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438 个仓库

Awesome GitHub RepositoriesDeployment & Serving

Explore 438 awesome GitHub repositories matching artificial intelligence & ml · Deployment & Serving. Refine with filters or upvote what's useful.

Awesome Deployment & Serving GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • awesome-selfhosted/awesome-selfhostedawesome-selfhosted 的头像

    awesome-selfhosted/awesome-selfhosted

    299,516在 GitHub 上查看↗

    这是一个由社区策划的开源软件目录,专为在私有服务器环境和家庭实验室中部署而设计。它作为发现主流云服务独立自托管替代方案的综合资源,使用户能够保持对数字基础设施的完全数据所有权和控制权。 该目录通过层级分类法构建,将庞大的应用程序集合组织成逻辑类别,范围从媒体管理和数据分析到私有通信和团队生产力工具。它通过协作同行评审流程脱颖而出,社区成员验证每个提交的质量和相关性,以确保目录保持准确和可靠。 该项目涵盖了广泛的能力领域,包括基础设施自动化、基于容器的服务部署和声明式配置管理。这些工具协助用户维护可复现的服务器环境,并管理私有硬件上的复杂服务依赖。 该目录作为版本控制仓库进行维护,确保所有更新和社区驱动的变更都是可追踪且透明的。

    Runs large language models directly on private infrastructure to generate content without relying on external cloud services.

    awesomeawesome-listcloud
    在 GitHub 上查看↗299,516
  • tensorflow/tensorflowtensorflow 的头像

    tensorflow/tensorflow

    195,697在 GitHub 上查看↗

    TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The system provides high-level interfaces for defining neural network architectures, alongside a robust engine for managing multidimensional array structures and tensor mathematics. The framework distinguishes itself through a scalable distributed runtime that orchestrates workloads acr

    Standardizes the toolchain for serializing, optimizing, and serving machine learning models within high-performance production environments.

    C++deep-learningdeep-neural-networksdistributed
    在 GitHub 上查看↗195,697
  • jmorganca/ollamajmorganca 的头像

    jmorganca/ollama

    174,350在 GitHub 上查看↗

    Ollama is a cross-platform runtime for managing, serving, and executing large language models on local hardware. It functions as a model manager and orchestrator that allows for the downloading, updating, and organization of model weights and configurations to ensure private and offline inference. The system provides a local inference API and a RESTful interface for programmatic model lifecycle management and text generation. It utilizes a compiled C++ backend to handle tensor operations and memory management. To support various hardware configurations, the runtime employs dynamic GPU offloa

    Hosts models locally to provide low-latency predictions via a standard network API.

    Go
    在 GitHub 上查看↗174,350
  • automatic1111/stable-diffusion-webuiAUTOMATIC1111 的头像

    AUTOMATIC1111/stable-diffusion-webui

    163,743在 GitHub 上查看↗

    Stable Diffusion Web UI is a browser-based interface designed for managing text-to-image generation tasks. It provides a centralized dashboard for controlling generative processes, including native support for multi-stage model architectures to facilitate high-quality image refinement. The platform distinguishes itself through granular control over the generation process, offering tools for precise parameter management and advanced prompt engineering. Users can customize generation styles and capabilities by integrating external model-extension formats, such as textual inversions, low-rank ad

    Walks through the configuration steps required to run the application within the Windows Subsystem for Linux.

    Pythonaiai-artdeep-learning
    在 GitHub 上查看↗163,743
  • huggingface/pytorch-pretrained-berthuggingface 的头像

    huggingface/pytorch-pretrained-BERT

    161,658在 GitHub 上查看↗

    This project is a PyTorch transformer model library and pre-trained model framework. It serves as a deep learning model hub and multimodal inference engine, providing a centralized system for loading, executing, and fine-tuning state-of-the-art model checkpoints. The library focuses on multimodal machine learning, enabling predictions across text, vision, and audio data. It provides specialized capabilities for model framework interoperability, allowing the conversion of weights and definitions between different deep learning libraries. The platform covers the full model lifecycle, including

    Ships a multimodal inference engine capable of processing and generating outputs from text, image, and audio data.

    Python
    在 GitHub 上查看↗161,658
  • huggingface/transformershuggingface 的头像

    huggingface/transformers

    161,630在 GitHub 上查看↗

    Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and

    Uploads model weights and configuration files directly to remote repositories using built-in push utilities.

    Pythonaudiodeep-learningdeepseek
    在 GitHub 上查看↗161,630
  • comfy-org/comfyuiComfy-Org 的头像

    Comfy-Org/ComfyUI

    117,227在 GitHub 上查看↗

    ComfyUI is a node-based generative AI orchestration engine designed for constructing, testing, and executing complex image and video synthesis pipelines. By utilizing a directed acyclic graph execution model, the platform allows users to build reproducible workflows through modular, interconnected processing blocks without requiring manual code implementation. It serves as both a local environment for high-performance model inference and a production-ready server for deploying generative capabilities. The platform distinguishes itself through its focus on workflow portability and extensibilit

    Serves visual, node-based generative pipelines as programmable API endpoints for integration into external software.

