y una interfaz de línea de comandos. Cuenta con un sistema de disparadores basado en eventos para ejecutar scripts basados en cambios de estado del hardware y entrada GPIO, así como un sistema de configuración basado en plantillas para gestionar la configuración de subsistemas. El proyecto proporciona herramientas para la automatización de entradas, configuración de interfaces de red y la creación de interfaces de red basadas en Bluetooth para acceso remoto.
Coordinates multiple triggers using group channels to ensure actions execute only after a specific sequence of events.
IREE is an MLIR-based compiler toolchain and runtime designed to translate machine learning models from various frameworks into optimized binaries for execution across diverse hardware targets. It provides a unified pipeline to ingest models from PyTorch, TensorFlow, JAX, and ONNX, lowering them into a common intermediate representation for deployment on CPUs, GPUs, and bare-metal embedded systems. The project distinguishes itself through a bytecode virtual machine and a hardware abstraction layer that decouple high-level model logic from specific hardware instruction sets. It supports sophis
Runs recorded sequences of HAL operations on matching devices to reproduce specific hardware behaviors.