Search Results for "gpu" - Page 6
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Showing 229 open source projects for "gpu"
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Darknet
Convolutional Neural Networks
...Darknet is lightweight, fast, and easy to compile, making it suitable for research and production use. The repository provides pre-trained models, configuration files, and tools for training custom object detection models. With GPU acceleration via CUDA and OpenCV integration, it achieves high performance in image recognition tasks. Its simplicity, combined with powerful capabilities, has made Darknet one of the most influential projects in the computer vision community. -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the... -
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Ion
Portable suite of libraries and tools for building client applications
Ion is a modular C++ toolkit for building high-performance 2D/3D graphics applications with a strong emphasis on portability, correctness, and developer ergonomics. Rather than a monolithic engine, it offers focused libraries—math, image, GPU resource management, shader utilities, remote inspection, and platform abstractions—that you can adopt à la carte. The rendering layer wraps modern OpenGL/OpenGL ES concepts with a carefully layered API that tracks object lifetimes, deduplicates resources, and enables safe multithreaded recording of draw calls. Asset utilities handle image formats, texture compression, and color management so pipelines can stay consistent across desktop and mobile GPUs. ... -
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SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
...AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. -
Desktop and Mobile Device Management SoftwareDesktop Central is a unified endpoint management (UEM) solution that helps in managing servers, laptops, desktops, smartphones, and tablets from a central location.
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Deep Daze
Simple command line tool for text to image generation
Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text. Simply specify the path to the image you wish to use, and... -
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GroIMP
Growth-grammar related Interactive Modelling Platform
Important: Groimp migrates to Gitlab. You can find the latest code at "https://gitlab.com/grogra/groimp/". The version on Sourceforge will not be updated anymore. The modelling platform GroIMP is designed as an integrated platform which incorporates modelling, visualisation and interaction. It exhibits several features which makes itself suitable for the field of biological or ALife modelling: The “modelling backbone” consists in the language XL. It is fully integrated, e.g., the... -
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glfx.js
An image effects library for JavaScript using WebGL
glfx.js is a JavaScript image-effects library that uses WebGL to apply real-time filters and transformations directly in the browser. It exposes a simple API where images are uploaded into GPU textures, processed with shader-based filters, and rendered to a WebGL canvas. Because the work is done on the GPU, many effects that would be too slow in pure JavaScript (like complex blurs, lens effects, or tilt-shift) can run interactively, even on large images. The library is structured around three components: textures (image sources), filters (shader pipelines), and canvases (render targets), making it easy to compose multiple effects. ... -
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Big Sleep
A simple command line tool for text to image generation
...Ryan Murdock has done it again, combining OpenAI's CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU. You will be able to have the GAN dream-up images using natural language with a one-line command in the terminal. User-made notebook with bug fixes and added features, like google drive integration. Images will be saved to wherever the command is invoked. If you have enough memory, you can also try using a bigger vision model released by OpenAI for improved generations. ... -
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Flux3D.jl
3D computer vision library in Julia
...This package utilizes Flux.jl and Zygote.jl as its building blocks for training 3D vision models and for supporting differentiation. This package also have support of CUDA GPU acceleration with CUDA.jl. -
Free and Open Source HR SoftwareGive your HR team the tools they need to streamline administrative tasks, support employees, and make informed decisions with the OrangeHRM free and open source HR software.
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MACE
Deep learning inference framework optimized for mobile platforms
...Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs. UI responsiveness guarantee is sometimes obligatory when running a model. Mechanism like automatically breaking OpenCL kernel into small units is introduced to allow better preemption for the UI rendering task. Graph level memory allocation optimization and buffer reuse are supported. The core library tries to keep minimum external dependencies to keep the library footprint small. -
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SVoice (Speech Voice Separation)
We provide a PyTorch implementation of the paper Voice Separation
SVoice is a PyTorch-based implementation of Facebook Research’s study on speaker voice separation as described in the paper “Voice Separation with an Unknown Number of Multiple Speakers.” This project presents a deep learning framework capable of separating mixed audio sequences where several people speak simultaneously, without prior knowledge of how many speakers are present. The model employs gated neural networks with recurrent processing blocks that disentangle voices over multiple... -
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feathersui-starling
User interface components for Starling Framework, ActionScript 3
Feathers UI (Starling edition) is a lightweight, open-source library of user interface components designed specifically for use with the Starling Framework. It allows ActionScript developers to build GPU-accelerated interfaces for games and applications that run on desktop and mobile platforms. With a focus on performance and flexibility, Feathers UI includes buttons, sliders, lists, navigators, and layout containers optimized for Starling's rendering pipeline. -
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Detectron2
Next-generation platform for object detection and segmentation
...Models can be exported to TorchScript format or Caffe2 format for deployment. With a new, more modular design, Detectron2 is flexible and extensible, and able to provide fast training on single or multiple GPU servers. Detectron2 includes high-quality implementations of state-of-the-art object detection. -