    Pythonaicomfycomfyui
    在 GitHub 上查看↗117,227
  • ggerganov/llama.cppggerganov 的头像

    ggerganov/llama.cpp

    116,912在 GitHub 上查看↗

    llama.cpp is a high-performance C++ inference engine and runtime for executing large language models locally across various hardware architectures. It provides the core components for local model execution, including a dedicated model quantizer for compressing weights into the GGUF format and a system for generating text embeddings for semantic search. The project distinguishes itself through specialized memory and execution optimizations, such as block-wise weight quantization to reduce memory footprints and memory-mapped model loading. It supports structured text generation by using formal

    Executes generative AI models directly on local hardware to ensure privacy and reduce latency.

    C++
    在 GitHub 上查看↗116,912
  • ggml-org/llama.cppggml-org 的头像

    ggml-org/llama.cpp

    116,799在 GitHub 上查看↗

    Llama.cpp is an inference engine designed for the local execution of text-based and multimodal language models on consumer hardware. It provides a core environment for running models that process both text and image inputs, utilizing hardware-accelerated backends to optimize performance across diverse CPU and GPU architectures. The project distinguishes itself by offering a lightweight HTTP server that adheres to standard API specifications, enabling chat completion, embeddings, and reranking services. It includes a suite of tools for model quantization and conversion, which reduces memory us

    Executes large language models locally on standard consumer hardware with high performance.

    C++ggml
    在 GitHub 上查看↗116,799
  • deepseek-ai/deepseek-v3deepseek-ai 的头像

    deepseek-ai/DeepSeek-V3

    103,753在 GitHub 上查看↗

    DeepSeek-V3 is a large language model that provides comprehensive resources for model utilization, including technical specifications, pre-trained weights, and evaluation benchmarks. The project details the core transformer architecture, including parameter counts and multi-token prediction modules, while supporting native 8-bit floating-point quantization. The repository offers extensive support for local and distributed inference through integration with multiple frameworks and engines. It includes documentation for deploying the model across various hardware configurations, such as GPUs an

    Downloadable parameter files and technical configurations enable direct integration of the pre-trained model into custom environments.

    Python
    在 GitHub 上查看↗103,753
  • browser-use/browser-usebrowser-use 的头像

    browser-use/browser-use

    100,229在 GitHub 上查看↗

    Browser-use is a framework for building autonomous agents that navigate, interact with, and extract data from web interfaces using natural language instructions. By acting as an orchestration layer between large language models and browser automation protocols, it enables the execution of complex, multi-step workflows without relying on brittle selectors. The system functions as a headless browser controller, providing a programmatic interface to manage browser instances and execute granular interactions. The project distinguishes itself through its ability to translate high-level intent into

    Adjusts operational behavior and inference parameters for Llama models to optimize their performance in web-based reasoning tasks.

    Pythonai-agentsai-toolsbrowser-automation
    在 GitHub 上查看↗100,229
  • hacksider/deep-live-camhacksider 的头像

    hacksider/Deep-Live-Cam

    93,878在 GitHub 上查看↗

    Deep-Live-Cam is a generative video transformation tool designed for real-time facial manipulation and cinematic enhancement. It functions as a local-first AI runtime, performing all media processing directly on the user's hardware to ensure complete data privacy without external network dependencies. By utilizing a high-performance processing pipeline, the application enables live face swapping and interactive video modifications during active streaming sessions or on pre-recorded media. The system distinguishes itself through a hardware-abstraction execution layer that dynamically routes co

    Accelerates model execution on local hardware to reduce inference latency during video processing.

    Pythonaiai-deep-fakeai-face
    在 GitHub 上查看↗93,878
  • zed-industries/zedzed-industries 的头像

    zed-industries/zed

    85,338在 GitHub 上查看↗

    Zed is an AI-native, high-performance code editor designed for extreme responsiveness and keyboard-centric workflows. It functions as an extensible text processing workspace that integrates autonomous agents and predictive models directly into the development environment to automate complex engineering tasks, refactoring, and code generation. The editor distinguishes itself through a GPU-accelerated rendering pipeline and an asynchronous multi-threaded architecture that ensures low-latency interaction even with large-scale projects. It features built-in support for real-time, multi-user colla

    Runs machine learning models on local hardware to ensure data privacy and reduce latency for AI-assisted coding tasks.

