/nvaitc-toolkit

Open source code base to showcase interoperability of CUDA-X AI software stack in multi-GPU environments and thus provide researchers a reference framework to build new projects on.

NVIDIA AI Technology Centre (NVAITC)

Toolkit

Introduction

Python codebase to showcase the interoperability of CUDA-X AI software stack in multi-GPU environments. The goal of this project is to provide researchers a reference framework to build new projects on. It requests the availability of ImageNet to demonstrate how to train a network (ResNet[18/50/101]) against a well known dataset. This codebase served as the underlying playground for the Oct 2020 NVAITC Webinar Series on Deep Learning available as a YouTube playlist.

Clone repo

git clone -b toolkit --single-branch https://github.com/nvidia/nvaitc-toolkit.git toolkit

Getting Started

Please find details and installation instructions in README.md.

cuAugment

Introduction

cuAugment is a CUDA-accelerated 1D/2D/3D/4D augmenter library that utilizes a just-in-time compiler to transform a cascade of coordinate transformation into a single monolithic kernel to avoid unnecessary accesses to global memory.

Clone repo

git clone -b cuaugment --single-branch https://github.com/nvidia/nvaitc-toolkit.git cuaugment

Getting Started

Please find details and installation instructions in README.md.