awesome-repositories.com
Blog
MCP
awesome-repositories.com

Découvrez les meilleurs dépôts open-source grâce à notre recherche par IA.

ExplorerRecherches sélectionnéesAlternatives open sourceLogiciels auto-hébergésBlogPlan du site
ProjetServeur MCPÀ proposNotre méthodologiePresse
Mentions légalesConfidentialitéConditions d'utilisation
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

44 dépôts

Awesome GitHub RepositoriesPipeline Component Modularization

Architectural patterns for isolating individual processing steps into swappable units within a data pipeline.

Distinct from Modular Design Patterns: Distinct from general Modular Design Patterns by focusing specifically on the decomposition of linear or graph-based processing pipelines.

Explore 44 awesome GitHub repositories matching software engineering & architecture · Pipeline Component Modularization. Refine with filters or upvote what's useful.

Awesome Pipeline Component Modularization GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • google/mediapipeAvatar de google

    google/mediapipe

    35,673Voir sur GitHub↗

    MediaPipe is a cross-platform machine learning framework designed for building and deploying pipelines that process live and streaming media. It provides a system for connecting processing components into custom machine learning chains to analyze real-time audio and video streams. The framework includes a suite of pre-trained models for tasks such as hand, face, and pose tracking, along with tools for retraining and customizing these models with specific datasets. It also features a dedicated benchmarker for measuring the execution speed and accuracy of machine learning models directly within

    Implements a modular design where processing logic is encapsulated into discrete calculators that can be reconfigured into different pipelines.

    C++
    Voir sur GitHub↗35,673
  • facebookresearch/detectron2Avatar de facebookresearch

    facebookresearch/detectron2

    34,548Voir sur GitHub↗

    Detectron2 is a PyTorch computer vision framework and visual recognition platform designed for training and deploying models for object detection, image segmentation, and visual recognition. It provides a research-oriented environment for training complex vision models with multi-GPU acceleration. The project includes a specialized object detection library for identifying and locating multiple objects via bounding boxes, as well as an image segmentation toolkit for creating pixel-level masks through instance, semantic, and panoptic segmentation. Additionally, it features a human pose estimati

    Utilizes a modular architecture to assemble detection pipelines from interchangeable processing components.

    Python
    Voir sur GitHub↗34,548
  • microsoft/taskmatrixAvatar de microsoft

    microsoft/TaskMatrix

    34,079Voir sur GitHub↗

    TaskMatrix is a visual language model orchestration framework and modular visual pipeline designed to coordinate disparate foundation models. It functions as a multi-model workflow coordinator that sequences visual and textual models through logic paths to handle image processing tasks without requiring additional training. The system integrates large language models with visual foundation models to enable the exchange of image data during interactive chat sessions. It utilizes template-based orchestration to chain specialized models together for complex visual tasks. The framework supports

    Implements a sequence of pre-defined templates that chain disparate foundation models to solve visual tasks.

    Python
    Voir sur GitHub↗34,079
  • hwchase17/langchainjsAvatar de hwchase17

    hwchase17/langchainjs

    17,822Voir sur GitHub↗

    LangChainJS is an AI agent orchestrator and application framework designed for building autonomous systems that use large language models to plan and execute tasks. It serves as an integration library that connects language models with tools, memory, and external data sources to create context-aware logic and complex workflows. The project provides a provider-agnostic interface and model provider abstraction, allowing applications to switch between different language model providers without rewriting core logic. It includes a toolkit for retrieval augmented generation, utilizing retrievers to

    Implements a pipeline pattern that links discrete AI components to pass data and state between sequential steps.

    TypeScript
    Voir sur GitHub↗17,822
  • nvidia/nemoAvatar de NVIDIA

    NVIDIA/NeMo

    17,394Voir sur GitHub↗

    NeMo is a multimodal AI framework and toolkit designed for the development, training, and scaling of large language models, generative AI systems, and speech-based models. It functions as an automatic speech recognition toolkit, a text-to-speech engine, and a framework for building models that process and generate combinations of text, image, and audio data. The project serves as a conversational AI orchestrator capable of managing real-time, interruptible voice interactions. It provides specialized workflows for speech translation, converting spoken audio from one language into text or speec

    Supports the composition of multimodal generative pipelines by combining separate audio and text encoders and decoders.

    Python
    Voir sur GitHub↗17,394
  • zulko/moviepyAvatar de Zulko

    Zulko/moviepy

    14,699Voir sur GitHub↗

    MoviePy is a Python video editing library and automated video processor designed for programmatically cutting, concatenating, and manipulating video and audio files. It serves as a non-linear video editor and an interface for FFmpeg to handle the reading, writing, and conversion of diverse media formats and codecs. The library enables automated video composition through the layering of multiple video and audio streams using transparency and coordinate-based positioning. It supports dynamic content generation by inserting text overlays and performing custom video frame processing where raw fra

    Provides a modular pipeline where video frames are processed through a series of pluggable functions.