    Rustgpuirust-langtext-editor
    在 GitHub 上查看↗85,338
  • vllm-project/vllmvllm-project 的头像

    vllm-project/vllm

    83,048在 GitHub 上查看↗

    vLLM is a high-throughput inference engine designed for the efficient serving and execution of large language models. It functions as a production-ready distributed model server, providing standard API protocols for online serving while also supporting offline batch processing. The system is built to maximize token generation speed and memory efficiency, enabling both large-scale cloud deployments and local execution on personal hardware. The project distinguishes itself through advanced memory management and request scheduling techniques, most notably its use of non-contiguous key-value cach

    Scales large language model inference to handle high volumes of concurrent requests with minimal latency.

    Pythonamdblackwellcuda
    在 GitHub 上查看↗83,048
  • infiniflow/ragflowinfiniflow 的头像

    infiniflow/ragflow

    82,922在 GitHub 上查看↗

    This project is a comprehensive retrieval-augmented generation platform designed for building, managing, and deploying knowledge-based AI applications. It provides a unified environment for organizing datasets, configuring conversational chat assistants, and developing autonomous agents that execute multi-step reasoning workflows. By integrating document intelligence with advanced retrieval pipelines, the platform enables the creation of grounded, verifiable responses supported by traceable citations. The platform distinguishes itself through deep document understanding and sophisticated know

    Processes unstructured data using deep document understanding to extract structured knowledge for high-quality information retrieval.

    Pythonagentagenticagentic-ai
    在 GitHub 上查看↗82,922
  • paddlepaddle/paddleocrPaddlePaddle 的头像

    PaddlePaddle/PaddleOCR

    82,412在 GitHub 上查看↗

    PaddleOCR is a comprehensive optical character recognition framework designed for detecting and transcribing text from images and documents into structured, machine-readable formats. It provides a modular computer vision pipeline that decouples image preprocessing, text detection, and character recognition into independent, configurable stages. This architecture supports automated document digitization and multilingual text recognition, capable of identifying text in over one hundred languages across diverse environments ranging from scanned documents to industrial scenes. The framework disti

    Converts trained models into the universal format for cross-engine compatibility and deployment flexibility.

    Pythonai4sciencechineseocrdocument-parsing
    在 GitHub 上查看↗82,412
  • mlabonne/llm-coursemlabonne 的头像

    mlabonne/llm-course

    80,178在 GitHub 上查看↗

    This project is a comprehensive educational curriculum and engineering handbook focused on the lifecycle of large language models. It serves as a structured knowledge base for machine learning practitioners, covering the fundamental mathematical and architectural principles of transformer-based sequence modeling, as well as the practical implementation of supervised instruction fine-tuning and preference-based model alignment. The repository distinguishes itself by providing a deep dive into advanced model composition and optimization techniques. It details methodologies for weight-space mode

    Implements efficient attention mechanisms and optimization strategies to maximize inference throughput.

    courselarge-language-modelsllm
    在 GitHub 上查看↗80,178
  • hoppscotch/hoppscotchhoppscotch 的头像

    hoppscotch/hoppscotch

    79,618在 GitHub 上查看↗

    Hoppscotch is an open-source API development ecosystem designed for building, testing, and debugging REST, GraphQL, and real-time APIs. It provides a unified platform that functions across web browsers, desktop applications, and command-line interfaces, allowing developers to manage the entire API lifecycle from a single environment. The platform distinguishes itself through a highly interactive, command-driven interface that utilizes a global spotlight palette and keyboard shortcuts to streamline complex workflows. It supports advanced request manipulation and validation by executing JavaScr

    Configures AI-driven assistance to generate payloads and automate test script creation.

    TypeScriptapiapi-clientapi-rest
    在 GitHub 上查看↗79,618
  • cdr/code-servercdr 的头像

    cdr/code-server

    78,072在 GitHub 上查看↗

    code-server is a browser-based integrated development environment that allows a VS Code compatible editor to run on a remote server. It functions as a self-hosted code editor, enabling users to access a persistent development workspace from any web browser without requiring local software installation. The project focuses on remote development environment hosting, moving heavy compilation and processing tasks to remote hardware to offload resources. This architecture enables cloud development environments that standardize workspaces and preserve local battery life by shifting computation to a

    Offloads intensive processing to remote hardware to preserve local battery and increase performance.

    TypeScript
    在 GitHub 上查看↗78,072
  • nomic-ai/gpt4allnomic-ai 的头像

    nomic-ai/gpt4all

    77,375在 GitHub 上查看↗

    GPT4All is a cross-platform runtime environment designed to execute large language models directly on local consumer hardware. By leveraging an optimized C++ inference backend, it enables private, offline AI interactions without requiring an internet connection or external cloud services. The project provides a comprehensive ecosystem for managing the entire model lifecycle, including discovery, downloading, and configuration of local weights. What distinguishes the platform is its integrated retrieval-augmented generation engine, which allows users to index local documents into semantic vect

    Indexes and queries local files using semantic search to provide context-aware assistance without external data exposure.

    C++ai-chatllm-inference
    在 GitHub 上查看↗77,375
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