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166 مستودعات

Awesome GitHub RepositoriesHardware & Acceleration

Explore 166 awesome GitHub repositories matching artificial intelligence & ml · Hardware & Acceleration. Refine with filters or upvote what's useful.

Awesome Hardware & Acceleration GitHub Repositories

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  • tensorflow/tensorflowالصورة الرمزية لـ tensorflow

    tensorflow/tensorflow

    195,697عرض على GitHub↗

    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

    Executes high-performance element-wise functions, trigonometric operations, and logical reductions across multi-dimensional arrays.

    C++deep-learningdeep-neural-networksdistributed
    عرض على GitHub↗195,697
  • automatic1111/stable-diffusion-webuiالصورة الرمزية لـ AUTOMATIC1111

    AUTOMATIC1111/stable-diffusion-webui

    163,743عرض على GitHub↗

    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

    Configures hardware-specific settings to leverage NVIDIA graphics processing units for accelerated computation.

    Pythonaiai-artdeep-learning
    عرض على GitHub↗163,743
  • ggerganov/llama.cppالصورة الرمزية لـ ggerganov

    ggerganov/llama.cpp

    116,912عرض على GitHub↗

    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

    Includes a low-level C-based tensor library for efficient memory management and mathematical operations.

    C++
    عرض على GitHub↗116,912
  • ggml-org/llama.cppالصورة الرمزية لـ ggml-org

    ggml-org/llama.cpp

    116,799عرض على GitHub↗

    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

    Unifies diverse CPU and GPU architectures through a common interface to normalize model execution across heterogeneous hardware.

    C++ggml
    عرض على GitHub↗116,799
  • godotengine/godotالصورة الرمزية لـ godotengine

    godotengine/godot

    112,618عرض على GitHub↗

    Godot is a comprehensive, node-based game engine designed for building interactive 2D and 3D applications. It provides an integrated development environment that utilizes a hierarchical scene system to organize objects, propagate spatial transformations, and manage lifecycle events. The engine functions as a cross-platform development suite, allowing developers to author, test, and export software to desktop, mobile, and web environments from a single, unified codebase. The engine distinguishes itself through a modular, component-based architecture that relies on signals-based decoupling for

    Normalizes hardware-specific tasks like input, audio, and file I/O across heterogeneous deployment targets.

    C++game-developmentgame-enginegamedev
    عرض على GitHub↗112,618
  • pytorch/pytorchالصورة الرمزية لـ pytorch

    pytorch/pytorch

    100,814عرض على GitHub↗

    PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array operations across both CPU and accelerator hardware. It provides a foundational infrastructure for mathematical computation and dynamic neural network construction, utilizing a tape-based automatic differentiation system that allows for flexible, non-static graph execution. The framework is designed for deep integration with Python, enabling natural usage alongside standard scientific computing ecosystems. It distinguishes itself through a comprehensive distributed training sui

    Accelerates multi-dimensional array operations by leveraging native GPU and specialized hardware support.

    Pythonautograddeep-learninggpu
    عرض على GitHub↗100,814
  • hacksider/deep-live-camالصورة الرمزية لـ hacksider

    hacksider/Deep-Live-Cam

    93,878عرض على GitHub↗

    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

    Routes model inference tasks to hardware-specific acceleration APIs like CUDA or CoreML.

    Pythonaiai-deep-fakeai-face
    عرض على GitHub↗93,878
  • home-assistant/home-assistantالصورة الرمزية لـ home-assistant

    home-assistant/home-assistant

    87,771عرض على GitHub↗

    Home Assistant is a home automation platform and IoT device orchestrator that serves as a central hub for controlling smart devices and executing automated routines. It functions as a local smart home controller, managing device states and automation logic on a local network to provide a private alternative to cloud-based hubs. The system emphasizes privacy-focused IoT management by prioritizing local control to reduce reliance on external cloud services. It enables multi-vendor device integration, translating diverse third-party hardware signals into a unified interface for consolidated mana

    Translates diverse third-party hardware signals into a unified internal data model for consistent state management.

    Python
    عرض على GitHub↗87,771
  • home-assistant/coreالصورة الرمزية لـ home-assistant

    home-assistant/core

    87,753عرض على GitHub↗

    Home Assistant is a centralized home automation platform designed to orchestrate diverse internet-connected devices and services. It functions as a local-first control system that normalizes heterogeneous hardware protocols into a unified set of entities, attributes, and services. The core architecture relies on an event-driven state bus and a modular integration model, allowing the system to manage state changes and communicate across decoupled components through standardized interfaces. The platform distinguishes itself through a highly flexible, declarative configuration framework that all

    Normalizes heterogeneous hardware protocols into a consistent set of entities, attributes, and services.