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TensorNetwork
A library for easy and efficient manipulation of tensor networks
TensorNetwork is a high-level library for building and contracting tensor networks—graphical factorizations of large tensors that underpin many algorithms in physics and machine learning. It abstracts networks as nodes and edges, then compiles efficient contraction orders across multiple numeric backends so users can focus on model structure rather than index bookkeeping. Common network families (MPS/TT, PEPS, MERA, tree networks) are expressed with concise APIs that encourage... -
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Curv
A language for making art using mathematics
Curv is a programming language for creating art using mathematics. It's a 2D and 3D geometric modelling tool that supports full colour, animation and 3D printing. Curv is a simple, powerful, dynamically typed, pure functional programming language. Curv is easy to use for beginners. It has a standard library of predefined geometric shapes, plus operators for transforming and combining shapes. These can be plugged together like Lego to make 2D and 3D models. Coloured shapes are represented... -
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Spleeter
Deezer source separation library including pretrained models
...It makes it easy to train music source separation models (assuming you have a dataset of isolated sources), and provides already trained state of the art models for performing various flavours of separation. 2 stems and 4 stems models have state of the art performances on the musdb dataset. Spleeter is also very fast as it can perform separation of audio files to 4 stems 100x faster than real-time when run on a GPU. We designed Spleeter so you can use it straight from command line as well as directly in your own development pipeline as a Python library. It can be installed with Conda, with pip or be used with Docker. -
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TRFL
TensorFlow Reinforcement Learning
...The library is designed to integrate seamlessly with TensorFlow, allowing users to define differentiable RL objectives and train models using standard optimization routines. TRFL supports both CPU and GPU TensorFlow environments, though TensorFlow itself must be installed separately. It exposes clean, modular APIs for various RL methods including Q-learning, policy gradient, and actor-critic algorithms, among others. Each function returns not only the computed loss tensor but also a detailed structure containing auxiliary information like TD errors and targets. -
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rspirv
Rust implementation of SPIR-V module processing functionalities
...It’s part of the gfx-rs ecosystem, a suite of graphics tools aiming to provide cross-platform rendering capabilities in Rust. rspirv enables manipulation and inspection of SPIR-V modules, which is useful in shader compilers, graphics drivers, and development tools for low-level GPU programming. The library strictly follows the SPIR-V specification and is used in projects that need to generate, analyze, or modify shaders dynamically. -
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Intel compute-runtime
Intel® Graphics Compute Runtime
The Intel(R) Graphics Compute Runtime for oneAPI Level Zero and OpenCL(TM) Driver is an open source project providing compute API support (Level Zero, OpenCL) for Intel graphics hardware architectures (HD Graphics, Xe). -
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Arraymancer
A fast, ergonomic and portable tensor library in Nim
Arraymancer is a tensor and deep learning library for the Nim programming language, designed for high-performance numerical computations and machine learning applications. -
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SageMaker TensorFlow Serving Container
A TensorFlow Serving solution for use in SageMaker
...The Docker images are built from the Dockerfiles in docker/. The Dockerfiles are grouped based on the version of TensorFlow Serving they support. Each supported processor type (e.g. "cpu", "gpu", "ei") has a different Dockerfile in each group. If your are testing locally, building the image is enough. But if you want to your updated image in SageMaker, you need to publish it to an ECR repository in your account. You can also run your container locally in Docker to test different models and input inference requests by hand. -
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Minkowski Engine
Auto-diff neural network library for high-dimensional sparse tensors
The Minkowski Engine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unspooling, and broadcasting operations for sparse tensors. The Minkowski Engine supports various functions that can be built on a sparse tensor. We list a few popular network architectures and applications here. To run the examples, please install the package and run the command in the package root directory. Compressing a neural network to... -
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TinyGL
The penultimate portable graphics library
tinygl is a lightweight, software-based OpenGL implementation originally written by Fabrice Bellard and modified here for modern learning and development purposes. It implements a subset of OpenGL 1.x features and provides a minimal yet functional rendering pipeline with no reliance on graphics hardware. tinygl is an ideal resource for educational purposes, embedded development, or rendering in software-only environments. Its simplicity and compact codebase allow developers to study how 3D... -
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YOLO ROS
YOLO ROS: Real-Time Object Detection for ROS
...Darknet on the CPU is fast (approximately 1.5 seconds on an Intel Core i7-6700HQ CPU @ 2.60GHz × 8) but it's like 500 times faster on GPU! You'll have to have an Nvidia GPU and you'll have to install CUDA. The CMakeLists.txt file automatically detects if you have CUDA installed or not. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. -
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TensorFlow MacOS
TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute
...It shipped ready-made Python 3.8 wheels and install scripts so developers could quickly get an accelerated stack running without building from source. As TensorFlow added a Metal PluggableDevice path, the project directed users toward using Apple’s tensorflow-metal to get GPU acceleration on Mac directly through Metal. The README and releases emphasized this was a macOS-optimized distribution, not an upstream fork of TensorFlow, and it cataloged common installation notes and issues encountered by early Apple Silicon users. In practice, the repo served as a bridge during the transition to native Mac acceleration, making modern TensorFlow viable on Apple hardware while upstream support matured.
