1592 Open source projects that are alternatives of or similar to Chainer

NumPy & SciPy for GPU

A Python wrapper of NVIDIA Video Loader (NVVL) with CuPy for fast video loading with Python

Tests and benchmarks for cudnn (and in the future, other nvidia libraries)

Automatic parallelization of Python/NumPy, C, and C++ codes on Linux and MacOSX

A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends

Deep learning operations reinvented (for pytorch, tensorflow, jax and others)

Script to remotely check GPU servers for free GPUs

Qualia is a deep learning framework deeply integrated with automatic differentiation and dynamic graphing with CUDA acceleration. Qualia was built from scratch.

Grokking Deep Reinforcement Learning

Komputation is a neural network framework for the Java Virtual Machine written in Kotlin and CUDA C.

The purpose of this tutorial is to learn how to install and prepare TensorFlow framework to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch

Library for faster pinned CPU <-> GPU transfer in Pytorch

Fork of CUDA GPU memtest 👓

GPU Fluid Simulation with Volumetric Rendering

Implementation of Macklin, Miles, and Matthias Müller. "Position based fluids.". Visual Studio 2015 + CUDA 8.0

an implementation of Revisiting Adaptive Convolutions for Video Frame Interpolation using PyTorch

continuous energy monte carlo neutron transport in general geometries on GPUs

[内测中]前向式Python环境快捷封装工具，快速将Python打包为EXE并添加CUDA、NoAVX等支持。

A domain-specific language and compiler for image processing

PopSift is an implementation of the SIFT algorithm in CUDA.

🧠 A tool that makes AI easier.

Simple utilities to enable code reuse and portability between CUDA C/C++ and standard C/C++.

a reimplementation of LiteFlowNet in PyTorch that matches the official Caffe version

This repository consists for gpu bootcamp material for HPC and AI

Chainer implementation of waifu2x

This is a list of useful libraries and resources for CUDA development.

A fast Clojure Tensor & Deep Learning library

FLAME GPU 2 is a GPU accelerated agent based modelling framework for C++ and Python

monolish: MONOlithic LInear equation Solvers for Highly-parallel architecture

Archlinux PKGBUILDs for Data Science, Machine Learning, Deep Learning, NLP and Computer Vision

A stream processing framework for high-throughput applications.

an implementation of parallel linear BVH (LBVH) on GPU

An efficient C++17 GPU numerical computing library with Python-like syntax

Lightning fast & tiny C++/CUDA neural network framework

BriefMatch real-time GPU optical flow

High-Performance Multiple-Backend Renderer Based on LuisaCompute

gprMax is open source software that simulates electromagnetic wave propagation using the Finite-Difference Time-Domain (FDTD) method for numerical modelling of Ground Penetrating Radar (GPR)

Dockerfiles for OpenCV compiled with CUDA, opencv_contrib modules and Python 3 bindings

A CUDA implementation of SIFT for NVidia GPUs (1.2 ms on a GTX 1060)

A curated list of awesome Distributed Deep Learning resources.

High-performance Bayesian Data Analysis on the GPU in Clojure

Python bindings for ArrayFire: A general purpose GPU library.

Lighthouse 2 framework for real-time ray tracing

ArrayFire: a general purpose GPU library.

Thin C++-flavored wrappers for the CUDA Runtime API

The C++ parallel algorithms library.

CUDA programming in Julia.

ILGPU JIT Compiler for high-performance .Net GPU programs

Implementation of SYCL for CPUs, AMD GPUs, NVIDIA GPUs

MegEngine 是一个快速、可拓展、易于使用且支持自动求导的深度学习框架

A collection of GICP-based fast point cloud registration algorithms

Minimal runtime core of Caffe, Forward only, GPU support and Memory efficiency.

cuDF - GPU DataFrame Library

An open-source tool for sequence learning in NLP built on TensorFlow.

Open3D: A Modern Library for 3D Data Processing

Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).

ThunderGBM: Fast GBDTs and Random Forests on GPUs

stdgpu: Efficient STL-like Data Structures on the GPU

H2Oai GPU Edition

High-performance Vision library in Python. Scale your research, not boilerplate.