    Pythonanimationgifhacktoberfest
    Voir sur GitHub↗14,699
  • zalandoresearch/flairAvatar de zalandoresearch

    zalandoresearch/flair

    14,378Voir sur GitHub↗

    Flair is a natural language processing framework for training and applying models for sequence labeling and text classification. It provides a system for generating word embeddings and identifying semantic entities within text. The framework includes a dedicated system for zero and few-shot learning, enabling text classification and entity extraction using minimal training examples by leveraging pre-trained knowledge. Its capabilities cover named entity recognition, sentiment analysis, and the training of specialized models using custom datasets. It also includes tooling for the visual highl

    Allows users to swap different embedding schemes and labeling heads while maintaining a consistent input and output flow.

    Python
    Voir sur GitHub↗14,378
  • microsoft/ai-eduAvatar de microsoft

    microsoft/ai-edu

    14,065Voir sur GitHub↗

    ai-edu is a comprehensive AI education curriculum and machine learning courseware collection. It provides theoretical tutorials, deep learning lab exercises, and project blueprints designed to teach artificial intelligence fundamentals through a combination of study and practical implementation. The project focuses on a learning-by-doing approach, guiding users from Python programming and neural network basics to advanced topics. It includes specialized instructional content on distributed AI training, MLOps educational guides for model quantization and pruning, and detailed frameworks for im

    Demonstrates how to construct deep learning workflows by assembling reusable neural network modules and configuration files.

    HTML
    Voir sur GitHub↗14,065
  • netease-youdao/qanythingAvatar de netease-youdao

    netease-youdao/QAnything

    14,020Voir sur GitHub↗

    QAnything is a retrieval-augmented generation application framework and self-hosted AI interface. It functions as a system that combines a vector database knowledge base, a document parsing service, and a hybrid search engine to generate answers based on private user data. The project features a modular pipeline architecture that allows users to independently replace components such as parsers, embedding models, and reranking engines. It supports local-first model deployment and offline operation to ensure data privacy, and includes a two-stage retrieval pipeline that merges dense vector embe

    Features a decoupled architecture allowing independent replacement of parsers, embedding models, and LLMs via configuration.

    Python
    Voir sur GitHub↗14,020
  • cpfl/autowareAvatar de CPFL

    CPFL/Autoware

    11,716Voir sur GitHub↗

    Autoware is a modular autonomous driving stack and open-source platform for advanced driver assistance systems. It functions as an integrated operating environment that manages the full pipeline from sensor data processing to vehicle actuation, utilizing the ROS 2 robotics framework for distributed communication and hardware abstraction. The system provides a comprehensive software architecture to enable autonomous driving across various vehicle platforms. It coordinates perception, planning, and control systems to operate vehicles without human intervention. The platform covers several core

    Processes raw sensor data through discrete stages of detection and tracking to build a consistent world model.

    Dockerfile
    Voir sur GitHub↗11,716
  • pointcloudlibrary/pclAvatar de PointCloudLibrary

    PointCloudLibrary/pcl

    11,028Voir sur GitHub↗

    The Point Cloud Library is a collection of C++ algorithms designed for filtering, registering, and analyzing large-scale 3D spatial datasets. It provides a framework for 3D point cloud processing, incorporating tools for spatial data filtering and geometric feature estimation. The library includes specialized systems for aligning multiple spatial datasets into a single unified coordinate system and a rendering engine for the visual inspection and analysis of processed point cloud data. It also features tools for calculating spatial descriptors to identify structural patterns and shapes within

    Connects discrete filtering and estimation stages into a modular linear sequence for data processing.

    C++c-plus-pluscomputer-visioncpp
    Voir sur GitHub↗11,028
  • kedro-org/kedroAvatar de kedro-org

    kedro-org/kedro

    10,889Voir sur GitHub↗

    Kedro is a data science pipeline framework and orchestration tool designed to build reproducible and modular data engineering workflows. It functions as an MLOps project template and Python data workflow tool that enforces software engineering best practices to move projects from prototype to production. The system distinguishes itself through a centralized data catalog manager that abstracts data access and versioning across various file formats and cloud storage systems. It further separates processing logic from data access via a lazy-loading data registry and provides a standardized proje

    Supports the design of isolated, reusable pipeline components that can be packaged and shared across projects.

