Search Results for "gpu max performance" - Page 15
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Showing 458 open source projects for "gpu max performance"
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BytePS
A high performance and generic framework for distributed DNN training
BytePS is a high-performance and generally distributed training framework. It supports TensorFlow, Keras, PyTorch, and MXNet, and can run on either TCP or RDMA networks. BytePS outperforms existing open-sourced distributed training frameworks by a large margin. For example, on BERT-large training, BytePS can achieve ~90% scaling efficiency with 256 GPUs (see below), which is much higher than Horovod+NCCL. -
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Alive Kernel 10 Revolution Alive Kernel 9 (Discontinue) :(( I think soon i will make for pie again... •Feature On Alive Kernel• -Based on caf kernel linux Stable -Merged with new tag caf latest -OC to 2.2Ghz -Battre Friedly -Better Performance -Add more governor ~Nightmare ~Darknessv5 ~Alucard ~and more -Undervolt Cpu Gpu -80mV -Add Cpu Bost Control and More Control -Add Cpu hotplug -Add Gpu boost -Add New battery Calculation -Add Fast Charging and On/Off Charging -Add Sound Control -Add Kcal Support and more option -Add I/O Tweak and more scheduler -Lmk Optimize (4/6Gb Ram varian) -Add Boeffla Wakelocks -Add WireGuard -Optimize Zram for battery saving -Tcp Algorithm Optimize -Much More
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Bangla TTS
Bangla text to speech synthesis in python
Bangla text to speech Multilingual (Bangla, English) real-time ([almost] in a GPU) speech synthesis library. Installation -------------------------------------- * Install Anaconda * conda create -n new_virtual_env python==3.6.8 * conda activate new_virtual_env * pip install -r requirements.txt * While running for the first time, keep your internet connection on to download the weights of the speech synthesis models (>500 MB) * For... -
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imgaug
Image augmentation for machine learning experiments
imgaug is a library for image augmentation in machine learning experiments. It supports a wide range of augmentation techniques, allows to easily combine these and to execute them in random order or on multiple CPU cores, has a simple yet powerful stochastic interface and can not only augment images but also key points/landmarks, bounding boxes, heatmaps and segmentation maps. Affine transformations, perspective transformations, contrast changes, gaussian noise, dropout of regions,... -
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anyLive
RTMP streamer, RTMP (HLS) second-on player, live streaming on demand
...The project is designed for real-time communication and live broadcasting applications, offering low-latency streaming and compatibility with standard RTMP servers and CDNs. It integrates support for multiple protocols such as RTMP, HLS, HTTP, and RTSP, enabling flexible streaming scenarios. The framework also includes features like GPU-based filters, screen sharing, custom rendering pipelines, and multi-stream playback, making it adaptable for complex media applications. With a focus on performance optimization and minimal resource usage, it serves as a practical reference for building production-ready streaming systems. -
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Hardcoder
Hardcoder is a solution which allows Android APP and Android System
...Hardcoder framework can averagely optimize the performance of Wechat by 10%-30% in terms of Wechat startup, video delivery, mini program startup, and other highly-loaded scenes. Furthermore, it could also averagely optimize the performance of Mobile QQ by 10%-50% in terms of mobile QQ startup. -
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Rain Benchmark
Benchmark to test PC performance and stability.
Benchmark to test PC performance and stability. Benchmark consists of 5 different tests that can heavily load even very productive home PCs, workstations and servers with a large number of processor cores. Benchmark has a compact size, only 20 MB and does not require installation. -
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AutoBench
This program is a benchmark site data extraction util program
This program is a program that extracts the latest CPU, GPU, Drive and RAM performance scores and rankings from benchmark sites. The Output Data is saved as a csv, xlsx and xls file. CPU information is written by model name and score. GPU information is written by model name and score. Drive information is written by model name and score. RAM information is written by model name and score. -
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XMRig AMD
Monero AMD (OpenCL) miner
XMRig is a high-performance Monero (XMR) OpenCL miner, with the official full Windows support. -
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Torchreid
Deep learning person re-identification in PyTorch
Torchreid is a library for deep-learning person re-identification, written in PyTorch and developed for our ICCV’19 project, Omni-Scale Feature Learning for Person Re-Identification. In "deep-person-reid/scripts/", we provide a unified interface to train and test a model. See "scripts/main.py" and "scripts/default_config.py" for more details. The folder "configs/" contains some predefined configs which you can use as a starting point. The code will automatically (download and) load the... -
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XMRig NVIDIA
Monero (XMR) NVIDIA miner
XMRig is high performance Monero (XMR) NVIDIA miner, with the official full Windows support. -
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maskrcnn-benchmark
Fast, modular reference implementation of Instance Segmentation
...It supports multi-GPU distributed training, mixed precision, and custom data loaders for new datasets. Built as a reference implementation, it became a foundation for the next-generation Detectron2, yet remains widely used for research needing a stable, reproducible environment. Visualization tools, model zoo checkpoints, and benchmark scripts make it easy to replicate state-of-the-art results or fine-tune models for custom tasks. -
