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.
git clone -b toolkit --single-branch https://github.com/nvidia/nvaitc-toolkit.git toolkit
Please find details and installation instructions in README.md.
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.
git clone -b cuaugment --single-branch https://github.com/nvidia/nvaitc-toolkit.git cuaugment
Please find details and installation instructions in README.md.
The Dockerfile available within this repository allows you build a new docker container from scratch pulling both branches and installing packages needed to execute the code.
Before executing the docker build command please download the NCCL package from the NVIDIA Developer zone and edit the Dockerfile accordingly.
git clone https://github.com/nvidia/nvaitc-toolkit.git
cd nvaitc-toolkit
docker build .