The Forge Cross-Platform Rendering Framework PC Windows, Steamdeck (native), Ray Tracing, macOS / iOS, Android, XBOX, PS4, PS5, Switch, Quest 2

Toolkit for efficient experimentation with Speech Recognition, Text2Speech and NLP

Face recognition system for ID photos

QUDA is a library for performing calculations in lattice QCD on GPUs.

Code for training py-faster-rcnn and py-R-FCN on multiple GPUs in caffe

Extract video features from raw videos using multiple GPUs. We support RAFT flow frames as well as S3D, I3D, R(2+1)D, VGGish, CLIP, and TIMM models.

<케라스 창시자에게 배우는 딥러닝 2판> 도서의 코드 저장소

The world's first CUDA implementation of Weakly-Compressible Smoothed Particle Hydrodynamics

A PyTorch implementation of the 'FaceNet' paper for training a facial recognition model with Triplet Loss using the glint360k dataset. A pre-trained model using Triplet Loss is available for download.

Multi-threaded GUI manager for mass creation of AI-generated art with support for multiple GPUs.

Efficient Distributed GPU Programming for Exascale, an SC/ISC Tutorial

multi-gpu pre-training in one machine for BERT from scratch without horovod (Data Parallelism)

Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python

GPU-ready Dockerfile to run Stability.AI stable-diffusion model v2 with a simple web interface. Includes multi-GPUs support.

Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing

Package for writing high-level code for parallel high-performance stencil computations that can be deployed on both GPUs and CPUs

POT3D: High Performance Potential Field Solver

Multi-GPU training for Keras

Almost trivial distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid

High-level C++ for Accelerator Clusters