Search Results for "nvidia" - Page 2
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Showing 45 open source projects for "nvidia"
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Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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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. -
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Real-ESRGAN ncnn Vulkan
NCNN implementation of Real-ESRGAN
...It provides command-line tools for upscaling images with selected models, allowing users to specify input/output paths, scaling factors, tile sizes, and model names from a compressed model set, which is particularly helpful for larger images or automated workflows. The Vulkan backend enables fast execution on GPUs from different vendors (Intel/AMD/Nvidia) with broad support, making it suitable for non-Python environments, production systems, or performance-constrained setups. -
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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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YOLO ROS
YOLO ROS: Real-Time Object Detection for ROS
...We also provide branches that work under ROS Melodic, ROS Foxy and ROS2. 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. -
HOA SoftwareSimplify HOA management with software that handles everything from financials to communication.
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PyText
A natural language modeling framework based on PyTorch
...We use PyText at Facebook to iterate quickly on new modeling ideas and then seamlessly ship them at scale. Distributed-training support built on the new C10d backend in PyTorch 1.0. Mixed precision training support through APEX (trains faster with less GPU memory on NVIDIA Tensor Cores). Extensible components that allows easy creation of new models and tasks. -
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SageMaker Chainer Containers
Docker container for running Chainer scripts to train and host Chainer
SageMaker Chainer Containers is an open-source library for making the Chainer framework run on Amazon SageMaker. This repository also contains Dockerfiles which install this library, Chainer, and dependencies for building SageMaker Chainer images. Amazon SageMaker utilizes Docker containers to run all training jobs & inference endpoints. The Docker images are built from the Dockerfiles specified in Docker/. The Docker files are grouped based on Chainer version and separated based on Python... -
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Tiramisu
Polyhedral compiler for expressing fast and portable data algorithms
...The Tiramisu compiler is based on the polyhedral model thus it can express a large set of loop optimizations and data layout transformations. Currently, it targets (1) multicore X86 CPUs, (2) Nvidia GPUs, (3) Xilinx FPGAs (Vivado HLS) and (4) distributed machines (using MPI). It is designed to enable easy integration of code generators for new architectures. -
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DICOM Image Reader
A minimal Medical application to read and view DICOM (dcm) image file
...It is based DWV JavaScript opensource library. =============== Requirements: - Processor: Intel Pentium 4 processor or later that's SSE2 capable - RAM: 512 MB - Hard Disk: 1500 MB - Video Card: nVidia, ATI and Intel Shader Model 2 and Shader Model 3 video cards with 512 megabytes of RAM minimum - Supported OS: Windows 10, Windows 8, Windows 8.1, Windows 7 =============== Alternative software for Windows: Available on Ecwid - https://goo.gl/twBcAq =============== Android App: https://goo.gl/i4NX9K -------------- Chrome app Release Pro: https://goo.gl/p456N5 -
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Ethereum Mining NVIDIA Graph Card Ubuntu
USB flash drive ISO image for Ethereum, Zcash and Monero mining
USB flash drive ISO image for Ethereum mining with NVIDIA graphics cards and Ubuntu GNU/Linux (64-bit Intel/AMD (x86_64)). Other cryptocurrencies, such as Monero or Zcash, can also be mined. With this ISO image, you can immediately mine Ethereum (ETH). Do not spend long time searching and researching. If you do not trust me and do not want to use the image, you will find all configuration files and scripts in the files folder. -
Comet Backup - Fast, Secure Backup Software for MSPsComet is a flexible backup platform, giving you total control over your backup environment and storage destinations.
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Mocha.jl
Deep Learning framework for Julia
Mocha.jl is a deep learning framework for Julia, inspired by the C++ Caffe framework. It offers efficient implementations of gradient descent solvers and common neural network layers, supports optional unsupervised pre-training, and allows switching to a GPU backend for accelerated performance. The development of Mocha.jl happens in relative early days of Julia. Now that both Julia and the ecosystem has evolved significantly, and with some exciting new tech such as writing GPU kernels... -
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Caffe Framework
Caffe, a fast open framework for deep learning
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine... -
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Basic WebPage Builder
Drag and Drop basic webpage builder
.... ==== Official download also on Softpedia® - https://bit.ly/2AGwc7a ==== Requirements: - Processor: Intel Pentium 4 processor or later that's SSE2 capable - RAM: 512 MB - Hard Disk: 1500 MB - Video Card: nVidia, ATI and Intel Shader Model 2 and Shader Model 3 video cards with 512 megabytes of RAM minimum - Supported OS: Windows 10, Windows 8, Windows 8.1, Windows 7 -
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Caffe
A fast open framework for deep learning
...It’s got an expressive architecture that encourages application and innovation, and extensible code that’s great for active development. Caffe also offers great speed, capable of processing over 60M images per day with a single NVIDIA K40 GPU. It’s arguably one of the fastest convnet implementations around. Caffe is developed by the Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and a great community of contributors that continue to make Caffe state-of-the-art in both code and models. It’s been used in numerous projects, from startup prototypes and academic research projects, to large scale industrial applications. -
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OpenCV CUDA Binaries
OpenCV Pre-built CUDA binaries
This project is now hosted as the nuget packages : opencvcuda-release opencvcuda-debug 3 Builds now available as nuget packages : - https://www.nuget.org/packages/opencvdefault/ Package for the default Windows x64 build available on opencv.org - https://www.nuget.org/packages/opencvcontrib/ Package for Windows x64 Visual Studio 2015 for the contrib and vtk modules built with AVX, SSE & OpenGL support. - https://www.nuget.org/packages/opencvcuda-release/ -... -
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MXLib is a C++ wrapper around the Intel® Integrated Performance Primitives (IPP) library and NVidia NPP CUDA library. You can use either IPP code (or a subset of functions that do not require IPP) on the CPU side, or use NPP/CUDA on the GPU side, or use both together. The function syntax is similar to that found in MatLab and the library is designed to make it easy to port your code from MatLab to C++. The idea is to provide Scientists, Engineers, Researchers and other non full-time programmers an easy to use, high performance library of functions.
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CURRENNT
CUDA-enabled machine learning library for recurrent neural networks
CURRENNT is a machine learning library for Recurrent Neural Networks (RNNs) which uses NVIDIA graphics cards to accelerate the computations. The library implements uni- and bidirectional Long Short-Term Memory (LSTM) architectures and supports deep networks as well as very large data sets that do not fit into main memory. -
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This Eclipse plugin help you to create, edit and verify your GLSL (OpenGL Shading Language) and NVidia CG vertex and pixel shaders directly inside the Eclipse IDE. Each shader can be edit in an editor with syntax color with error/warning markers.
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This project is a Neural Network Training Library implemented on CUDA. It's compatible with the most used libraries but allows to exploit the full power of NVIDIA graphic cards. Experimental results show speed ups over 100 times against CPU libraries
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A data parallel scientific programming model. Compiles efficiently to different platforms like distributed memory (MPI), shared memory multi-processor (pthreads), Cell BE processor, Nvidia Cuda, SIMD vectorization (SSE, Altivec), and sequential C++ code.
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A simple, cross platform performance monitoring application specifically designed to be used with nVidia's instrumented driver and the NVPerfSDK to give a graphical representation of internal GPU counters. Support for non-GPU counters is also available.