    Python
    Voir sur GitHub↗10,889
  • quantumblacklabs/kedroAvatar de quantumblacklabs

    quantumblacklabs/kedro

    10,889Voir sur GitHub↗

    Kedro is a data science pipeline framework and production toolbox designed to build reproducible, modular workflows using software engineering best practices. It functions as a data engineering orchestrator and catalog manager, bridging the gap between interactive analysis and maintainable production pipelines. The framework distinguishes itself by using a data catalog to decouple data access from processing logic and providing tools to transition analysis from interactive notebooks into structured workflows. It includes a workflow visualization tool that generates visual maps of data pipelin

    Isolates data processing logic into independent, swappable functional units to separate business logic from the execution framework.

    Python
    Voir sur GitHub↗10,889
  • projectdiscovery/httpxAvatar de projectdiscovery

    projectdiscovery/httpx

    10,053Voir sur GitHub↗

    httpx is a suite of tools and libraries for HTTP reconnaissance, infrastructure discovery, and DNS resolution. It functions as a command line toolkit for extracting metadata and status codes from HTTP targets and CIDR ranges, as well as a Go library for integrating these probing capabilities into custom programs. The project distinguishes itself through specialized infrastructure profiling, using TLS fingerprinting to extract JARM hashes and certificate details. It identifies underlying components such as CDN usage, Autonomous System Numbers, and CNAMEs to map web server software and infrastr

    Implements a modular pipeline to sequentially extract status codes, page titles, and server headers from targets.

    Go
    Voir sur GitHub↗10,053
  • open-mmlab/mmsegmentationAvatar de open-mmlab

    open-mmlab/mmsegmentation

    9,860Voir sur GitHub↗

    MMSegmentation is an open-source semantic segmentation toolbox built on PyTorch that provides a modular, configurable framework for building, training, evaluating, and deploying segmentation models. At its core, it offers a config-driven pipeline that assembles training, evaluation, and inference workflows by parsing hierarchical configuration files, with a modular component registry that enables plug-and-play composition of neural network modules, optimizers, datasets, and metrics. The framework supports the full model lifecycle through a unified runner interface that controls training, testi

    Assembles training, evaluation, and inference workflows by parsing hierarchical configuration files that wire together registered components.

    Pythondeeplabv3image-segmentationmedical-image-segmentation
    Voir sur GitHub↗9,860
  • olistic/warriorjsAvatar de olistic

    olistic/warriorjs

    9,526Voir sur GitHub↗

    Warriorjs est un framework de jeu de programmation et un puzzle logique basé sur le code où les joueurs écrivent du code pour contrôler des personnages et résoudre des défis de combat stratégiques. Il sert de jeu de codage éducatif conçu pour enseigner la logique de programmation et la pensée algorithmique à travers une série de scénarios de combat. Le projet fonctionne comme un moteur de jeu extensible qui permet l'ajout de nouveaux univers, niveaux, capacités et unités via des extensions de packages indépendantes. Il fournit également des outils pour la conception de niveaux personnalisés, permettant la création de défis que d'autres joueurs peuvent résoudre. Le système gère les définitions d'entités de jeu, le positionnement spatial et la simulation d'état au tour par tour. Il exécute les niveaux de jeu via une interface terminale, calculant des scores numériques et des notes basés sur la performance de la logique fournie.

    Allows users to code autonomous behaviors for game characters to navigate environments and defeat opponents.

    TypeScript
    Voir sur GitHub↗9,526
  • modelscope/modelscopeAvatar de modelscope

    modelscope/modelscope

    8,718Voir sur GitHub↗

    ModelScope is a comprehensive machine learning platform that functions as a model hub, training framework, inference engine, and cloud development environment. It provides a centralized repository for discovering, downloading, and managing pre-trained models and datasets across multiple modalities, including natural language, vision, and speech. The platform features a unified interface for multimodal model inference and a standardized framework for fine-tuning and evaluating large-scale models. It supports distributed training to scale workloads across multiple processors and provides contai

    Offers a pluggable architecture for modifying and swapping specific stages of AI inference and training pipelines.

    Pythoncvdeep-learningmachine-learning
    Voir sur GitHub↗8,718
  • bentoml/bentomlAvatar de bentoml

    bentoml/BentoML

    8,456Voir sur GitHub↗

    BentoML is a machine learning model serving framework and GPU-accelerated inference server designed to package, deploy, and scale AI models as production-ready REST APIs. It functions as an AI model lifecycle manager and an inference graph orchestrator, enabling the chaining of multiple models and custom logic into complex pipelines for advanced task sequences. The framework distinguishes itself through a dynamic batching engine that optimizes GPU throughput and an artifact-based packaging system that bundles model weights and dependencies into immutable archives for consistent deployment. It

    Combines multiple models and custom logic into complex inference graphs and task queues.