    Pythonasynciohacktoberfesthome-automation
    عرض على GitHub↗87,753
  • d2l-ai/d2l-zhالصورة الرمزية لـ d2l-ai

    d2l-ai/d2l-zh

    78,493عرض على GitHub↗

    This project is an open-source, interactive educational platform designed to teach deep learning through a comprehensive, code-first curriculum. It provides a structured learning path that covers foundational mathematics, modern neural network architectures, and practical optimization techniques, enabling practitioners to master complex artificial intelligence concepts through hands-on experimentation. The platform distinguishes itself by integrating technical explanations with executable Jupyter notebooks. This design allows readers to modify code and hyperparameters in real-time, facilitati

    Discusses optimization techniques for leveraging hardware acceleration to improve throughput in large-scale model training.

    Pythonbookchinesecomputer-vision
    عرض على GitHub↗78,493
  • pewdiepie-archdaemon/odysseusالصورة الرمزية لـ pewdiepie-archdaemon

    pewdiepie-archdaemon/odysseus

    72,184عرض على GitHub↗

    Odysseus is a self-hosted AI workspace and autonomous agent framework designed for deploying and managing large language models. It serves as a centralized platform for orchestrating agentic tasks, utilizing a model context protocol server to connect AI models to external system utilities, browser automation, and local hardware. The system distinguishes itself through a combination of retrieval-augmented generation and a RAG knowledge base, using vector stores and local embeddings to provide persistent semantic memory. It further integrates AI-driven communication management to triage email i

    Provides configuration settings to utilize NVIDIA GPUs for accelerating model inference.

    Python
    عرض على GitHub↗72,184
  • labmlai/annotated_deep_learning_paper_implementationsالصورة الرمزية لـ labmlai

    labmlai/annotated_deep_learning_paper_implementations

    66,981عرض على GitHub↗

    This project is a collection of deep learning research papers translated into annotated code. It serves as a resource for reproducing academic research, providing implementations of transformers, diffusion models, and reinforcement learning architectures. The library distinguishes itself by using a side-by-side annotation format that combines executable Python code with descriptive markdown notes. This approach provides a structured way to explain the logic of neural network papers alongside their PyTorch-based implementations. The codebase covers several major capability areas, including ge

    Uses PyTorch for fundamental tensor operations and automatic differentiation to implement deep learning linear algebra.

    Pythonattentiondeep-learningdeep-learning-tutorial
    عرض على GitHub↗66,981
  • karpathy/nanogptالصورة الرمزية لـ karpathy

    karpathy/nanoGPT

    59,730عرض على GitHub↗

    nanoGPT is a lightweight engine for training and fine-tuning transformer-based language models from scratch. It provides a minimalist codebase designed for educational exploration and rapid experimentation with neural network architectures, utilizing self-attention and feed-forward layers to process sequences and predict subsequent elements. The project distinguishes itself through a focus on high-speed data ingestion and hardware-accelerated performance. It includes a dedicated pipeline for transforming raw text into memory-mapped binary files, which enables efficient streaming during traini

    Executes high-dimensional array operations and mathematical functions essential for training deep neural networks.

    Python
    عرض على GitHub↗59,730
  • ageitgey/face_recognitionالصورة الرمزية لـ ageitgey

    ageitgey/face_recognition

    56,504عرض على GitHub↗

    This is a Python facial recognition library designed to detect, encode, and identify human faces in images and video. It functions as a biometric identification tool that converts facial features into numerical encodings to compare and match identities. The library provides a computer vision command line interface for batch processing face detection and recognition tasks across image directories. It also supports a GPU accelerated vision API that utilizes CUDA and NVIDIA hardware to increase the speed of facial analysis and identification. Its capabilities cover human face detection and faci

    Utilizes NVIDIA GPU hardware and CUDA to accelerate computationally intensive facial recognition tasks.