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GPUImage 2
Framework for GPU-accelerated video and image processing
...By relying on the GPU to run these operations, performance improvements of 100X or more over CPU-bound code can be realized. This is particularly noticeable in mobile or embedded devices. On an iPhone 4S, this framework can easily process 1080p video at over 60 FPS. On a Raspberry Pi 3, it can perform Sobel edge detection on live 720p video at over 20 FPS. -
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Imogen
GPU Texture Generator
Imogen is a real-time, node-based procedural texture generation tool aimed at artists, developers, and shader enthusiasts. It allows users to build complex material textures using a graph-based interface, combining operations like blending, noise, filters, and color correction in a non-destructive workflow. Built with Vulkan and ImGui, Imogen provides immediate visual feedback and supports GPU acceleration for high-resolution texture output. It's particularly useful in game development, VFX,... -
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Effeckt.css
A Performant Transitions and Animations Library
Effeckt.css is a showcase and toolkit of high-performance UI transitions and animations for the web. It catalogs common interaction patterns—button states, list reveals, modals, off-canvas menus, and page transitions—implemented with CSS transforms and opacity for smooth, GPU-friendly motion. The project focuses on practical details such as using compositing-friendly properties, keeping DOM structures lean, and providing hooks so JavaScript can toggle classes without micromanaging animation state. ... -
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Tensorpack
A Neural Net Training Interface on TensorFlow, with focus on speed
...On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack. Scalable data-parallel multi-GPU / distributed training strategy is off-the-shelf to use. Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not offer the data processing flexibility needed in research. Tensorpack squeezes the most performance out of pure Python with various auto parallelization strategies. ... -
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DarkMagic
This is a Custom Kernel for Redmi Note 5 Pro a.k.a Whyred [PIE]
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Video Nonlocal Net
Non-local Neural Networks for Video Classification
video-nonlocal-net implements Non-local Neural Networks for video understanding, adding long-range dependency modeling to 2D/3D ConvNet backbones. Non-local blocks compute attention-like responses across all positions in space-time, allowing a feature at one frame and location to aggregate information from distant frames and regions. This formulation improves action recognition and spatiotemporal reasoning, especially for classes requiring context beyond short temporal windows. The repo... -
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Mixup-CIFAR10
mixup: Beyond Empirical Risk Minimization
mixup-cifar10 is the official PyTorch implementation of “mixup: Beyond Empirical Risk Minimization” (Zhang et al., ICLR 2018), a foundational paper introducing mixup, a simple yet powerful data augmentation technique for training deep neural networks. The core idea of mixup is to generate synthetic training examples by taking convex combinations of pairs of input samples and their labels. By interpolating both data and labels, the model learns smoother decision boundaries and becomes more... -
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RpiOptimisation
Performance optimization tool for Raspberry Pi.
Support : [Raspberry Pi 0-1-2-3] [EN] Performance optimization tool for Raspberry Pi (CPU,GPU,SD card read and write speed) by model. [FR] Outil d'optimisation des performances pour Raspberry Pi (CPU, GPU, vitesse de lecture et d'écriture de la carte SD) par modèle. -
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Buddhabrot Max
A Buddhabrot generator for large (>20000² px) renderings.
A escape time orbit fractal renderer written in C#. Consists of two separate programs, one focused on high resolution and high quality rendering ("Buddhabrot Max"), the other focused on realtime exploration / zooming ("Buddhabrot Mag"). Uses multithreading and hardware acceleration. Allows creating of huge renderings (if enough memory is available) and saving files with full 32 bit float dynamic range as OpenEXR files. You can easily add new formulas which are then runtime compiled for maximum performance. -
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xmrMiner
A CUDA based miner for Monero
An optimized Monero miner designed to maximize GPU mining efficiency. -
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Microtonal Organ
Software organ created with Max and fluidsynth.
This is a soundfont instrument for performance and experiments with intonation systems. It is set up like an organ, while any instrument available in the sf2-format can be used. A database of several hundred tunings are included, and can be expanded by the user. Sources of these tunings are described in the manual. This program is made with Max 7, and standalones are provided for Mac Osx Yosemite and High Sierra. -
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DIGITS
Deep Learning GPU training system
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. ... -
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SLFS
Simple Log Structured Filesystem for Linux
SLFS is a implementation of log-structured file system designed for flash memory based storages(SSD, SD card, eMMC, …). Like other log-structured file systems, SLFS shows good performance under random write. <Build> 1. deploy SLFS source code at your Kernel Source # tar xzf slfs.tar.gz # mv slfs KERNEL_SRC/fs/. # vi KERNEL_SRC/fs/Makefile (add following line) obj-$(CONFIG_SLFS_FS) += slfs/ # vi KERNEL_SRC/fs/Kconfig (find section “MISC_FILESYSTEMS” and add following...