    Pythonai-inferencedeep-learninggenerative-ai
    Voir sur GitHub↗8,456
  • deepmipt/deeppavlovAvatar de deepmipt

    deepmipt/DeepPavlov

    6,986Voir sur GitHub↗

    DeepPavlov is a deep learning conversational AI framework designed for building end-to-end dialog systems and chatbots. It functions as an NLP model training library and a pipeline system that connects multiple natural language processing models into a single operational chain. The framework provides a REST API model server to expose trained deep learning models as web endpoints. This allows conversational agents to be deployed as web services that handle incoming HTTP requests and return predictions. The system covers the full lifecycle of conversational AI development, including NLP pipeli

    Connects independent NLP components into a sequential chain via structured configuration files.

    Python
    Voir sur GitHub↗6,986
  • cocoapods/specsAvatar de CocoaPods

    CocoaPods/Specs

    6,817Voir sur GitHub↗

    Specs is a centralized package metadata repository and distribution service for the Apple platform. It serves as a public index of library specifications, enabling the discovery, resolution, and installation of third-party frameworks for iOS and macOS projects. The project provides a podspec distribution service that hosts and validates library specifications to ensure reproducible dependency resolution. It utilizes a Git-based collection of structured specifications and a REST API to manage library publishing, ownership, and versioning. The system encompasses comprehensive capabilities for

    Divides libraries into optional subspecs, allowing consumers to install only required functionality.

    Voir sur GitHub↗6,817
Préc.123Suivant
  1. Home
  2. Software Engineering & Architecture
  3. Modular Design Patterns
  4. Pipeline Component Modularization

Explorer les sous-tags

  • AI Component Pipelines3 sous-tagsArchitectural patterns for linking AI-specific processing steps into sequential or graph-based flows. **Distinct from Pipeline Component Modularization:** Focuses specifically on the sequence of AI model calls and tool interactions rather than general data processing pipelines.
  • Configuration-Driven AssemblySystems that use structured configuration files to define the sequence and connection of modular components. **Distinct from Pipeline Component Modularization:** Specifically targets the use of config files to orchestrate the pipeline, not just the modularity of the components
  • Facial Analysis PipelinesSequential processing stages specifically for face detection, alignment, and recognition. **Distinct from Pipeline Component Modularization:** Applies modular pipeline patterns to facial analysis specifically, rather than general data or software pipelines
  • Investigation PipelinesAutomated sequences for executing a multi-stage intelligence gathering workflow. **Distinct from Pipeline Component Modularization:** Focuses on the investigative sequence of verification and discovery rather than general data pipeline components
  • Multi-Model CompositionsIntegrating different types of foundation models into a single processing pipeline. **Distinct from Pipeline Component Modularization:** Specifically focuses on the composition of diverse AI models rather than general data pipeline components.
  • Perception Pipelines1 sous-tagModular sequences of data processing stages specifically for environmental sensing and world modeling. **Distinct from Pipeline Component Modularization:** Distinct from Pipeline Component Modularization: specifically targets the perception-to-world-model pipeline in robotics rather than general data processing.
  • Physics Pipeline PluginsModular components for extending physics simulation pipelines, such as custom collision backends. **Distinct from Pipeline Component Modularization:** Focuses specifically on plugins for physics simulation backends rather than general data pipeline modularization.
  • Prompt Segment AssemblersLogic for concatenating independent data-fetching modules into a final shell prompt string. **Distinct from Pipeline Component Modularization:** Distinct from general pipeline modularization by focusing specifically on the assembly of UI segments for a shell prompt.
  • Security Scanning PipelinesModular architectures specifically designed for sequencing independent security checks on text data. **Distinct from Pipeline Component Modularization:** Specializes general pipeline component modularization for security-specific risk mitigation tasks.
  • Specification ModularizationDividing a library specification into optional sub-components for granular installation. **Distinct from Pipeline Component Modularization:** Distinct from Pipeline Component Modularization: focuses on optional library subsets in a manifest, not data pipeline stages.
  • Swappable Embedding and Labeling HeadsModular interfaces for exchanging embedding schemes and prediction heads while maintaining consistent data flow. **Distinct from Pipeline Component Modularization:** Focuses on NLP-specific modularity (embeddings/heads) rather than general data pipeline steps.
  • Visual Model PipelinesModular pipelines specifically designed for processing and manipulating visual data using AI models. **Distinct from Pipeline Component Modularization:** Focuses on the visual AI model chain rather than general data ingestion or software modularity.