    Pythonface-detectionface-recognitionmachine-learning
    عرض على GitHub↗56,504
  • ggerganov/whisper.cppالصورة الرمزية لـ ggerganov

    ggerganov/whisper.cpp

    50,791عرض على GitHub↗

    whisper.cpp is a C++ implementation of the Whisper speech-to-text model, serving as a lightweight machine learning inference engine and quantized runtime. It provides high-performance automatic speech recognition and real-time audio transcription without requiring a Python environment. The project utilizes model quantization to reduce memory usage and increase inference speed on local hardware. It incorporates hardware acceleration to optimize processing speed across different processors. The system covers audio processing capabilities including voice activity detection, speaker diarization,

    Utilizes specialized hardware components and GPUs to enhance computational throughput for model inference.

    C++
    عرض على GitHub↗50,791
  • google-research/google-researchالصورة الرمزية لـ google-research

    google-research/google-research

    38,139عرض على GitHub↗

    This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

    Optimizes high-performance machine learning workloads through hardware-accelerated tensor processing.

    Jupyter Notebookaimachine-learningresearch
    عرض على GitHub↗38,139
  • exacity/deeplearningbook-chineseالصورة الرمزية لـ exacity

    exacity/deeplearningbook-chinese

    37,285عرض على GitHub↗

    This project is a comprehensive Chinese translation of a technical deep learning textbook, providing an educational resource on the theory and implementation of neural networks. It functions as a collaborative technical translation project designed to make complex academic AI literature accessible to non-English speakers. The project utilizes a community-driven translation model that integrates external suggestions and pull requests to refine linguistic accuracy and reduce bias. It employs standardized terminology mapping to ensure a uniform vocabulary throughout the translated content. To i

    Explains the application of penalties to encoding layers to ensure only a few hidden units remain active.

    TeX
    عرض على GitHub↗37,285
  • google/jaxالصورة الرمزية لـ google

    google/jax

    35,835عرض على GitHub↗

    JAX is a hardware-accelerated array library and automatic differentiation system for numerical computing. It provides a framework compatible with NumPy that extends array operations with a just-in-time compiler to transform Python functions into optimized kernels for execution on GPU and TPU accelerators. The system differentiates itself through the use of an XLA-based compiler and a single program multiple data sharding model. These capabilities allow the library to distribute large-scale computations across multiple hardware accelerators using both automatic parallelization and manual shard

    Ships a tensor library with native support for executing vectorized operations across multiple hardware accelerators.

    Python
    عرض على GitHub↗35,835
  • bvlc/caffeالصورة الرمزية لـ BVLC

    BVLC/caffe

    34,576عرض على GitHub↗

    Caffe is a high-performance deep learning framework designed for training and deploying deep neural networks. It functions as a machine learning engine and a convolutional neural network library, providing a C++ backend to accelerate computations on both GPUs and CPUs. The system includes a specialized toolset for computer vision, enabling tasks such as object detection, semantic segmentation, and large-scale image retrieval. It supports the deployment of pre-trained models for image and scene recognition, as well as the ability to fine-tune neural network weights for specialized tasks. The

    Increases throughput using specialized CPU instructions, GPU processing, and multi-node scaling.

    C++deep-learningmachine-learningvision
    عرض على GitHub↗34,576
  • huggingface/diffusersالصورة الرمزية لـ huggingface

    huggingface/diffusers

    33,872عرض على GitHub↗

    Diffusers is a PyTorch-based library and generative AI framework used to build, train, and deploy diffusion pipelines for producing multi-modal media. It provides a suite of tools for generating images, video, and audio from natural language descriptions, as well as specialized systems for text-to-image generation. The project differentiates itself through a modular architecture that separates noise schedulers, pretrained model blocks, and pipeline compositions. This structure allows for the construction of custom generation workflows and the ability to swap individual components of the diffu

    Utilizes PyTorch for high-performance tensor manipulation and hardware-accelerated mathematical operations.

    Pythondeep-learningdiffusionflux
    عرض على GitHub↗33,872
السابق123456…9التالي
  1. Home
  2. Artificial Intelligence & ML
  3. Machine Learning
  4. Infrastructure
  5. Optimization & Inference
  6. Hardware & Acceleration

استكشف الوسوم الفرعية

  • Hardware Abstraction Layers3 وسوم فرعيةMiddleware layers that provide unified interfaces to normalize and abstract heterogeneous hardware backends for software tasks.
  • Hardware Acceleration6 وسوم فرعيةUtilization of specialized hardware components to enhance computational throughput in machine learning tasks.
  • Hardware Compatibility MatricesDocumentation or tooling tracking operational support across diverse hardware backends.
  • Tensor Computing Libraries6 وسوم فرعيةLow-level libraries and utilities for tensor manipulation, memory management, and hardware-accelerated mathematical operations.